18 papers · 1 filter
Dreaming in Flow: Generative Grounding Feedback for Self-Evolving Unified Multimodal Models
Ke Hao, Yuanzhi Liang, Tingxi Chen +5
Unified multimodal models integrate visual understanding and generation within a single network, yet the two capabilities are commonly optimized as separate tasks. We introduce Gen…
Sample-Adaptive Latent Rewards for Uncertainty-Guided Diffusion Post-Training
Rui Li, Yuanzhi Liang, Ke Hao +4
Latent reward models can supervise visual diffusion models without decoding intermediate states into pixel space. This makes alignment with human preferences more efficient. Howeve…
VideoWeave: Unlocking Geometric Consistency in Video Generation via Joint Geometry-Video Modeling
Xunzhi Xiang, Zixuan Duan, Yabo Chen +8
Large-scale video diffusion models often fail to preserve 3D structure over time, causing geometric drift and implausible motion under viewpoint changes. Existing methods usually e…
Reinforcing Few-step Generators via Reward-Tilted Distribution Matching
Yushi Huang, Xiangxin Zhou, Ruoyu Wang +3
Recent advances in few-step diffusion distillation have enabled efficient image generation, yet aligning these models with human preferences remains challenging. We propose Reward-…
Full-4D: Generating Full-Scope 4D Scenes from a Single-View Video
Tingxi Chen, Ke Hao, Yabo Chen +6
Generating 4D scenes from a single-view video is inherently ill-posed: a single viewpoint lacks the information needed to recover a complete, dynamic scene with full coverage. Exis…
Learning to Credit the Right Steps: Objective-aware Process Optimization for Visual Generation
Rui Li, Ke Hao, Yuanzhi Liang +4
Reinforcement learning, particularly Group Relative Policy Optimization (GRPO), has emerged as an effective framework for post-training visual generative models with human preferen…