SMAL-pets: SMAL Based Avatars of Pets from Single Image
Piotr Borycki, Joanna Waczy\'nska, Yizhe Zhu, Yongqiang Gao, Przemys{\l}aw Spurek

TL;DR
SMAL-pets is a novel framework that creates high-quality, editable 3D animal avatars from a single image, combining 3D Gaussian Splatting with the SMAL model to enable realistic, customizable, and animatable pet representations.
Contribution
The paper introduces a hybrid architecture integrating 3D Gaussian Splatting with SMAL for single-image animal avatar reconstruction and a multimodal editing suite for natural language-based customization.
Findings
Produces high-fidelity, editable animal avatars from a single image.
Enables complex animations through natural language prompts.
Bridges the gap between reconstruction and generative modeling.
Abstract
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…
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Taxonomy
Topics3D Shape Modeling and Analysis · Human Motion and Animation · Generative Adversarial Networks and Image Synthesis
