newvoicemod
May 11, 2026 18:25
· 17:33
· English
· Whisper Turbo
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And again,
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it works really well.
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Conclusion.
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So yeah,
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this platform is actually very powerful.
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From text or image,
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to a full 3D model,
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to a rigged and animated character,
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all in just a few minutes.
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That's a huge shift in how 3D content can be created.
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It saves time,
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simplifies the workflow,
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and makes this kind of work much more accessible.
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So yeah,
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this is definitely a game changer.
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You can find the link to the tool in the description below if you want to try it yourself.
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Thanks for watching,
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and I'll see you in the next video.
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Welcome to the video.
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Today,
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I'm going to show you a tool that allows you to generate high -quality 3D
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models from either text or images and use them directly in
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your projects.
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Now,
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before we jump into testing it,
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let me quickly explain what this tool actually does.
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This tool is called Triple3D,
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and it's an AI 3D model generator.
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It allows you to create 3D models in two main ways,
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text to 3D image to 3D.
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So you can either describe what you want or upload
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an image, and it will turn that into a full 3D model.
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And these aren't just basic shapes.
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The outputs are actually structured and usable.
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One of the key features is intelligent segmentation.
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This means the model is split into different editable parts instead of
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being one single block,
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so you can actually select,
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modify,
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and refine specific areas of the model,
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which gives you much more control.
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Then there's AI texturing.
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With just one click,
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you can apply high -resolution 4KPBR ready textures
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to your model.
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And there's also a magic brush,
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which lets you repaint or adjust specific areas with precision without
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affecting the whole model.
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Another powerful feature is automatic rigging and animation.
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So instead of just getting a static model,
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the tool can generate a skeleton,
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apply skin weights,
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and make the character ready for animation.
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This usually takes a lot of manual work,
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but here it's done automatically.
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And on top of that,
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there's something really important.
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The DCC bridge.
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This means the tool connects directly with professional software like Blender
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Maya, 3DS,
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Max Unity,
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Unreal Engine,
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Godot,
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and Cocos.
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So you can easily move your models into your existing workflow without
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extra steps.
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Overall,
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this tool turns hours of manual 3D work into something
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you can do in minutes and integrates directly into real
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production pipelines.
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Now let's test it.
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Demo 1.
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Image to 3D.
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Let's start by uploading an image of Ezreal from League of Legends
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and see how it turns him into a 3D model.
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While it's generating,
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you can already see a preview forming,
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kind of like a 3D outline of the character.
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This is actually really cool because you can start visualizing
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the result before it's finished,
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and now the final model is ready.
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And honestly,
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this looks really good.
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The proportions,
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the structure.
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It's very impressive for something generated automatically.
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Demo 2.
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Text to 3D object.
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Now let's try something different.
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Instead of an image,
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we'll use a prompt.
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Let's generate an ornate carved wooden throne chair.
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Same process.
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It builds the base shape first,
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then refines the details.
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And now we have the result.
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And this is actually very well done.
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The details are clean,
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and it looks like something you could actually use as a real asset.
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Demo 3.
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Organic model.
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Now let's test something more complex.
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A flying hummingbird with iridescent blue,
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purple, and green plumage.
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This is a good test because it's more organic and detailed.
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And here are the results.
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And yeah,
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this is impressive.
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The shape,
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the flow.
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It works really well.
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At this point,
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I can say this tool genuinely delivers.
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Rigging and animation.
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Humanoid.
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Now let's move to rigging.
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So for rigging,
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you go into this section,
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and you can choose presets depending on your character.
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There's one for humanoids,
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and another one for animals.
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So I used the humanoid preset,
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and the rigging was successful.
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What's interesting is that our character wasn't even in a proper T -pose,
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which is usually recommended.
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But it still worked.
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Now let's preview an animation.
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Let's try running.
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And as you can see,
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it's running really well.
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Which is kind of crazy,
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because just a few minutes ago,
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this was just an image.
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And now,
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it's a fully animated 3D character.
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That's honestly amazing.
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Rigging.
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Animal test.
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Now let's try the bird.
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There's a preset for animals,
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so let's use that.
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And the rigging worked?
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As you can see,
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the structure is correct,
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but there's one limitation here.
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There aren't any preset animations available for birds,
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so we can't preview movement directly.
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It's a bit of a downside,
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but the rig itself is usable if you export it.
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Final test,
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T -Pose character.
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Now for a final test,
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let's do things properly.
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We're going to generate a character in a T -Pose to
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see how much better the results can be.
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We'll go with something simple,
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just a warrior.
