4 papers
PhyRPR: Training-Free Physics-Constrained Video Generation
Yibo Zhao, Hengjia Li, Xiaofei He +1
Recent diffusion-based video generation models can synthesize visually plausible videos, yet they often struggle to satisfy physical constraints. A key reason is that most existing…
SUDO: Enhancing Text-to-Image Diffusion Models with Self-Supervised Direct Preference Optimization
Liang Peng, Boxi Wu, Haoran Cheng +2
Previous text-to-image diffusion models typically employ supervised fine-tuning (SFT) to enhance pre-trained base models. However, this approach primarily minimizes the loss of mea…
Discriminator-Free Direct Preference Optimization for Video Diffusion
Haoran Cheng, Qide Dong, Liang Peng +7
Direct Preference Optimization (DPO), which aligns models with human preferences through win/lose data pairs, has achieved remarkable success in language and image generation. Howe…
SAMRefiner: Taming Segment Anything Model for Universal Mask Refinement
Yuqi Lin, Hengjia Li, Wenqi Shao +5
In this paper, we explore a principal way to enhance the quality of widely pre-existing coarse masks, enabling them to serve as reliable training data for segmentation models to re…