3 citations · 3 across the 8 of their papers we have counts for
5 papers · 1 filter
Image Diffusion Models Are Good 3D Human Motion Generators
Siyuan Yang, Chong Wang, Hao Cheng +4
Text-to-motion generation typically relies on dataset-specific skeletal representations, with models trained separately for benchmarks with different joint layouts. We investigate…
TAME:Topology-Aware Text-Driven Motion Editing across Heterogeneous Humanoid Skeletons
Qichen Zheng, Siyuan Yang, Chong Wang +4
Text-driven motion editing modifies an existing motion sequence according to a text instruction while preserving the content of the source motion. Existing methods are typically bu…
Persistence Forcing: Exploiting Feature Specialization in Pixel-Space Diffusion
Chong Wang, Zixuan Fu, Shiqi Huang +3
Pixel-space diffusion Transformers (DiTs) directly operate on high-dimensional visual data, yet their hidden representations typically undergo uniform refinement across depth. Natu…
A Frozen Pixel-Space Diffusion Model Can Guide Itself with Its Own Samples
Zixuan Fu, Chong Wang, Lanqing Guo +3
Pixel-space diffusion models aim to learn an end-to-end generator directly over raw pixels. This is challenging because a single model must capture both global structure and local…
Improving Flexible Image Tokenizers for Autoregressive Image Generation
Zixuan Fu, Lanqing Guo, Chong Wang +3
Flexible image tokenizers aim to represent an image using an ordered 1D variable-length token sequence. This flexible tokenization is typically achieved through nested dropout, whe…