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So I'm generating it directly from text.
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Now we wait.
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And as it's generating,
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we can already see the preview forming in a proper T -pose.
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We just need to wait a bit more for the full model.
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And yeah,
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it takes a few minutes,
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but the results are worth it.
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All right,
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now it's ready.
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And here's our warrior.
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As you can see,
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it looks really good.
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Clean structure,
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good proportions,
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solid base model.
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Now let's rig it.
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Alright,
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rigging is done.
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Let's test some animations.
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Let's try the afraid animation.
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Okay,
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this looks good.
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Now let's try boxing.
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Yeah,
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this is interesting.
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The pose estimation capability is the thing that really makes you stop
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and think.
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The project is partly inspired by research out of Carnegie
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Mellon University on something called dense pose from Wi -Fi.
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The idea that you can reconstruct human body pose from
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radio signals alone.
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RuVue implements this using an architecture called YFlow,
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which takes the processed CSI signals and maps them
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to 17 key points on the human skeleton,
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the same set of points used in standard computer vision pose estimation benchmarks.
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Shoulders,
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elbows, wrists,
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hips,
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knees, ankles,
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head.
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When trained with camera ground truth,
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the system reaches 92 .9 % accuracy on the PC
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-K at 20 benchmark,
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which is a standard measure of pose estimation quality.
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Without camera supervision,
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training purely from Wi -Fi signals with no labeled data,
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it still learns a meaningful body model in about 84 seconds on
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a laptop.
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The self -supervised training approach is one of the more
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clever parts of the system.
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because it means you don't need to go collect a specialized data set or run a
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camera alongside the sensor forever.
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You just let it watch people move around,
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and it figures things out on its own.
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One thing worth highlighting is the privacy architecture,
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because I think it's actually a core part of what makes this interesting rather
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than alarming.
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RueView produces no images.
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There's no video,
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no photograph,
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no visual record of any kind.
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What it produces is structured data.
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A position,
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a breathing rate,
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a skeleton of points in space.
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That data lives on your local hardware.
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Nothing is sent to a cloud server.
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There's no account to create,
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no terms of service,
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no company receiving your information.
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The entire system runs on devices you own,
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in your home,
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under your control.
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This matters for a lot of reasons beyond privacy.
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It means the system works without an internet connection.
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It means it works if the company that made it disappears tomorrow.
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And it means you can actually read the code and understand
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exactly what it's doing,
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because it's all right there in the repository.
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The use cases this opens up are genuinely compelling.
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Eldercare is the obvious one.
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Being able to know that someone is okay,
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that they're breathing,
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that they haven't fallen,
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without putting a camera in their bedroom or asking them to wear anything.
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Sleep monitoring without a wearable.
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Home security that doesn't rely on line -of -sight cameras.
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Smart home automation that actually knows where you are and
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what you're doing rather than guessing from motion sensors with a five -second
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delay.
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There are also more exotic applications documented in the project.
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Disaster response,
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searching for survivors through rubble,
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monitoring industrial spaces for safety compliance,
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even contactless blood pressure estimation using higher
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frequency radar hardware.
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The repository has examples and documentation
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covering a lot of this,
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and the architecture is designed to be extensible so you can build new
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sensing capabilities on top of the existing pipeline.
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If you want to try it yourself,
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the fastest path is the Docker image,
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one command,
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and you have a running system.
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If you want to go further,
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the repository has a guided installer that walks you through hardware
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detection, profile selection,
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and getting everything set up.
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The full documentation covers the API,
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the WebSocket interface for real -time streaming,
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how to flash and provision the ESP32 firmware,
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Speaker 1 (newvoicemod)
and how to train your own models.
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The GitHub repository is linked in the description.
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Speaker 1 (newvoicemod)
It's called Ruvue,
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Speaker 1 (newvoicemod)
it's by Ruvnet,
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Speaker 1 (newvoicemod)
and it is absolutely worth an hour of your time to explore.
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Speaker 1 (newvoicemod)
Whether you're a developer who wants to build something on top of it,
10:09
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Speaker 1 (newvoicemod)
or just someone who finds it fascinating that this is possible at all.
10:13
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Speaker 1 (newvoicemod)
Because honestly,
10:15
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Speaker 1 (newvoicemod)
the fact that a $9 chip and some open source software can
10:19
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Speaker 1 (newvoicemod)
do this with signals that are already in your home.
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Speaker 1 (newvoicemod)
That's the kind of thing that changes how you think about what's possible.
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Speaker 1 (newvoicemod)
Your Wi -Fi router is doing something right now that you have no idea about.
