GAS: Generative Avatar Synthesis from a Single Image

• URL: http://arxiv.org/abs/2502.06957v2
• Date: Sun, 03 Aug 2025 14:14:09 GMT
• Title: GAS: Generative Avatar Synthesis from a Single Image
• Authors: Yixing Lu, Junting Dong, Youngjoong Kwon, Qin Zhao, Bo Dai, Fernando De la Torre,
• Abstract summary: We present a framework for synthesizing view-consistent and temporally coherent avatars from a single image.<n>Our approach combines the reconstruction power of regression-based 3D human reconstruction with the generative capabilities of a diffusion model.
• Score: 54.95198111659466
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a unified and generalizable framework for synthesizing view-consistent and temporally coherent avatars from a single image, addressing the challenging task of single-image avatar generation. Existing diffusion-based methods often condition on sparse human templates (e.g., depth or normal maps), which leads to multi-view and temporal inconsistencies due to the mismatch between these signals and the true appearance of the subject. Our approach bridges this gap by combining the reconstruction power of regression-based 3D human reconstruction with the generative capabilities of a diffusion model. In a first step, an initial 3D reconstructed human through a generalized NeRF provides comprehensive conditioning, ensuring high-quality synthesis faithful to the reference appearance and structure. Subsequently, the derived geometry and appearance from the generalized NeRF serve as input to a video-based diffusion model. This strategic integration is pivotal for enforcing both multi-view and temporal consistency throughout the avatar's generation. Empirical results underscore the superior generalization ability of our proposed method, demonstrating its effectiveness across diverse in-domain and out-of-domain in-the-wild datasets.

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