2 citations · 3 across the 14 of their papers we have counts for
7 papers · 1 filter
Towards Error-Free Long Video Generation
Shuning Chang, Weihua Chen, Jiasheng Tang +8
Recent advances in video generation have made minute-level synthesis possible; however, generating long videos remains challenging due to error accumulation, attribute drift, and t…
SpecLoR: Spectral Lookahead Rectification for Motion-Coherent Text-to-Video Generation
Xu Zhang, Yu Lu, Ruijie Quan +3
Flow Matching has enabled robust text-to-video generation via latent ODE sampling. However, velocity approximation and numerical discretization errors inevitably accumulate, causin…
FadeMem: Distance-Aware Memory Consolidation for Autoregressive Video Diffusion
Yu Lu, Junjie Yang, Piotr Koniusz +2
Autoregressive video generators synthesize long videos by generating successive temporal segments, but their historical KV cache grows with video length. Existing bounded-cache met…
Power Reinforcement Post-Training of Text-to-Image Models with Super-Linear Advantage Shaping
Haoyuan Sun, Jing Wang, Yuxin Song +9
Recently, post-training methods based on reinforcement learning, with a particular focus on Group Relative Policy Optimization (GRPO), have emerged as the robust paradigm for furth…
SAMA: Factorized Semantic Anchoring and Motion Alignment for Instruction-Guided Video Editing
Xinyao Zhang, Wenkai Dong, Yuxin Song +10
Current instruction-guided video editing models struggle to simultaneously balance precise semantic modifications with faithful motion preservation. While existing approaches rely…
GIFSplat: Generative Prior-Guided Iterative Feed-Forward 3D Gaussian Splatting from Sparse Views
Tianyu Chen, Wei Xiang, Kang Han +4
Feed-forward 3D reconstruction offers substantial runtime advantages over per-scene optimization, which remains slow at inference and often fragile under sparse views. However, exi…
CoLoGen: Progressive Learning of Concept-Localization Duality for Unified Image Generation
YuXin Song, Yu Lu, Haoyuan Sun +6
Unified conditional image generation remains difficult because different tasks depend on fundamentally different internal representations. Some require conceptual understanding for…