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Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation

2023/11/28 by Li Hu, Xin Gao, Hu, Li +9 · 1 voice · 256 citations
Computer Science · Engineering · #3D Shape Modeling and Analysis #Animation #Artificial intelligence #Character (mathematics) #Character animation #Computer animation #Computer graphics (images) #Computer science #Computer vision #Generative Adversarial Networks and Image Synthesis #Human Motion and Animation #Information retrieval #Merge (version control) #cs.CV

paper · pdf · doi:10.48550/arxiv.2311.17117

published in arXiv (Cornell University) (Cornell University)

openalex publication_date 2023/11/28 · openalex created_date 2023/12/01 · openalex updated_date 2026/07/28

Abstract

Character Animation aims to generating character videos from still images through driving signals. Currently, diffusion models have become the mainstream in visual generation research, owing to their robust generative capabilities. However, challenges persist in the realm of image-to-video, especially in character animation, where temporally maintaining consistency with detailed information from character remains a formidable problem. In this paper, we leverage the power of diffusion models and propose a novel framework tailored for character animation. To preserve consistency of intricate appearance features from reference image, we design ReferenceNet to merge detail features via spatial attention. To ensure controllability and continuity, we introduce an efficient pose guider to direct character's movements and employ an effective temporal modeling approach to ensure smooth inter-frame transitions between video frames. By expanding the training data, our approach can animate arbitrary characters, yielding superior results in character animation compared to other image-to-video methods. Furthermore, we evaluate our method on benchmarks for fashion video and human dance synthesis, achieving state-of-the-art results.

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