Cheng Lin
187 posts
@_cheng_lin
Associate Professor @MUST | Prev Research Scientist @Tencent&MiHoYo | Organizer @AnySyn3D 3D Vision, 3D AIGC, Geometric Modeling, Graphics
SAD: Soft Anisotropic Diagrams for Differentiable Image Representation has been accepted by #SIGGRAPH2026 Check it out, and huge congrats to Lucky! @Luckyballa #SAD represents an image as a soft, anisotropic, differentiable diagram over learnable sites. Each pixel is modeled as a softmax blend over its top-K nearby sites under a site-dependent distance, yielding a differentiable partition of unity with explicit ownership and content-aligned boundaries. A GPU-friendly top-K propagation scheme keeps the cost constant per pixel, enabling fast fitting at matched or better quality. Classical geometric structures can still inspire fresh perspectives in modern visual computing. Voronoi and Power diagrams have long been elegant tools for 3D shape analysis, reconstruction, and geometric reasoning; here, related diagram ideas, with connections to Apollonius-style diagrams, are explored for image representations. Homepage: luckyiyi.github.io/SAD/ arXiv: arxiv.org/pdf/2604.21984 #SIGGRAPH2026 #SIGGRAPH #CV #Vision #Graphics #CG
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
AHA! Animating Human Avatars in Diverse Scenes with Gaussian Splatting Contributions: • We introduce the 3D Gaussian Splatting representation to the classical computer graphics problem of animating humans in 3D environments. • We demonstrate that our framework can be used for geometry-consistent free viewpoint rendering of monocular videos edited with new animated humans. • We introduce a novel Gaussian-aligned motion module for motion synthesis in scenes represented as 3D Gaussians. • We introduce a human scene Gaussian refinement optimization for the correct placement of human Gaussians in scenes represented using 3DGS, leading to better contact and interactions.
🚀 We’ll be hosting a Tutorial on "3D Human Motion Generation and Simulation" at ICCV 2026 in Honolulu, Hawaii! 🌺 📅 Date: October 19, 2026 ⏰ Time: 9:00–16:00 (HST) 🔗 More details & resources: 3dmogen.github.io #AIGC #Simulation #robotics #ComputerVision #ICCV2025
Excited to share our latest work on 🎧spatial audio-driven human motion generation. We aim to tackle a largely underexplored yet important problem of enabling virtual humans to move naturally in response to spatial audio—capturing not just what is heard, but also where the sound is coming from. To this end, we introduce the Spatial Audio-Driven Human Motion (SAM) dataset—the first comprehensive dataset featuring paired high-quality human motion and spatial audio recordings. For benchmarking, we develop a generative framework for human MOtion generation driven by SPAtial audio, termed MOSPA, which learns to synthesize realistic and diverse human motions conditioned on spatial audio input. We hope this research could provide a foundation for future research in spatial perception, virtual characters, and embodied AI. The dataset and model will be open-sourced soon. A big thank you to our intern, Shuyang Xu, for the wonderful collaboration! Congratulations, Shuyang! Project page: frank-zy-dou.github.io/projects/MOSPA… Paper: arxiv.org/abs/2507.11949 Video: youtu.be/p_xwTDA-K0g #Animation #CG #CV #AIGC #DL #Deeplearning #Motion #Graphics #AI #GenerativeAI
Excited to share our latest work on 🎧spatial audio-driven human motion generation. We aim to tackle a largely underexplored yet important problem of enabling virtual humans to move naturally in response to spatial audio—capturing not just what is heard, but also where the sound is coming from. To this end, we introduce the Spatial Audio-Driven Human Motion (SAM) dataset—the first comprehensive dataset featuring paired high-quality human motion and spatial audio recordings. For benchmarking, we develop a generative framework for human MOtion generation driven by SPAtial audio, termed MOSPA, which learns to synthesize realistic and diverse human motions conditioned on spatial audio input. We hope this research could provide a foundation for future research in spatial perception, virtual characters, and embodied AI. The dataset and model will be open-sourced soon. A big thank you to our intern, Shuyang Xu, for the wonderful collaboration! Congratulations, Shuyang! Project page: frank-zy-dou.github.io/projects/MOSPA… Paper: arxiv.org/abs/2507.11949 Video: youtu.be/p_xwTDA-K0g #Animation #CG #CV #AIGC #DL #Deeplearning #Motion #Graphics #AI #GenerativeAI
