Abstract
Monocular 3D animal reconstruction is challenging due to complex articulation, self-occlusion, and fine-scale details such as fur. Existing methods often produce distorted geometry and inconsistent textures due to the lack of articulated 3D supervision and limited availability of back-view images in 2D datasets, which makes reconstructing unobserved regions particularly difficult. To address these limitations, we propose DogWeave, a model-based framework for reconstructing high-fidelity 3D canine models from a single RGB image. DogWeave improves geometry by refining a coarsely-initiated parametric mesh into a detailed SDF representation through multi-view normal field optimization using diffusion-enhanced normals. It then generates view-consistent textures through conditional partial inpainting guided by structure and style cues, enabling realistic reconstruction of unobserved regions. Using only about 7,000 dog images processed via our 2D pipeline for training, DogWeave produces complete, realistic 3D models and outperforms state-of-the-art single image to 3d reconstruction methods in both shape accuracy and texture realism for canines.
Abstract (translated)
单目三维动物重建由于复杂的关节运动、自遮挡以及如毛发这样的细小特征，极具挑战性。现有方法通常会因缺乏关节运动的三维监督和二维数据集中背面视角图像的稀缺而产生几何失真和纹理不一致的问题，这使得难以重建未观察到的区域。为解决这些问题，我们提出了DogWeave——一种基于模型的框架，用于从单张RGB图像中重构高质量的3D犬类模型。 DogWeave通过多视图法线场优化（利用扩散增强的法线）来细化初始参数化网格，从而改进几何形状，并将其转化为详细的SDF表示。随后，它根据结构和风格提示生成一致的纹理，通过条件部分修复完成未观察区域的真实重建工作。仅使用我们处理过的约7,000张犬类图像（经过2D管道训练），DogWeave能够产生完整且真实的3D模型，并在犬类形状准确度和纹理真实感方面超越了最先进的单图到三维重建方法。
URL
https://arxiv.org/abs/2603.07441
