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arXiv · 2505.24877

AdaHuman: Animatable Detailed 3D Human Generation with Compositional Multiview Diffusion

Yangyi Huang·Ye Yuan·Xueting Li·Jan Kautz·Umar Iqbal
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Abstract

Existing methods for image-to-3D avatar generation struggle to produce highly detailed, animation-ready avatars suitable for real-world applications. We introduce AdaHuman, a novel framework that generates high-fidelity animatable 3D avatars from a single in-the-wild image. AdaHuman incorporates two key innovations: (1) A pose-conditioned 3D joint diffusion model that synthesizes consistent multi-view images in arbitrary poses alongside corresponding 3D Gaussian Splats (3DGS) reconstruction at each diffusion step; (2) A compositional 3DGS refinement module that enhances the details of local body parts through image-to-image refinement and seamlessly integrates them using a novel crop-aware camera ray map, producing a cohesive detailed 3D avatar. These components allow AdaHuman to generate highly realistic standardized A-pose avatars with minimal self-occlusion, enabling rigging and animation with any input motion. Extensive evaluation on public benchmarks and in-the-wild images demonstrates that AdaHuman significantly outperforms state-of-the-art methods in both avatar reconstruction and reposing. Code and models will be publicly available for research purposes.

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Yangyi Huang, Ye Yuan, Xueting Li, Jan Kautz, Umar Iqbal. 2025-05-30. AdaHuman: Animatable Detailed 3D Human Generation with Compositional Multiview Diffusion. https://arxiv.org/abs/2505.24877

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