4 papers
SARA: Semantically Adaptive Relational Alignment for Video Diffusion Models
Jiesong Lian, Zixiang Zhou, Ruizhe Zhong +6
Recent video diffusion models (VDMs) synthesize visually convincing clips, yet still drop entities, mis-bind attributes, and weaken the interactions specified in the prompt. Repres…
Euphonium: Steering Video Flow Matching via Process Reward Gradient Guided Stochastic Dynamics
Ruizhe Zhong, Jiesong Lian, Xiaoyue Mi +4
While online Reinforcement Learning has emerged as a crucial technique for aligning flow matching models with human preferences, current approaches are hindered by inefficient expl…
SoliReward: Mitigating Susceptibility to Reward Hacking and Annotation Noise in Video Generation Reward Models
Jiesong Lian, Ruizhe Zhong, Zixiang Zhou +6
Post-training alignment of video generation models with human preferences is a critical goal. Developing effective Reward Models (RMs) for this process faces significant methodolog…
Video Generation Models Are Good Latent Reward Models
Xiaoyue Mi, Wenqing Yu, Jiesong Lian +9
Reward feedback learning (ReFL) has proven effective for aligning image generation with human preferences. However, its extension to video generation faces significant challenges.…