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Speaker 1 (newvoicemod)
It's flooding every room in your home with radio waves.
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Speaker 1 (newvoicemod)
Those waves pass through your walls,
10:35
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Speaker 1 (newvoicemod)
your furniture,
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Speaker 1 (newvoicemod)
your body,
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Speaker 1 (newvoicemod)
and they bounce back every single second.
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Speaker 1 (newvoicemod)
And for decades,
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Speaker 1 (newvoicemod)
all of that information has just been thrown away.
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Speaker 1 (newvoicemod)
Until now.
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Speaker 1 (newvoicemod)
There's an open source project on GitHub called RueView,
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Speaker 1 (newvoicemod)
and what it does is genuinely one of the most interesting things I've
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Speaker 1 (newvoicemod)
come across in a long time.
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Speaker 1 (newvoicemod)
It takes those radio waves that your router is already producing and
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Speaker 1 (newvoicemod)
turns them into a sensing system.
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Speaker 1 (newvoicemod)
It can detect people through walls.
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Speaker 1 (newvoicemod)
It can measure your breathing rate and your heart rate without touching you.
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Speaker 1 (newvoicemod)
It can track movement,
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Speaker 1 (newvoicemod)
recognize when someone falls,
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Speaker 1 (newvoicemod)
and even estimate body pose,
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Speaker 1 (newvoicemod)
where your arms are,
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Speaker 1 (newvoicemod)
where your legs are.
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Speaker 1 (newvoicemod)
All from Wi -Fi signals alone.
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Speaker 1 (newvoicemod)
No cameras,
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Speaker 1 (newvoicemod)
no wearables,
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Speaker 1 (newvoicemod)
nothing you need to put on or install in a way you'd ever notice.
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Speaker 1 (newvoicemod)
Let me show you how it works.
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Speaker 1 (newvoicemod)
So the first thing you're going to notice when you land on the repository is that
11:32
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Speaker 1 (newvoicemod)
this project is enormous.
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Speaker 1 (newvoicemod)
This isn't a weekend experiment someone threw together.
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Speaker 1 (newvoicemod)
There are over 400 pull requests,
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Speaker 1 (newvoicemod)
79 architectural decision records documenting every
11:44
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Speaker 1 (newvoicemod)
major technical choice,
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Speaker 1 (newvoicemod)
a full Rust rewrite of the signal processing pipeline,
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Speaker 1 (newvoicemod)
ESP32 firmware,
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Speaker 1 (newvoicemod)
Docker images,
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Speaker 1 (newvoicemod)
a desktop app in progress,
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Speaker 1 (newvoicemod)
and over 1 ,000 tests.
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Speaker 1 (newvoicemod)
This is a seriously well -engineered piece of software,
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Speaker 1 (newvoicemod)
and it's all open source under the MIT license,
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Speaker 1 (newvoicemod)
meaning you can use it,
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Speaker 1 (newvoicemod)
modify it,
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Speaker 1 (newvoicemod)
build on it,
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Speaker 1 (newvoicemod)
do whatever you want with it.
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Speaker 1 (newvoicemod)
The project is built around a concept called Channel State
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Speaker 1 (newvoicemod)
Information,
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Speaker 1 (newvoicemod)
or CSI.
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Speaker 1 (newvoicemod)
Your router and your devices are constantly exchanging Wi -Fi signals,
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Speaker 1 (newvoicemod)
and buried inside those signals is extremely detailed information
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Speaker 1 (newvoicemod)
about how the radio environment of a room is behaving,
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Speaker 1 (newvoicemod)
which frequencies are strong,
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Speaker 1 (newvoicemod)
which are weak,
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Speaker 1 (newvoicemod)
how the signal is bouncing around.
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Speaker 1 (newvoicemod)
Most devices just use this to improve connection quality
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Speaker 1 (newvoicemod)
and throw the rest away.
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Speaker 1 (newvoicemod)
RooView captures it and reads it like a map of everything happening
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Speaker 1 (newvoicemod)
in the space.
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Speaker 1 (newvoicemod)
Now, the hardware side of this is where it gets interesting.
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Speaker 1 (newvoicemod)
To actually capture CSI data,
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Speaker 1 (newvoicemod)
you need a device that exposes it,
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Speaker 1 (newvoicemod)
and the one RuView is built around is the ESP32
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Speaker 1 (newvoicemod)
-S3,
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Speaker 1 (newvoicemod)
a tiny microcontroller that you can get for about $9.
