AtomoVideo: High Fidelity Image-to-Video Generation
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/.
65e73216f9736183791ee60a
Litong Gong
admin_assigned
2024-03-05T20:47:03.753Z
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Litong.G
user
culeao
65e73216f9736183791ee60b
Yiran Zhu
admin_assigned
2024-03-06T11:38:17.970Z
63ecce0054bba678151814cf
https://cdn-avatars.huggingface.co/v1/production/uploads/1676463764139-63ecce0054bba678151814cf.png
y1Ran
user
y1Ran
65e73216f9736183791ee60c
Weijie Li
65e73216f9736183791ee60d
Xiaoyang Kang
admin_assigned
2024-03-06T17:20:15.974Z
63e4f0a15bbdd8d44b540925
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KK
user
piddnad
65e73216f9736183791ee60e
Biao Wang
claimed_verified
2024-03-06T15:34:29.872Z
64dcd9de0a0efd28e3b846e3
/avatars/0732be326b1d3658118f669dd283d582.svg
ictwangbiao
user
ictwangbiao
65e73216f9736183791ee60f
Tiezheng Ge
65e73216f9736183791ee610
Bo Zheng
admin_assigned
2024-03-05T20:50:07.099Z
62c695ad5aae1c624ca992e2
/avatars/20d10fb3338e4bd4dc59e88a18cb2617.svg
Bo Zheng
user
bzheng
AtomoVideo: High Fidelity Image-to-Video Generation
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/.
AtomoVideo generates high-fidelity videos from images using multi-granularity injection, achieving superior motion intensity and temporal stability.
multi-granularity image injection temporal consistency frame prediction iterative generation adapter training personalized models controllable modules