3 papers
cs.CV2026
Contrastive-Augmented Flow Matching for Style-Content Disentanglement
Yusong Li, Pingchuan Ma, Ming Gui +2
Learning representations that separate content and style is crucial for controllable generation and compositional generalization. However, diffusion and flow-based models trained p…
cs.LG2025
DAWM: Diffusion Action World Models for Offline Reinforcement Learning via Action-Inferred Transitions
Zongyue Li, Xiao Han, Yusong Li +2
Diffusion-based world models have demonstrated strong capabilities in synthesizing realistic long-horizon trajectories for offline reinforcement learning (RL). However, many existi…
cs.CV2025
SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models
Pingchuan Ma, Xiaopei Yang, Yusong Li +4
Explicitly disentangling style and content in vision models remains challenging due to their semantic overlap and the subjectivity of human perception. Existing methods propose sep…