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

• URL: http://arxiv.org/abs/2505.24877v1
• Date: Fri, 30 May 2025 17:59:54 GMT
• Title: AdaHuman: Animatable Detailed 3D Human Generation with Compositional Multiview Diffusion
• Authors: Yangyi Huang, Ye Yuan, Xueting Li, Jan Kautz, Umar Iqbal,
• Abstract summary: AdaHuman is a novel framework that generates high-fidelity animatable 3D avatars from a single in-the-wild image.<n>AdaHuman incorporates two key innovations: a pose-conditioned 3D joint diffusion model and a compositional 3DGS refinement module.
• Score: 56.12859795754579
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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