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Depth Anything V2 is a total beast for real-time 3D. Built a website that hits 30fps+ depth reconstructions from a single camera feed, extruding 12k voxels via WebGPU. Pure ML running local Vision Transformers with Three.js and zero backend lag. Lmk in the comments if you want a copy!...

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Gábor Pribék

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142,027 views • 2 months ago •via X (Twitter)

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Vibe-coded a "Ponku" sticker experience to mirror how Apple is blending iPhone hardware with Korea Emojis. 🌈📱 3D iPhone by Ranguel on Sketchfab. Built with AI in Omma by Spline. Lmk in the comments if you would like to grab a copy of the experience!

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Another action experiment with Higgsfield AI 🧩, this time with a little fisty cuffs! Using a single image made in Ideogram and sound FX with MMaudio via cocktail peanut. Action is the new frontier in AI video, lets see how hard we can push it at this early stage!

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How I built with Claude 👇 Prompt: "Design an agency site where services are presented as supermarket product packaging in 3D" Use Three.js with WebGL to render all products in real time in the browser Use Blender to model the packaging then export as GLTF to load in Three.js Use GSAP for the smooth product rotation and hover interactions Save this if you want a portfolio that makes clients feel they are buying something premium 🛒

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This week is already so hot. 🔥 Massive release from Decart : Lucy 2.0 a World Editing Model running at 1080p, 30FPS in realtime. This is truly exciting, the era of real-time generative reality is here. We are moving from watching AI video to living inside AI video. A breakthrough model capable of transforming the visual world in real-time. Moving beyond offline rendering, Lucy 2.0 delivers high-fidelity 1080p video generation with near-zero latency. Lucy 2.0 literally "redraws" the entire world pixel-by-pixel, while you are watching it. e.g. If you want to be an anime character, it doesn't just put a mask on you. It turns your skin into anime skin, your hair into anime hair, and the lighting in your room into anime lighting. Lucy 2.0 is also trained to stop the generated video from slowly falling apart over time, so the same stream can run much longer without faces and details drifting. So why is this a "Massive Deal"? Traditional AI video-generation model takes a prompt, you wait 10–20 minutes, and the computer "bakes" a video for you. You couldn't touch it or change it while it was happening. But Lucy 2.0 works like a mirror. It happens in real-time (30 frames per second). There is no waiting. You move your hand, the AI character moves its hand instantly. The craziest part isn't the visuals; it's the physics. Usually, AI hallucinations are glitchy—hands merge into faces, walls melt. Lucy 2.0 understands how the world works without being told. It knows that if you take off a helmet, there is hair underneath. It knows that if you splash water, droplets fly. It learned "physics" just by watching millions of videos. The physical behavior you see emerges from learned visual dynamics, not from engineered geometry or explicit physics engines. Their official technical report explicitly states that the model does not use traditional 3D engines, depth maps, or wireframes. It is a "pure diffusion model."

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12,761 views • 5 months ago

Robots can now reconstruct 3D scenes in real time from a single RGB camera. [📍 Projects page + paper] No depth sensor. No retraining. 30 FPS. Researchers at the Imperial College London introduced KV-Tracker, a training-free method that makes heavy models like π³ and Depth Anything 3 fast enough for real-time tracking. The idea is simple. These models use global self-attention, which is powerful but computationally expensive. KV-Tracker caches the key and value pairs from selected keyframes and reuses them for new frames. That cache becomes an implicit scene representation. Result: • Up to 30 FPS • 10 to 15x speedup • Accurate 6-DoF tracking on benchmarks like TUM RGB-D and 7-Scenes • Works with monocular RGB only It also supports object-level tracking with masks and allows saving the KV-cache for later reuse. For robotics, this reduces hardware constraints and moves real-time 3D perception closer to practical deployment. Credit to Marwan Taher (Marwan Taher) at Imperial’s Dyson Robotics Lab and many others who contributed to this! 📍 Save projects page + paper for later: Video: ——- if it matters in AI or Robotics you'll read it here first:
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Robots can now reconstruct 3D scenes in real time from a single RGB camera. [📍 Projects page + paper] No depth sensor. No retraining. 30 FPS. Researchers at the Imperial College London introduced KV-Tracker, a training-free method that makes heavy models like π³ and Depth Anything 3 fast enough for real-time tracking. The idea is simple. These models use global self-attention, which is powerful but computationally expensive. KV-Tracker caches the key and value pairs from selected keyframes and reuses them for new frames. That cache becomes an implicit scene representation. Result: • Up to 30 FPS • 10 to 15x speedup • Accurate 6-DoF tracking on benchmarks like TUM RGB-D and 7-Scenes • Works with monocular RGB only It also supports object-level tracking with masks and allows saving the KV-cache for later reuse. For robotics, this reduces hardware constraints and moves real-time 3D perception closer to practical deployment. Credit to Marwan Taher (Marwan Taher) at Imperial’s Dyson Robotics Lab and many others who contributed to this! 📍 Save projects page + paper for later: Video: ——- if it matters in AI or Robotics you'll read it here first:

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53,911 views • 3 months ago

Computer vision is so dope. You can map your space in real-time with the phone in your pocket — creating a simplified 3D model with ‘semantically meaningful’ entities representing the room, walls, windows and furniture within that space. Basically, a 3D floor plan. This is Apple’s aptly named Room Plan API inside Polycam. Cupertino has been laying the primitives for Vision Pro for years.
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Computer vision is so dope. You can map your space in real-time with the phone in your pocket — creating a simplified 3D model with ‘semantically meaningful’ entities representing the room, walls, windows and furniture within that space. Basically, a 3D floor plan. This is Apple’s aptly named Room Plan API inside Polycam. Cupertino has been laying the primitives for Vision Pro for years.

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136,380 views • 2 years ago