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Speaker 1 (newvoicemod)
The project has custom firmware for it that streams CSI
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Speaker 1 (newvoicemod)
data over UDP at around 20 to 28 frames per second.
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Speaker 1 (newvoicemod)
You flash the firmware onto the chip,
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Speaker 1 (newvoicemod)
connect it to your Wi -Fi network,
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Speaker 1 (newvoicemod)
point it at your sensing server,
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Speaker 1 (newvoicemod)
and you're collecting data.
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Speaker 1 (newvoicemod)
If you don't have any hardware and just want to try the software,
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Speaker 1 (newvoicemod)
there's a Docker image that runs the whole thing with simulated data.
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Speaker 1 (newvoicemod)
You pull it,
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Speaker 1 (newvoicemod)
run it,
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and open localhost colon 3000,
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Speaker 1 (newvoicemod)
and you've got a live demo running in under a minute.
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Speaker 1 (newvoicemod)
That's actually a great way to explore what the system is doing before
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Speaker 1 (newvoicemod)
you commit to any hardware.
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Speaker 1 (newvoicemod)
Once the data is flowing,
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the signal processing pipeline takes over,
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Speaker 1 (newvoicemod)
and this is where the real engineering lives.
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Speaker 1 (newvoicemod)
Raw CSI data is messy.
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Speaker 1 (newvoicemod)
It's full of noise,
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Speaker 1 (newvoicemod)
phase errors,
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Speaker 1 (newvoicemod)
reflections from objects that aren't people.
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Speaker 1 (newvoicemod)
RueView runs it through a series of research -grade algorithms,
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Speaker 1 (newvoicemod)
things like spot -five phase correction,
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Speaker 1 (newvoicemod)
Hample outlier filtering,
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Speaker 1 (newvoicemod)
and Fresnel zone modeling,
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Speaker 1 (newvoicemod)
to clean the signal and isolate what's actually caused by human
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Speaker 1 (newvoicemod)
presence in movement.
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Speaker 1 (newvoicemod)
After cleaning,
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Speaker 1 (newvoicemod)
the system separates out different frequency bands.
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Speaker 1 (newvoicemod)
Breathing happens at 0 .1 to 0 .5 hertz.
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Speaker 1 (newvoicemod)
Heart rate sits between 0 .8 and 2 .0
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Speaker 1 (newvoicemod)
hertz.
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Speaker 1 (newvoicemod)
Movement produces much higher frequency changes.
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Speaker 1 (newvoicemod)
The pipeline isolates each of these independently and extracts
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Speaker 1 (newvoicemod)
them as separate signals,
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Speaker 1 (newvoicemod)
which is how you get breathing rate and heart rate as clean numerical readings
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Speaker 1 (newvoicemod)
rather than just noise.
14:35
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Speaker 1 (newvoicemod)
The presence detection piece is probably the most immediately practical
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Speaker 1 (newvoicemod)
capability.
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Speaker 1 (newvoicemod)
The system learns what your room looks like when it's empty,
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Speaker 1 (newvoicemod)
the static reflections from your walls and furniture,
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Speaker 1 (newvoicemod)
and then continuously subtracts that baseline from every incoming
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Speaker 1 (newvoicemod)
frame.
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Speaker 1 (newvoicemod)
What's left is only the signal caused by something that changed.
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Speaker 1 (newvoicemod)
A person walking in.
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Speaker 1 (newvoicemod)
Someone breathing in the corner.
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Speaker 1 (newvoicemod)
A fall.
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Speaker 1 (newvoicemod)
This works through walls because Wi -Fi passes through most building materials
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Speaker 1 (newvoicemod)
without significant attenuation.
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Speaker 1 (newvoicemod)
You can put the sensor in one room and detect presence in the
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Speaker 1 (newvoicemod)
next room over.
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Speaker 1 (newvoicemod)
And the system doesn't just detect whether someone is there.
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Speaker 1 (newvoicemod)
It tracks them.
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Speaker 1 (newvoicemod)
With multiple nodes placed around a space,
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Speaker 1 (newvoicemod)
it can tell you where each person is,
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Speaker 1 (newvoicemod)
how many people are present,
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Speaker 1 (newvoicemod)
and follow them as they move.
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Speaker 1 (newvoicemod)
The multi -node fusion works by combining the signal from every sensor
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Speaker 1 (newvoicemod)
simultaneously,
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Speaker 1 (newvoicemod)
and the more overlap you have between their fields of view,
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Speaker 1 (newvoicemod)
the more precise the tracking gets.
