DogWeave: High-Fidelity 3D Canine Reconstruction from a Single Image via Normal Fusion and Conditional Inpainting

TL;DR

DogWeave is a novel framework that reconstructs detailed, high-fidelity 3D canine models from a single image by refining geometry with normal fusion and generating consistent textures through conditional inpainting, outperforming existing methods.

Contribution

We introduce DogWeave, a model-based approach that enhances 3D canine reconstruction from a single image by combining multi-view normal fusion and structure-guided inpainting, addressing previous limitations.

Findings

Produces complete, realistic 3D dog models from a single image.

Outperforms state-of-the-art methods in shape accuracy and texture realism.

Uses only 7,000 images for training, demonstrating efficiency.

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.