Single-Step Video Generation

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14 papers1 paper in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Guidance Is a Field, Not a Scalar: Interval-Integrated Classifier-Free Guidance for One-Step Video Generation
Nov 5, 2026Zijie Meng, Xixin CaoAdaptive CFG Classifier-Free Guidance
Peking University
NextGAN: Towards Simple and Scalable GAN Training for Image and Video Generation
Nov 4, 2026Xinhao Lou, Dongye Xie, Peng Sun +3Generative Adversarial Networks Image Generation
Westlake University · University of Cambridge · Zhejiang University +1
Any Forcing: Any-Step Generality and One-Step Stability for Autoregressive Video Generation
Oct 30, 2026Jiahua Xu, Yijie Fang, Nannan Wang +6Autoregressive Video Generation Long-Horizon Video Generation
State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an, China · Technology Planning Dept of the Computing Technology Research Institute, Huawei, Shanghai, China · Huawei Noah’s Ark Institute, Huawei, Shenzhen, China
DuoMatching: Joint-Marginal Distribution Matching for Few-Step Video Generation
Oct 2, 2026Jiahao Zhan, Yan Wang, Yongrui Ma +5Single-Step Video Generation Streaming Video Generation
MMLab, CUHK · ByteDance Inc. · HKUST +1
Uncertainty-Aware Consistency Distillation for Few-Step Video Generation
Sep 30, 2026Lingyu Liu, Yaxiong Wang, Li Zhu +1Video Diffusion Models Few-Step Diffusion Sampling
School of Software Xi’an Jiaotong University Xi’an, China. · School of Computer and Information Science Hefei University of Technology Hefei, China. · Faculty of Science and Technology, and Institute of Collaborative Innovation University of Macau Macau, China.
FlashRender: Few-Step Generative Rendering via Camera-Controlled Video MeanFlow
Sep 3, 2026Byeongjun Park, Byung-Hoon Kim, Hyungjin ChungFlow Matching Camera-Controlled Video Generation
EverEx · Yonsei University · Korea University
ForgeWM: Progressive Causal Training for Few-Step Action-Conditioned Video World Models
Aug 14, 2026Xinye Li, Lingshuai Lin, Lei Wang +8Single-Step Video Generation Video World Models
CUHK · Tencent PCG · FDU +2
Alaya-EVOKE: From Linear-Scaling Supervision to Endless World
Aug 13, 2026Yuanyang Yin, Gongxuan Wang, Yifan Zhan +3Open-Ended Generation Long-Horizon Video Generation
MoE Key Lab of BIPC, USTC · Shanghai Innovation Institute · Alaya Lab
DUET: A Diversity-Quality Duet of Distillation Experts for Two-Step Video Generation
Aug 10, 2026Zian Li, Litong Gong, Borui Liao +6Video Diffusion Models Diffusion Model Distillation
Institute for Artificial Intelligence, Peking University, Beijing, China · School of Intelligence Science and Technology, Peking University, Beijing, China · *Work done as an intern at Alibaba Group +3
LeapTalk: Breaking the Latency-Quality Trade-off in Talking Head Generation
Jul 29, 2026Rongxiang Zhang, Songhua LiuAudio-Video Generation Diffusion Model Distillation
School of Artificial Intelligence, Shanghai Jiao Tong University · 2Harbin Institute of Technology
AAD-1: Asymmetric Adversarial Distillation for One-Step Autoregressive Video Generation
Jun 2, 2026Haobo Li, Yanhong Zeng, Yunhong Lu +6Image-to-Video Generation Autoregressive Video Generation
AutoLab, SAI, SJTU · Ant Group · Department of Automation, Tsinghua University +2
One-Forcing: Towards Stable One-Step Autoregressive Video Generation
May 22, 2026Jiaqi Feng, Justin Cui, Yuanhao Ban +1Autoregressive Video Generation One-Step Generative Modeling
Tsinghua University · UCLA
Adaptive Video Distillation: Mitigating Oversaturation and Temporal Collapse in Few-Step Generation
Mar 23, 2026Yuyang You, Yongzhi Li, Jiahui Li +3Single-Step Video Generation Diffusion Model Distillation
Peking University · Kuaishou Technology
FlashMotion: Few-Step Controllable Video Generation with Trajectory Guidance
Mar 12, 2026Quanhao Li, Zhen Xing, Rui Wang +4Single-Step Video Generation Trajectory-Guided Video Generation
Institute of Trustworthy Embodied AI, Fudan University · Shanghai Key Laboratory of Multimodal Embodied AI · Microsoft Research Asia

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Guidance Is a Field, Not a Scalar: Interval-Integrated Classifier-Free Guidance for One-Step Video Generation

Zijie Meng, Xixin Cao

Peking University

Adaptive CFG Classifier-Free Guidance One-Step Generative Modeling Single-Step Video Generation MeanFlow

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One-step generators built on MeanFlow synthesize a video in a single function evaluation, yet inherit a rigid treatment of classifier-free guidance (CFG): the guidance scale is frozen during training or, at best, exposed as a single scalar. This conflicts with mounting evidence that the optimal guidance is not a constant but a field that varies with the noise level, with spatial content, and, for video, with frame position. Every existing remedy presupposes a multi-step sampler along which guidance can be re-scheduled--an assumption one-step models violate by construction. We resolve this tension through a single observation: because the MeanFlow target is an integral over a noise interval, an arbitrary guidance field can be absorbed into the target itself and compiled into one evaluation. We formalize this as the interval-integrated CFG identity, prove that the compiled guidance carries no schedule-discretization error and can thus match or exceed any finite multi-step sampler, and instantiate it as AGAVE, which equips the average-velocity field with a learnable temporal schedule, a free motion-saliency spatial modulation, and a frame-position asymmetry. Adding fewer than ten scalars at zero inference cost, AGAVE surpasses all one-step baselines on VBench, UCF-101 and MSR-VTT while matching multi-step guided sampling. Project repository: https://github.com/YMlinfeng/AGAVE.