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New framework "Any Forcing" boosts autoregressive video generation stability

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced "Any Forcing," a novel framework designed to enhance autoregressive video generation. This approach aims to provide flexibility in sampling steps for balancing quality and efficiency, while also improving the stability of one-step generation. The framework incorporates a unified adversarial discriminator for distribution alignment and quality enhancement, alongside an adaptive long-term memory selection mechanism to maintain temporal consistency in long video generation. Experiments show Any Forcing can achieve high-quality, stable one-step video generation at speeds of 20.9 FPS for short videos and 24.7 FPS for long videos on a single H100 GPU. AI

IMPACT Enhances autoregressive video generation efficiency and stability, potentially enabling more fluid and higher-quality AI-generated video content.

RANK_REASON The item is a research paper detailing a new technical framework for video generation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework "Any Forcing" boosts autoregressive video generation stability

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The item is a research paper detailing a new technical framework for video generation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Jiahua Xu, Yijie Fang, Nannan Wang, Xunhao Lin, Li Cao, Lei Hao, Mingrui Zhu, Fei Gao, Xinbo Gao ·

    Any Forcing: Any-Step Generality and One-Step Stability for Autoregressive Video Generation

    arXiv:2610.42031v1 Announce Type: new Abstract: Autoregressive video generation enables efficient inference through streaming prediction. However, existing methods are typically restricted to fixed sampling steps, limiting the flexibility to balance generation quality and efficie…

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