[2402.00769] AnimateLCM: Computation-Efficient Personalized Style Video Generation without Personalized Video Data
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Computer Science > Computer Vision and Pattern Recognition

arXiv:2402.00769 (cs)
[Submitted on 1 Feb 2024 (v1), last revised 16 Oct 2024 (this version, v3)]

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

Authors:Fu-Yun Wang, Zhaoyang Huang, Weikang Bian, Xiaoyu Shi, Keqiang Sun, Guanglu Song, Yu Liu, Hongsheng Li
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Abstract: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.
Comments: Accepted as a Short Paper by SIGGRAPH ASIA 2024 Technical Communications. This is a short version of the original work. Project Page: this https URL
Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2402.00769 [cs.CV]
  (or arXiv:2402.00769v3 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2402.00769

Submission history

From: Fu-Yun Wang [view email]
[v1] Thu, 1 Feb 2024 16:58:11 UTC (13,269 KB)
[v2] Tue, 15 Oct 2024 08:22:05 UTC (12,460 KB)
[v3] Wed, 16 Oct 2024 04:53:55 UTC (12,460 KB)
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