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
Towards Language-Driven Video Inpainting via Multimodal Large Language Models
Jianzong Wu, Xiangtai Li, Chenyang Si, Shangchen Zhou, Jingkang Yang, Jiangning Zhang, Yining Li, Kai Chen, Yunhai Tong, Ziwei Liu, Chen Change Loy
Motion-Zero: Zero-Shot Moving Object Control Framework for Diffusion-Based Video Generation
Changgu Chen, Junwei Shu, Lianggangxu Chen, Gaoqi He, Changbo Wang, Yang Li
AnimateZero: Video Diffusion Models are Zero-Shot Image Animators
Jiwen Yu, Xiaodong Cun, Chenyang Qi, Yong Zhang, Xintao Wang, Ying Shan, Jian Zhang
F3-Pruning: A Training-Free and Generalized Pruning Strategy towards Faster and Finer Text-to-Video Synthesis
Sitong Su, Jianzhi Liu, Lianli Gao, Jingkuan Song
BIVDiff: A Training-Free Framework for General-Purpose Video Synthesis via Bridging Image and Video Diffusion Models
Fengyuan Shi, Jiaxi Gu, Hang Xu, Songcen Xu, Wei Zhang, Limin Wang
DreamVideo: High-Fidelity Image-to-Video Generation with Image Retention and Text Guidance
Cong Wang, Jiaxi Gu, Panwen Hu, Songcen Xu, Hang Xu, Xiaodan Liang
Generative Rendering: Controllable 4D-Guided Video Generation with 2D Diffusion Models
Shengqu Cai, Duygu Ceylan, Matheus Gadelha, Chun-Hao Paul Huang, Tuanfeng Yang Wang, Gordon Wetzstein
ViVid-1-to-3: Novel View Synthesis with Video Diffusion Models
Jeong-gi Kwak, Erqun Dong, Yuhe Jin, Hanseok Ko, Shweta Mahajan, Kwang Moo Yi
TrackDiffusion: Tracklet-Conditioned Video Generation via Diffusion Models
Pengxiang Li, Kai Chen, Zhili Liu, Ruiyuan Gao, Lanqing Hong, Guo Zhou, Hua Yao, Dit-Yan Yeung, Huchuan Lu, Xu Jia
VMC: Video Motion Customization using Temporal Attention Adaption for Text-to-Video Diffusion Models
Hyeonho Jeong, Geon Yeong Park, Jong Chul Ye