6 papers
Train Short, Inference Long: Training-free Horizon Extension for Autoregressive Video Generation
Jia Li, Xiaomeng Fu, Xurui Peng +7
Autoregressive video diffusion models have emerged as a scalable paradigm for long video generation. However, they often suffer from severe extrapolation failure, where rapid error…
TAP: A Token-Adaptive Predictor Framework for Training-Free Diffusion Acceleration
Haowei Zhu, Tingxuan Huang, Xing Wang +7
Diffusion models achieve strong generative performance but remain slow at inference due to the need for repeated full-model denoising passes. We present Token-Adaptive Predictor (T…
Flow caching for autoregressive video generation
Yuexiao Ma, Xuzhe Zheng, Jing Xu +9
Autoregressive models, often built on Transformer architectures, represent a powerful paradigm for generating ultra-long videos by synthesizing content in sequential chunks. Howeve…
FlowAct-R1: Towards Interactive Humanoid Video Generation
Lizhen Wang, Yongming Zhu, Zhipeng Ge +15
Interactive humanoid video generation aims to synthesize lifelike visual agents that can engage with humans through continuous and responsive video. Despite recent advances in vide…
InfinityStar: Unified Spacetime AutoRegressive Modeling for Visual Generation
Jinlai Liu, Jian Han, Bin Yan +6
We introduce InfinityStar, a unified spacetime autoregressive framework for high-resolution image and dynamic video synthesis. Building on the recent success of autoregressive mode…
ERTACache: Error Rectification and Timesteps Adjustment for Efficient Diffusion
Xurui Peng, Chenqian Yan, Hong Liu +6
Diffusion models suffer from substantial computational overhead due to their inherently iterative inference process. While feature caching offers a promising acceleration strategy…