仅用一张图重建高保真狗3D模型，解决细节失真和背面缺失问题。
DogWeave: High-Fidelity 3D Canine Reconstruction from a Single Image via Normal Fusion and Conditional Inpainting
- 通过法向场优化与条件修复，融合多视角法线信息
- 用7000张训练图像实现形状准确、纹理真实的3D重建
- 适合需要高质量犬类3D建模的科研与动画应用
单目3D动物重建因复杂的关节运动、自我遮挡及毛发等细粒度细节而困难。现有方法因缺乏姿态化3D监督和2D数据集中后视图稀缺，常导致几何扭曲和纹理不一致。为此，我们提出DogWeave，一个基于模型的框架，仅凭单张RGB图像即可重建高保真犬类3D模型。该方法先通过扩散增强法线进行多视角法向场优化，将粗略参数网格细化为精细SDF表示；再利用结构与风格线索引导的条件局部修复生成视图一致的纹理，实现未观测区域的逼真重建。仅使用约7,000张经2D流水线处理的狗图像训练，DogWeave在犬类3D重建的形状准确性和纹理真实度上均优于当前最优单图重建方法。
原文摘要 · Abstract (English)
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.
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