3 papers
cs.CV2026
Joint Alignment and Distillation for Video Generation via Sample-Guided Distribution Matching
Jiuzhou Lin, Junlong Wu, Fei Zuo +11
Aligning video generative models to human preferences heavily relies on Reinforcement Learning (RL), which suffers from extensive computational overhead. Existing workflows typical…
cs.CV2026
Step Back to Move Forward: Reflection-Aware Preference Optimization for Visual Generation
Junlong Wu, Jiuzhou Lin, Jia Sun +7
Diffusion models have become the mainstream paradigm for modern visual generation and have substantially advanced multimedia content synthesis, especially in text-to-image and text…
cs.CV2026
CaC: Advancing Video Reward Models via Hierarchical Spatiotemporal Concentrating
Jiyuan Wang, Huan Ouyang, Jiuzhou Lin +15
In this paper, we propose Concentrate and Concentrate (CaC), a coarse-to-fine anomaly reward model based on Vision-Language Models. During inference, it first conducts a global tem…