Human Motion Video Generation: A Survey
Haiwei Xue, Xiangyang Luo, Zhanghao Hu, Xin Zhang, Xunzhi Xiang, Yuqin Dai, Jianzhuang Liu, Minglei Li, Jian Yang, Fei Ma, Changpeng Yang, Zonghong Dai, Fei Richard Yu
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- This survey provides a comprehensive review of human motion video generation methods, covering the latest techniques, applications, and future directions.

CanonSwap: High-Fidelity and Consistent Video Face Swapping via Canonical Space Modulation
Xiangyang Luo , Ye Zhu†, Yunfei Liu, Lijian Lin, Cong Wan, Zijian Cai, Shao-Lun Huang†, Yu Li
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- CanonSwap decouples motion information from appearance to enable high-fidelity and consistent video face swapping.

Object Isolated Attention for Consistent Story Visualization
Xiangyang Luo, Junhao Cheng, Yifan Xie, Xin Zhang, Tao Feng, Zhou Liu, Fei Ma†, Fei Yu
Paper

- We proposes a training-free method that uses isolated attention mechanisms to maintain character consistency and prevent feature confusion in story visualization.

Grid: Omni Visual Generation
Cong Wan*, Xiangyang Luo*, Hao Luo, Zijian Cai, Yiren Song, Yunlong Zhao, Yifan Bai, Fan Wang, Yuhang He, Yihong Gong
Paper

- We introduces GRID, an omni-visual generation framework that reformulates temporal tasks like video into grid layouts, enabling a single powerful image model to efficiently handle image, video, and 3D generation.

CodeSwap: Symmetrically Face Swapping Based on Prior Codebook
Xiangyang Luo, Xin Zhang, Yifan Xie, Xinyi Tong, Weijiang Yu, Heng Chang, Fei Ma†, Fei Ricahrd Yu
Paper

- CodeSwap achieves high-fidelity face swapping by symmetrically manipulating codes within a pre-trained, high-quality facial codebook.