1 citations · 1 across the 10 of their papers we have counts for
10 papers
An Empirical Study of Training Pixel-Space Text-to-Image Diffusion Models
Dengyang Jiang, Ruoyi Du, Zhennan Chen +10
This paper investigates an increasingly important topic in generative modeling: pixel-space diffusion models. Although numerous studies have explored this topic, most focus on smal…
Learning Explicit Physical Parameter Control and Benchmarking for Video Generation
Yanxun Li, Hao Wen, Bingze Song +7
Recent advances in image-to-video generation have improved visual realism, making physically grounded and controllable dynamics an important step toward future world simulation. Cu…
Z-Reward: Beyond Scalar Rewards by Internalizing Reasoning into Score Distributions
Xin Jin, Huanqia Cai, Zhen Li +9
Reward models are central to text-to-image post-training, but visual preference is subjective and better represented as a distribution over rubric scores than as a deterministic sc…
Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation
Chenxi Zhao, Chen Zhu, Xiaokun Feng +6
Few-step generation has been a long-standing goal, with recent one-step generation methods exemplified by MeanFlow achieving remarkable results. Existing research on MeanFlow prima…
MAR-GRPO: Stabilized GRPO for AR-diffusion Hybrid Image Generation
Xiaoxiao Ma, Jiachen Lei, Tianfei Ren +6
Reinforcement learning (RL) has been successfully applied to autoregressive (AR) and diffusion models. However, extending RL to hybrid AR-diffusion frameworks remains challenging d…
ConceptWeaver: Weaving Disentangled Concepts with Flow
Jintao Chen, Aiming Hao, Xiaoqing Chen +6
Pre-trained flow-based models excel at synthesizing complex scenes yet lack a direct mechanism for disentangling and customizing their underlying concepts from one-shot real-world…
Ranking-aware Reinforcement Learning for Ordinal Ranking
Aiming Hao, Chen Zhu, Jiashu Zhu +2
Ordinal regression and ranking are challenging due to inherent ordinal dependencies that conventional methods struggle to model. We propose Ranking-Aware Reinforcement Learning (RA…
Taming Hallucinations: Boosting MLLMs' Video Understanding via Counterfactual Video Generation
Zhe Huang, Hao Wen, Aiming Hao +6
Multimodal Large Language Models (MLLMs) have made remarkable progress in video understanding. However, they suffer from a critical vulnerability: an over-reliance on language prio…
Omni-Effects: Unified and Spatially-Controllable Visual Effects Generation
Fangyuan Mao, Aiming Hao, Jintao Chen +7
Visual effects (VFX) are essential visual enhancements fundamental to modern cinematic production. Although video generation models offer cost-efficient solutions for VFX productio…
VMBench: A Benchmark for Perception-Aligned Video Motion Generation
Xinran Ling, Chen Zhu, Meiqi Wu +7
Video generation has advanced rapidly, improving evaluation methods, yet assessing video's motion remains a major challenge. Specifically, there are two key issues: 1) current moti…