15:37
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Speaker 1 (newvoicemod)
Imagine turning just a text prompt into a full 3D character
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Speaker 1 (newvoicemod)
in minutes.
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Speaker 1 (newvoicemod)
This AI tool does exactly that.
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Speaker 1 (newvoicemod)
Just type something like alien,
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Speaker 1 (newvoicemod)
and it instantly builds a full 3D model with structure,
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Speaker 1 (newvoicemod)
depth,
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Speaker 1 (newvoicemod)
and detail.
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Speaker 1 (newvoicemod)
No modeling,
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Speaker 1 (newvoicemod)
no sculpting,
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Speaker 1 (newvoicemod)
just text to production -ready 3D assets.
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Speaker 1 (newvoicemod)
AI is changing 3D forever.
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Speaker 1 (newvoicemod)
This AI can turn a simple image into a fully animated 3D character.
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Speaker 1 (newvoicemod)
First,
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Speaker 1 (newvoicemod)
you upload an image,
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Speaker 1 (newvoicemod)
and it rebuilds it as a 3D model,
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Speaker 1 (newvoicemod)
automatically.
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Speaker 1 (newvoicemod)
But it doesn't stop there.
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Speaker 1 (newvoicemod)
It can also rig and animate it.
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Speaker 1 (newvoicemod)
So one image becomes a playable 3D character in
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Speaker 1 (newvoicemod)
minutes.
16:21
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Speaker 1 (newvoicemod)
Now let's try something else.
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Speaker 1 (newvoicemod)
Let's go with something very simple.
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Speaker 1 (newvoicemod)
Just one word.
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Speaker 1 (newvoicemod)
Alien.
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Speaker 1 (newvoicemod)
So it's generating.
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Speaker 1 (newvoicemod)
And already,
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Speaker 1 (newvoicemod)
from the preview,
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Speaker 1 (newvoicemod)
we can see it's forming something that clearly looks like an alien.
16:34
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Speaker 1 (newvoicemod)
Which is interesting,
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Speaker 1 (newvoicemod)
because this is just a single word prompt.
16:38
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Speaker 1 (newvoicemod)
No extra detail.
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Speaker 1 (newvoicemod)
Alright,
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Speaker 1 (newvoicemod)
now the result is ready.
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Speaker 1 (newvoicemod)
And here it is.
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Speaker 1 (newvoicemod)
A blue alien.
16:46
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Speaker 1 (newvoicemod)
This is actually pretty cool.
16:48
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Speaker 1 (newvoicemod)
I like this.
16:50
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Speaker 1 (newvoicemod)
Now let's take it a step further and rig this model.
16:54
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Speaker 1 (newvoicemod)
Alright,
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Speaker 1 (newvoicemod)
the rigging is done.
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Speaker 1 (newvoicemod)
Let's try some animations.
16:59
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Speaker 1 (newvoicemod)
Let's start with something like an angry animation.
17:01
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Speaker 1 (newvoicemod)
And yeah,
17:03
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Speaker 1 (newvoicemod)
this actually looks good.
17:04
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Speaker 1 (newvoicemod)
Now let's try something more fun.
17:06
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Speaker 1 (newvoicemod)
Maybe running,
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Speaker 1 (newvoicemod)
or even dancing.
17:08
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Speaker 1 (newvoicemod)
Let's try dancing.
17:10
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Speaker 1 (newvoicemod)
Yeah,
17:11
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Speaker 1 (newvoicemod)
this is actually funny.
17:13
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Speaker 1 (newvoicemod)
I like this.
17:14
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Speaker 1 (newvoicemod)
It's kind of crazy when you think about it.
17:16
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Speaker 1 (newvoicemod)
Just one word,
17:17
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Speaker 1 (newvoicemod)
and now we have a fully rigged and animated 3D character.
17:20
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Speaker 1 (newvoicemod)
That's actually very interesting.
17:22
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Speaker 1 (newvoicemod)
This means the tool connects directly with professional software,
17:25
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Speaker 1 (newvoicemod)
like Blender,
17:27
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Speaker 1 (newvoicemod)
Maya,
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Speaker 1 (newvoicemod)
3DS Max,
17:29
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Speaker 1 (newvoicemod)
Unity,
17:29
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Speaker 1 (newvoicemod)
Unreal Engine,
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Speaker 1 (newvoicemod)
Godot,
17:31
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Speaker 1 (newvoicemod)
and Cocos.
תעתיק זה נוצר על ידי AI (הכרה אוטומטית של דיבור). ייתכן שיש בו שגיאות אשר מאשרות את השמע המקורי לשימוש קריטי. מדיניות AI
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