Huaijin Pi

🚀 Excited to share our NeurIPS 2025 paper: CoDA: Coordinated Diffusion Noise Optimization for Whole-Body Manipulation of Articulated Objects 🔗 Project page: phj128.github.io/page/CoDA/inde… 🔗 Code: github.com/phj128/CoDA 🔗 Paper: arxiv.org/abs/2505.21437

🚀 Excited to share our #CVPR2026 paper: EmbodMocap: In-the-Wild 4D Human-Scene Reconstruction for Embodied Agents. EmbodMocap, a portable yet affordable solution requiring only two moving iPhones—no calibrated multi-view camera studio, motion capture suits, or LiDAR sensors needed. With our fully automated optimization pipeline, you can effortlessly obtain high-precision scene meshes, human interaction motions, RGBD images, and camera parameters. The captured data is ready for training human-scene reconstruction models (like TRAM, pi3, etc.) and humanoid control policies (like deepmimic, AMP, etc.). What you need to do: 1. Borrow or buy two iPhone 12 Pros from eBay (600 USD in total). 2. Find 2 friends, then capture the sequences. 3. Deploy our repo, run our code, and get the results! The code and data will be released within 1 week. (Just come back to work from the Chinese Spring Festival, Happy Chinese New Year!) 📷 Project page: wenjiawang0312.github.io/projects/embod… 📷ArXiv: arxiv.org/abs/2602.23205 📷Code: github.com/WenjiaWang0312…

Work hard for this project!

Please check out paper #MOSPA "🎧Human Motion Generation Driven by Spatial Audio” at #NeurIPS2025 (🌟Spotlight)! 😊We have released our dataset and models : ) 💡The paper tackles the challenge of spatial-audio-driven human motion generation, enabling virtual humans to respond dynamically and realistically to diverse spatial sounds — not just “what” is sounding, but also “where” and “how” it sounds in space. 💡We introduce SAM, the first comprehensive Spatial Audio-Driven Human Motion dataset, with diverse spatial audio scenarios and high-quality 3D motion pairs, providing a solid benchmark for studying human motion conditioned on spatial audio. 💡Building on this, MOSPA is a diffusion-based generative framework that fuses semantic and spatial features of the audio to synthesize diverse, realistic motions aligned with spatial audio cues, achieving state-of-the-art performance on this new task and offering a strong baseline for future research. If you work on virtual humans, spatial audio, XR, or humanoid / embodied control, this can be a good motion skill learning source. Please come meet the team at our #NeurIPS2025 San Diego Spotlight poster! 📍 Exhibit Hall C,D,E — #4310 🕚 Fri, Dec 5 | 11 a.m.–2 p.m. PST Homepage: frank-zy-dou.github.io/projects/MOSPA… Paper: arxiv.org/abs/2507.11949 Code and Data: github.com/xsy27/Mospa-Ac… #NeurIPS #NeurIPS2025 #MOSPA #motion #Animation #SpatialAudio #VirtualHuman #Robotics #Robot #AI #Deeplearning #GenerativeAI #AIGC

@WenjiaWang_HKU Thanks! 😊

@HuaijinPi Great work!

🚀 Excited to share our NeurIPS 2025 paper: CoDA: Coordinated Diffusion Noise Optimization for Whole-Body Manipulation of Articulated Objects 🔗 Project page: phj128.github.io/page/CoDA/inde… 🔗 Code: github.com/phj128/CoDA 🔗 Paper: arxiv.org/abs/2505.21437

Come meet us at San Diego Poster! 🎉 📍 Exhibit Hall C,D,E — #5207 🕚 Wed, Dec 3 | 11 a.m.–2 p.m. PST Huge thanks to my amazing collaborators: Zhi Cen, @frankzydou, and, Taku Komura

CoDA is not limited to articulated objects — it also supports rigid object manipulation, producing stable, coordinated whole-body motions driven purely by text.

@realzhenxu Awesome work

Introducing the 4D Gaussian Transformer, a feed-forward dynamic Gaussian reconstructor trained only on monocular videos, achieving performance on par with optimization-based methods at a fraction of the inference time! Paper: arxiv.org/abs/2506.08015 Page: 4dgt.github.io

@realzhenxu Great work.

Tired of short multi-view video datasets? Check out our new SelfCap dataset with up to 10 minutes of 24-cameras high-quality dense view recording at 4K resolution! Data released for our Long Volumetric Video paper: zju3dv.github.io/longvolcap Dataset link: forms.gle/MzJqZjBfyZ53fR…