Alignment is All You Need: A Training-free Augmentation Strategy for Pose-guided Video Generation

• URL: http://arxiv.org/abs/2408.16506v1
• Date: Thu, 29 Aug 2024 13:08:12 GMT
• Title: Alignment is All You Need: A Training-free Augmentation Strategy for Pose-guided Video Generation
• Authors: Xiaoyu Jin, Zunnan Xu, Mingwen Ou, Wenming Yang,
• Abstract summary: We introduce a training-free framework that ensures the generated video sequence preserves the reference image's subtleties. We decouple skeletal and motion priors from pose information, enabling precise control over animation generation. Our method significantly enhances the quality of video generation without the need for large datasets or expensive computational resources.
• Score: 19.408715783816167
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Character animation is a transformative field in computer graphics and vision, enabling dynamic and realistic video animations from static images. Despite advancements, maintaining appearance consistency in animations remains a challenge. Our approach addresses this by introducing a training-free framework that ensures the generated video sequence preserves the reference image's subtleties, such as physique and proportions, through a dual alignment strategy. We decouple skeletal and motion priors from pose information, enabling precise control over animation generation. Our method also improves pixel-level alignment for conditional control from the reference character, enhancing the temporal consistency and visual cohesion of animations. Our method significantly enhances the quality of video generation without the need for large datasets or expensive computational resources.

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