Video Diffusion Model
Video diffusion models generate videos by iteratively removing noise from random data, guided by text prompts or other conditioning information. Current research focuses on improving temporal consistency, enhancing video quality (especially at high resolutions), and developing efficient algorithms for long video generation and various control mechanisms (e.g., camera control, object manipulation). These advancements are significant for applications in film production, animation, and 3D modeling, offering powerful tools for creating realistic and controllable video content.
Papers
Human-VDM: Learning Single-Image 3D Human Gaussian Splatting from Video Diffusion Models
Zhibin Liu, Haoye Dong, Aviral Chharia, Hefeng Wu
Loopy: Taming Audio-Driven Portrait Avatar with Long-Term Motion Dependency
Jianwen Jiang, Chao Liang, Jiaqi Yang, Gaojie Lin, Tianyun Zhong, Yanbo Zheng
Solving Video Inverse Problems Using Image Diffusion Models
Taesung Kwon, Jong Chul Ye
Training-free Long Video Generation with Chain of Diffusion Model Experts
Wenhao Li, Yichao Cao, Xiu Su, Xi Lin, Shan You, Mingkai Zheng, Yi Chen, Chang Xu
TVG: A Training-free Transition Video Generation Method with Diffusion Models
Rui Zhang, Yaosen Chen, Yuegen Liu, Wei Wang, Xuming Wen, Hongxia Wang
CustomCrafter: Customized Video Generation with Preserving Motion and Concept Composition Abilities
Tao Wu, Yong Zhang, Xintao Wang, Xianpan Zhou, Guangcong Zheng, Zhongang Qi, Ying Shan, Xi Li
EasyControl: Transfer ControlNet to Video Diffusion for Controllable Generation and Interpolation
Cong Wang, Jiaxi Gu, Panwen Hu, Haoyu Zhao, Yuanfan Guo, Jianhua Han, Hang Xu, Xiaodan Liang