首个支持多动物3D重建的可提示框架，解决野外复杂场景难题。
SAM 3D Animal: Promptable Animal 3D Reconstruction from Images in the Wild
- 基于SMAL+模型，通过关键点与掩码提示实现多动物联合重建。
- 在5000+张图像数据集上训练，显著提升遮挡和密集场景下的重建精度。
- 适合需要灵活提示的野生动物3D建模、动画生成等应用。
野外环境中的3D动物重建因物种差异大、频繁遮挡及多动物共现而极具挑战，现有方法多聚焦单动物场景。本文提出SAM 3D Animal，首个支持单图多动物3D重建的可提示框架。基于SMAL+参数化动物模型，该方法联合重建多个实例，并支持关键点与掩码形式的灵活提示，有效提升复杂遮挡场景下的判别能力。为训练该模型，我们构建了Herd3D数据集，包含超过5000张图像，涵盖多样物种、交互行为与遮挡模式。在Animal3D、APTv2和Animal Kingdom数据集上的实验表明，该框架在基于模型与无模型方法中均达到当前最优性能，展现出可扩展且高效的提示驱动动物3D重建能力。
原文摘要 · Abstract (English)
3D animal reconstruction in the wild remains challenging due to large species variation, frequent occlusions, and the prevalence of multi-animal scenes, while existing methods predominantly focus on single-animal settings. We present SAM 3D Animal, the first promptable framework for multi-animal 3D reconstruction from a single image. Built on the SMAL+ parametric animal model, our method jointly reconstructs multiple instances and supports flexible prompts in the form of keypoints and masks which enable more reliable disambiguation in crowded and occluded scenes. To train such a model, we further introduce Herd3D, a multi-animal 3D dataset containing over 5K images, designed to increase diversity in species, interactions, and occlusion patterns. Experiments on the Animal3D, APTv2, and Animal Kingdom datasets show that our framework achieves state-of-the-art results over both existing model-based and model-free methods, demonstrating a scalable and effective solution for prompt-driven animal 3D reconstruction in the wild.
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