Animate124: Animating One Image to 4D Dynamic Scene
- URL: http://arxiv.org/abs/2311.14603v2
- Date: Mon, 19 Feb 2024 02:30:14 GMT
- Title: Animate124: Animating One Image to 4D Dynamic Scene
- Authors: Yuyang Zhao, Zhiwen Yan, Enze Xie, Lanqing Hong, Zhenguo Li, Gim Hee
Lee
- Abstract summary: Animate124 is the first work to animate a single in-the-wild image into 3D video through textual motion descriptions.
Our method demonstrates significant advancements over existing baselines.
- Score: 108.17635645216214
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We introduce Animate124 (Animate-one-image-to-4D), the first work to animate
a single in-the-wild image into 3D video through textual motion descriptions,
an underexplored problem with significant applications. Our 4D generation
leverages an advanced 4D grid dynamic Neural Radiance Field (NeRF) model,
optimized in three distinct stages using multiple diffusion priors. Initially,
a static model is optimized using the reference image, guided by 2D and 3D
diffusion priors, which serves as the initialization for the dynamic NeRF.
Subsequently, a video diffusion model is employed to learn the motion specific
to the subject. However, the object in the 3D videos tends to drift away from
the reference image over time. This drift is mainly due to the misalignment
between the text prompt and the reference image in the video diffusion model.
In the final stage, a personalized diffusion prior is therefore utilized to
address the semantic drift. As the pioneering image-text-to-4D generation
framework, our method demonstrates significant advancements over existing
baselines, evidenced by comprehensive quantitative and qualitative assessments.
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