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cs.CV2026

Manifold-Aware Exploration for Reinforcement Learning in Video Generation

Mingzhe Zheng, Weijie Kong, Yue Wu +9

Group Relative Policy Optimization (GRPO) methods for video generation like FlowGRPO remain far less reliable than their counterparts for language models and images. This gap arise…

cs.CV2025

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…

cs.CV2025

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…

cs.CV2025

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…

cs.CV2025

Follow-Your-Shape: Shape-Aware Image Editing via Trajectory-Guided Region Control

Zeqian Long, Mingzhe Zheng, Kunyu Feng +6

While recent flow-based image editing models demonstrate general-purpose capabilities across diverse tasks, they often struggle to specialize in challenging scenarios -- particular…

cs.CV2025

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…