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AnimateLCM: Computation-Efficient Personalized Style Video Generation without Personalized Video Data

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arxiv 2402.00769 v3 pith:AW7ZTGJJ submitted 2024-02-01 cs.CV cs.LG

Fu-Yun Wang , Zhaoyang Huang , Weikang Bian , Xiaoyu Shi , Keqiang Sun , Guanglu Song , Yu Liu , Hongsheng Li This is my paper · ORCID
classification cs.CVcs.LG
keywords videogenerationpersonalizedstyledataaccelerationwithoutcomputation-efficient
verification ladder T0 review T1 audit T2 compute T3 formal
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This paper introduces an effective method for computation-efficient personalized style video generation without requiring access to any personalized video data. It reduces the necessary generation time of similarly sized video diffusion models from 25 seconds to around 1 second while maintaining the same level of performance. The method's effectiveness lies in its dual-level decoupling learning approach: 1) separating the learning of video style from video generation acceleration, which allows for personalized style video generation without any personalized style video data, and 2) separating the acceleration of image generation from the acceleration of video motion generation, enhancing training efficiency and mitigating the negative effects of low-quality video data.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Yume: An Interactive World Generation Model

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A diffusion-based video model generates extendable, keyboard-controlled walkthroughs from a single input image, using quantized camera actions as text prompts.

  2. TaxaDiffusion: Progressively Trained Diffusion Model for Fine-Grained Species Generation

    cs.CV 2025-06 conditional novelty 5.0 of 10

    A diffusion model trained progressively from Kingdom to Species generates more accurate fine-grained animal images, including rare species with as few as one training sample.

  3. Reinforcement Learning: From Algorithms To Foundation Models

    cs.AI 2026-07 conditional novelty 3.0 of 10

    A dissertation uniting the author's published results: non-exploitable Nash-DQN policies and the FightLadder benchmark for games, plus diffusion/consistency-model world models for RL — a compilation rather than new results.

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