基于SMAL的宠物单图像生成高保真可编辑3D宠物化身
SMAL-pets: SMAL Based Avatars of Pets from Single Image
AI总结：
本文提出SMAL-pets框架，通过混合架构结合3D高斯溅射与SMAL参数模型，实现单图像生成高质量可编辑宠物3D化身，支持通过文本提示进行外观和行为编辑。
摘要：
本文提出SMAL-pets框架，通过混合架构结合3D高斯溅射与SMAL参数模型，实现单图像生成高质量可编辑宠物3D化身，支持通过文本提示进行外观和行为编辑。
英文摘要：
Creating high-fidelity, animatable 3D dog avatars remains a formidable challenge in computer vision. Unlike human digital doubles, animal reconstruction faces a critical shortage of large-scale, annotated datasets for specialized applications. Furthermore, the immense morphological diversity across species, breeds, and crosses, which varies significantly in size, proportions, and features, complicates the generalization of existing models. Current reconstruction methods often struggle to capture realistic fur textures. Additionally, ensuring these avatars are fully editable and capable of performing complex, naturalistic movements typically necessitates labor-intensive manual mesh manipulation and expert rigging. This paper introduces SMAL-pets, a comprehensive framework that generates high-quality, editable animal avatars from a single input image. Our approach bridges the gap between reconstruction and generative modeling by leveraging a hybrid architecture. Our method integrates 3D Gaussian Splatting with the SMAL parametric model to provide a representation that is both visually high-fidelity and anatomically grounded. We introduce a multimodal editing suite that enables users to refine the avatar's appearance and execute complex animations through direct textual prompts. By allowing users to control both the aesthetic and behavioral aspects of the model via natural language, SMAL-pets provides a flexible, robust tool for animation and virtual reality.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
