37 citations · 91 across the 17 of their papers we have counts for
16 papers · 1 filter
Make-Your-Anchor: A Diffusion-based 2D Avatar Generation Framework
Ziyao Huang, Fan Tang, Yong Zhang +4
Despite the remarkable process of talking-head-based avatar-creating solutions, directly generating anchor-style videos with full-body motions remains challenging. In this study, w…
Depth-aware Test-Time Training for Zero-shot Video Object Segmentation
Weihuang Liu, Xi Shen, Haolun Li +4
Zero-shot Video Object Segmentation (ZSVOS) aims at segmenting the primary moving object without any human annotations. Mainstream solutions mainly focus on learning a single model…
VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models
Haoxin Chen, Yong Zhang, Xiaodong Cun +4
Text-to-video generation aims to produce a video based on a given prompt. Recently, several commercial video models have been able to generate plausible videos with minimal noise,…
Towards A Better Metric for Text-to-Video Generation
Jay Zhangjie Wu, Guian Fang, Haoning Wu +11
Generative models have demonstrated remarkable capability in synthesizing high-quality text, images, and videos. For video generation, contemporary text-to-video models exhibit imp…
VideoCrafter1: Open Diffusion Models for High-Quality Video Generation
Haoxin Chen, Menghan Xia, Yingqing He +9
Video generation has increasingly gained interest in both academia and industry. Although commercial tools can generate plausible videos, there is a limited number of open-source m…
ScaleCrafter: Tuning-free Higher-Resolution Visual Generation with Diffusion Models
Yingqing He, Shaoshu Yang, Haoxin Chen +7
In this work, we investigate the capability of generating images from pre-trained diffusion models at much higher resolutions than the training image sizes. In addition, the genera…