3 citations · 5 across the 13 of their papers we have counts for
5 papers · 1 filter
InterMoE: Individual-Specific 3D Human Interaction Generation via Dynamic Temporal-Selective MoE
Lipeng Wang, Hongxing Fan, Haohua Chen +2
Generating high-quality human interactions holds significant value for applications like virtual reality and robotics. However, existing methods often fail to preserve unique indiv…
Multi-Agent Amodal Completion: Direct Synthesis with Fine-Grained Semantic Guidance
Hongxing Fan, Lipeng Wang, Haohua Chen +3
Amodal completion, generating invisible parts of occluded objects, is vital for applications like image editing and AR. Prior methods face challenges with data needs, generalizatio…
VoxHammer: Training-Free Precise and Coherent 3D Editing in Native 3D Space
Lin Li, Zehuan Huang, Haoran Feng +4
3D local editing of specified regions is crucial for game industry and robot interaction. Recent methods typically edit rendered multi-view images and then reconstruct 3D models, b…
AnimaX: Animating the Inanimate in 3D with Joint Video-Pose Diffusion Models
Zehuan Huang, Haoran Feng, Yangtian Sun +3
We present AnimaX, a feed-forward 3D animation framework that bridges the motion priors of video diffusion models with the controllable structure of skeleton-based animation. Tradi…
Personalize Anything for Free with Diffusion Transformer
Haoran Feng, Zehuan Huang, Lin Li +2
Personalized image generation aims to produce images of user-specified concepts while enabling flexible editing. Recent training-free approaches, while exhibit higher computational…