8 papers
FVAttn: Adaptive Sparse Attention with Runtime Load Balancing for Video Generation
Hao Liu, Chenghuan Huang, Ye Huang +6
Video Diffusion Transformers process long spatio-temporal sequences, making self-attention the main bottleneck in high-resolution video generation. Training-free sparse attention r…
EMOSH: Expressive Motion and Shape Disentanglement for Human Animation
Dongbin Zhang, Hao Liu, Binquan Dai +5
High-fidelity and expressive controllable human animation is essential for content creation and digital avatar applications. However, existing methods face a dilemma between expres…
Chorus II: Cross-Request Sparsity Reuse for Efficient Image-to-Video Generation
Hao Liu, Chenghuan Huang, Xing Cai +5
Serving diffusion models for image-to-video generation is computationally expensive, posing significant challenges for large-scale deployment. Real I2V workloads often contain simi…
AnyMo: Scaling Any-Modality Conditional Motion Generation with Masked Modeling
Yiheng Li, Zhuo Li, Ruibing Hou +4
Conditional human motion generation remains a fundamental challenge in computer vision and robotics. Despite significant progress, current methods are often constrained by fixed mo…
Identity as Presence: Towards Appearance and Voice Personalized Joint Audio-Video Generation
Qin Chen, Yingjie Chen, Shilun Lin +9
Recent advances in video synthesis have enabled realistic integration of real individuals, driving demand for identity-aware generation. While emerging methods support joint appear…
TimeMachine: Fine-Grained Facial Age Editing with Identity Preservation
Yilin Mi, Qixin Yan, Zheng-Peng Duan +5
With the advancement of generative models, facial image editing has made significant progress. However, achieving fine-grained age editing while preserving personal identity remain…
Stand-In: A Lightweight and Plug-and-Play Identity Control for Video Generation
Bowen Xue, Zheng-Peng Duan, Qixin Yan +6
Generating high-fidelity human videos that match user-specified identities is important yet challenging in the field of generative AI. Existing methods often rely on an excessive n…
Morph: A Motion-free Physics Optimization Framework for Human Motion Generation
Zhuo Li, Mingshuang Luo, Ruibing Hou +5
Human motion generation has been widely studied due to its crucial role in areas such as digital humans and humanoid robot control. However, many current motion generation approach…