CORGI: 从野外单张图像进行一致性感知的3D狗重建

中文摘要 AI 辅助

从单张野外图像重建高保真3D高度关节动物（如狗）模型仍然是一个艰巨的挑战。在本文中，我们介绍了CORGI，一个用于从单张无约束图像进行一致性感知的3D狗重建的新颖框架，完全消除了对3D监督的需求。为了克服生成不一致性和缺乏多视角捕捉的问题，我们的流程引入了三个核心组件。首先，我们提出了一种规范驱动轨道生成（CDOG）策略，利用专门的规范LoRA和轨道LoRA来标准化任意输入姿态并合成可靠的360度视频观察。其次，我们设计了一个一致性感知的可变形3DGS（CA-3DGS）模块，该模块锚定在D-SMAL先验上，通过专门的神经变形场显式建模每视角生成误差，以学习准确的顶点级位移。最后，为了消除结构畸变并恢复高频细节，我们引入了一个自监督的变形条件生成修复（DCGR）模块。大量实验表明，CORGI实现了最先进的性能，能够无缝泛化到不同犬种，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。

英文摘要

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.

发表机构

• University of Science and Technology of China（中国科学技术大学）

机构由 AI 辅助整理，请以论文原文为准。