Alexandre Devaux

@bilawalsidhu Cool! Super curious to see your results 🙂

360 drone has arrived. Looking fwd to capturing some big ass gaussian splats.

@_cgman_ ooooo so good!

Vegetation interaction test render with the new #blender 5.2 Alpha Cloth Dynamics node in #geometrynodes and rendered with #b3d Cycles Animated hippo 3D model from Sketchfab

@SpenserFX You could train a feed-forward model with that :)

Synthetic splat training from a single image using the new Pixal3D + Trellis 2 backbone.. 9.4m polygons wrapped up into a 9mb splat asset.. over 200 image to splat assets made over the weekend, now just training a few data sets to see how they look

@neilsonks ooooo This is huge !

every object you create as a mesh is then removed from the input image to create the static version of the world

open-sourcing a 3D gen toolkit for Claude Code input image → environment, meshes, physics, lighting, & audio

@chrisfirst @johnAGI168 Aahahh, u keep getting good ideas of situations😀

With this prompt, you can sit courtside for Game 3 as the Knicks look to sweep the 76ers in Philly. Prompts below. Inspired by @johnAGI168

@baptistevercier @Prada Good vibe, well executed!

I worked on the summer campaign for @Prada. Cg environment made with #blender in just 2 days

@rubenhume Very cool! The great @zachlieberman had done it 10 years ago: zachlieberman.medium.com/land-lines-e1f…

Amazonia has a new logo, and it’s a masterpiece. The letters were extracted from satellite images of the river itself

@4DV_ai @SirWrender @CorridorDigital Congrats guys it was a nice demo!

Check out our collaboration with Corridor Crew on 4D Gaussian Splatting: youtu.be/X8yRlA7jqEQ?si… Huge thanks to @SirWrender and the entire @CorridorDigital team for making this happen. It’s easily one of the best explainer videos on Gaussian Splatting we’ve seen. Also check out our website at 4dv.ai. From jazz performances, to the first wedding ever captured in 4D, to a cute little cat, to a REAL rocket launch, we’re constantly pushing the boundaries of what 4D capture can be and bringing 4D creation to everyone. We believe 4D content is the new, and ultimately the most powerful form of visual media. Unlocking it means solving the entire pipeline end to end. That’s what we’re building at 4DV.ai: a full-stack toolkit for 4D content creation. Beyond the specialized 4D representations for data processing discussed in the video, we’re also building mobile capture systems with PTZ camera rigs, non-linear 4D editing tools, and playback and distribution workflows across the web, Unity, Blender, UE5, Houdini, and other platforms. More to come soon. Stay tuned.

@gradeeterna 🔥🔥🔥

Testing my DJI Osmo 360 rig for gaussian splatting. Mason’s Avenue, London. 14 million splats total. Trained with LichtFeld Studio and gsplat, edited in Houdini GSOPs, camera animation in Unity and rendered with Deckard Render. #gaussiansplatting #3DGS

@junyi42 🔥🔥🔥

𝗢𝗻𝗲 𝗺𝗲𝗺𝗼𝗿𝘆 𝗰𝗮𝗻’𝘁 𝗿𝘂𝗹𝗲 𝘁𝗵𝗲𝗺 𝗮𝗹𝗹. We present 𝗟𝗼𝗚𝗲𝗥, a new 𝗵𝘆𝗯𝗿𝗶𝗱 𝗺𝗲𝗺𝗼𝗿𝘆 architecture for long-context geometric reconstruction. LoGeR enables stable reconstruction over up to 𝟭𝟬𝗸 𝗳𝗿𝗮𝗺𝗲𝘀 / 𝗸𝗶𝗹𝗼𝗺𝗲𝘁𝗲𝗿 𝘀𝗰𝗮𝗹𝗲, with 𝗹𝗶𝗻𝗲𝗮𝗿-𝘁𝗶𝗺𝗲 𝘀𝗰𝗮𝗹𝗶𝗻𝗴 in sequence length, 𝗳𝘂𝗹𝗹𝘆 𝗳𝗲𝗲𝗱𝗳𝗼𝗿𝘄𝗮𝗿𝗱 inference, and 𝗻𝗼 𝗽𝗼𝘀𝘁-𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻. Yet it matches or surpasses strong optimization-based pipelines. (1/5) @GoogleDeepMind @Berkeley_AI

@Haian_Jin Brilliant work! Looking forward to trying the code 🔥

Spatial reconstruction is a long-context problem: real scenes come with hundreds of images. But O(N²) transformer-based models don’t scale efficiently. Introducing: 🤐ZipMap (CVPR ’26): Linear-Time, Stateful 3D Reconstruction via Test-Time Training (TTT). ZipMap “zips” a large image collection into an implicit TTT scene state in a single linear-time operation. The state will then be decoded into spatial outputs, and can be queried efficiently for novel-view geometry and appearance (~100 FPS) ZipMap is not only much faster (>20× faster than VGGT), but also matches or surpasses the accuracy of all SOTA models.

@mrdoob @deno_land A camera near a bit too far ? 😉 That's nice!

@deno_land Kind of want to keep this bug 🤓

Okay Claude, can you help me port Quake to Three.js? ... One hour later

@Jaenam97 Superb!

Shader-magic 🪄 Rendering flowers from a dodecahedron wireframe 🔮 #glsl

@onirenaud Exciting work! 🔥 Following closely, When will be able to test it?

Got Gaussian Splatting to 120fps for ~200k splats with: - Morton order for tight chunk bounds - Hierarchical chunk culling with indirect dispatch - 16bit Radix sort - Render Bundle to cut CPU overhead - Packed buffers Now trying 120fps with ~500k, bottleneck still overdraw.

@YuanLiu41955461 Nice!! Thanks for sharing the code 😀

Excited to share our recent work, UniSH, which unifies dynamic 3D scene reconstruction and SMPL estimation within a single framework. (Left-top is input video). Code has been released! github.com/murphylmf/UniSH Project page: murphylmf.github.io/UniSH/ Paper: arxiv.org/abs/2601.01222

@skalskip92 Congrats! Segmentation is pretty hot to me, just tested on your hugging space. Can I use it for images used later in broadcast without complexe rights/licence? 😀

RF-DETR paper is finally on arXiv - real time detection with DINOv2 backbone - runs neural architecture search (NAS) over about 6000 architecture variants - uses weight sharing across all configs - first real-time segmentation DETR to break past top YOLO results ↓ more

@romvsuals Some surprising relaxing good feeling about this flat. Well done!🔥

Wip 💻

@romvsuals Some surprising relaxing good feeling about this flat. Well done!🔥

@YuanLiu41955461 🔥🔥🔥 Looks impressive! Any Hugging face demo coming ? 😀

Share our recent work - TrackingWorld: World-centric Monocular 3D Tracking of Almost All Pixels [NeurIPS'25] An optimization-based 3D tracking of all pixels. Code: github.com/IGL-HKUST/Trac… Webpage: igl-hkust.github.io/TrackingWorld.… Welcome to try it! A feedforward version is on the way!

@skandakoppula So cool! 🔥🔥🔥

Thrilled to release 🎯 D4RT (Dynamic 4D Reconstruction and Tracking)! 🌟 State-of-the-art results on 4D reconstruction & tracking benchmarks 🚀 Up to 300x faster tracking and 100x faster pose estimation than prior works 📍 A simple, unified interface for tracking, depth, and pose using point-wise decoding 🔗 Learn more about D4RT: d4rt-paper.github.io