标题： AdaHuman：基于组合多视图扩散的可动画详细3D人体生成 显示英文标题

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

摘要： 现有的图像到3D头像生成方法难以生成高度详细的、可动画化的头像，这些头像适用于现实世界的应用场景。 我们引入了AdaHuman，这是一种新颖的框架，可以从单张野外图像生成高保真的可动画化3D头像。 AdaHuman包含了两项关键创新：（1）一种姿态条件的3D关节扩散模型，在每个扩散步骤中，可以同时合成任意姿态下的多视角图像以及对应的3D高斯光点（3DGS）重建；（2）一种组合式3DGS细化模块，通过图像到图像的细化来增强局部身体部位的细节，并利用一种新颖的裁剪感知相机射线图无缝地将它们集成起来，从而生成一个连贯且详细的3D头像。 这些组件使AdaHuman能够生成高度逼真的标准A-pose头像，最大程度减少自遮挡现象，支持与任何输入运动的绑定和动画处理。 在公共基准数据集和野外图像上的广泛评估表明，AdaHuman在头像重建和重新定位方面显著优于最先进的方法。 代码和模型将公开用于研究目的。 显示英文摘要

摘要： 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.