We present a novel framework for generating high-quality, animatable 4D avatar from a single image. While recent advances have shown promising results in 4D avatar creation, existing methods either require extensive multiview data or struggle with shape accuracy and identity consistency. To address these limitations, we propose a comprehensive system that leverages shape, image, and video priors to create full-view, animatable avatars. Our approach first obtains initial coarse shape through 3D-GAN inversion. Then, it enhances multiview textures using depth-guided warping signals for cross-view consistency with the help of the image diffusion model. To handle expression animation, we incorporate a video prior with synchronized driving signals across viewpoints. We further introduce a Consistent-Inconsistent training to effectively handle data inconsistencies during 4D reconstruction. Experimental results demonstrate that our method achieves superior quality compared to the prior art, while maintaining consistency across different viewpoints and expressions.
View on arXiv@article{yin2025_2504.15179,
title={ FaceCraft4D: Animated 3D Facial Avatar Generation from a Single Image },
author={ Fei Yin and Mallikarjun B R and Chun-Han Yao and Rafał Mantiuk and Varun Jampani },
journal={arXiv preprint arXiv:2504.15179},
year={ 2025 }
}