AtomoVideo: High Fidelity Image-to-Video Generation

Litong.G, y1Ran, Weijie Li, KK, ictwangbiao, Tiezheng Ge, Bo Zheng

AtomoVideo: High Fidelity Image-to-Video Generation: 22 upvotes on Hugging Face Daily Papers, #3 of 11 papers on 2024-03-05. Day-by-day upvote history.

Recently, video generation has achieved significant rapid development based on superior text-to-image generation techniques. In this work, we propose a high fidelity framework for image-to-video generation, named AtomoVideo. Based on multi-granularity image injection, we achieve higher fidelity of the generated video to the given image. In addition, thanks to high quality datasets and training strategies, we achieve greater motion intensity while maintaining superior temporal consistency and stability. Our architecture extends flexibly to the video frame prediction task, enabling long sequence prediction through iterative generation. Furthermore, due to the design of adapter training, our approach can be well combined with existing personalised models and controllable modules. By quantitatively and qualitatively evaluation, AtomoVideo achieves superior results compared to popular methods, more examples can be found on our project website: https://atomo- video.github.io/.

Paper page on Hugging Face · arXiv

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