仅用一张野外照片重建高精度可动画的狗3D模型
CORGI: Consistency-Aware 3D Dog Reconstruction from a Single Image in the Wild
- 通过规范姿态生成360度视频观测，解决单图歧义问题
- 利用神经变形场建模每视角生成误差，提升顶点级精度
- 自监督修复模块恢复高频细节，避免结构畸变
从一张无约束的野外图像中重建高保真、高度灵活的动物（如狗）3D模型仍是重大挑战。本文提出CORGI框架，完全无需3D监督即可实现一致性感知的3D狗重建。为克服生成不一致与缺乏多视角数据的问题，该方法引入三个核心组件：首先，提出基于规范姿态驱动的轨道生成（CDOG）策略，使用专用的规范与轨道LoRA对任意输入姿态进行归一化，并合成可靠的360度视频观测；其次，设计一致性感知可变形3DGS（CA-3DGS）模块，基于D-SMAL先验，通过专用神经变形场显式建模每视角生成误差，学习精确的顶点级位移；最后，引入自监督变形条件生成修复（DCGR）模块，消除结构畸变并恢复高频细节。大量实验表明，CORGI在多种犬种上均达到顶尖性能，生成几何准确、视觉连贯且完全可动画的3D资产，可直接用于下游应用。
原文摘要 · Abstract (English)
Reconstructing high-fidelity 3D models of highly articulated animals, such as dogs, from a single in-the-wild image remains a formidable challenge. In this paper, we introduce CORGI, a novel framework for consistency-aware 3D dog reconstruction from a single unconstrained image that completely eliminates the need for 3D supervision. To overcome generative inconsistencies and the lack of multi-view capture, our pipeline introduces three core components. First, we propose a Canonical-Driven Orbital Generation (CDOG) strategy, utilizing specialized Canonical and Orbit LoRAs to normalize arbitrary input poses and synthesize reliable 360-degree video observations. Second, we design a Consistency-aware Deformable 3DGS (CA-3DGS) module that anchors on a D-SMAL prior, explicitly modeling per-view generative errors through dedicated neural deformation fields to learn accurate vertex-level displacements. Finally, to eliminate structural distortions and recover high-frequency details, we introduce a self-supervised Deformation-Conditioned Generative Repair (DCGR) module. Extensive experiments demonstrate that CORGI achieves state-of-the-art performance, generalizing seamlessly across diverse dog breeds to produce geometrically accurate, visually coherent, and fully animatable 3D assets ready for downstream applications.
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