This paper studies the human image animation task, which aims to generate a video of a certain reference identity following a particular motion sequence. Existing animation works typically employ the frame-warping technique to animate the reference image towards the target motion. Despite achieving reasonable results, these approaches face challenges in maintaining temporal consistency throughout the animation due to the lack of temporal modeling and poor preservation of reference identity. In this work, we introduce MagicAnimate, a diffusion-based framework that aims at enhancing temporal consistency, preserving reference image faithfully, and improving animation fidelity. To achieve this, we first develop a video diffusion model to encode temporal information. Second, to maintain the appearance coherence across frames, we introduce a novel appearance encoder to retain the intricate details of the reference image. Leveraging these two innovations, we further employ a simple video fusion technique to encourage smooth transitions for long video animation. Empirical results demonstrate the superiority of our method over baseline approaches on two benchmarks. Notably, our approach outperforms the strongest baseline by over 38% in terms of video fidelity on the challenging TikTok dancing dataset. Code and model will be made available.

这篇论文研究了人物图像动画任务，提出了一种基于扩散的框架MagicAnimate，旨在提高动画的时间一致性、保持参考图像的准确性和提升动画质量。通过引入视频扩散模型、新颖的外观编码器和简单的视频融合技术，该方法在两个基准测试中表现出卓越的性能，其中在具有挑战性的TikTok舞蹈数据集上，视频保真度比最强基线方法提高了38%以上。

MagicAnimate: 使用扩散模型的时间一致人体图像动画