11 papers
Zero-shot Synthetic Video Realism Enhancement via Structure-aware Denoising
Yifan Wang, Liya Ji, Zhanghan Ke +3
We propose an approach to enhancing synthetic video realism, which can re-render synthetic videos from a simulator in photorealistic fashion. Our realism enhancement approach is a…
Enhancing Diffusion-based Restoration Models via Difficulty-Adaptive Reinforcement Learning with IQA Reward
Xiaogang Xu, Ruihang Chu, Jian Wang +6
Reinforcement Learning (RL) has recently been incorporated into diffusion models, e.g., tasks such as text-to-image. However, directly applying existing RL methods to diffusion-bas…
CML-Bench: A Framework for Evaluating and Enhancing LLM-Powered Movie Scripts Generation
Mingzhe Zheng, Dingjie Song, Guanyu Zhou +7
Large Language Models (LLMs) have demonstrated remarkable proficiency in generating highly structured texts. However, while exhibiting a high degree of structural organization, mov…
Hierarchical Fine-grained Preference Optimization for Physically Plausible Video Generation
Harold Haodong Chen, Haojian Huang, Qifeng Chen +2
Recent advancements in video generation have enabled the creation of high-quality, visually compelling videos. However, generating videos that adhere to the laws of physics remains…
VideoGen-of-Thought: Step-by-step generating multi-shot video with minimal manual intervention
Mingzhe Zheng, Yongqi Xu, Haojian Huang +8
Current video generation models excel at short clips but fail to produce cohesive multi-shot narratives due to disjointed visual dynamics and fractured storylines. Existing solutio…
VideoMerge: Towards Training-free Long Video Generation
Siyang Zhang, Harry Yang, Ser-Nam Lim
Long video generation remains a challenging and compelling topic in computer vision. Diffusion based models, among the various approaches to video generation, have achieved state o…