Creating high-quality articulated 3D models of animals is challenging either
via manual creation or using 3D scanning tools. Therefore, techniques to
reconstruct articulated 3D objects from 2D images are crucial and highly
useful. In this work, we propose a practical problem setting to estimate 3D
pose and shape of animals given only a few (10-30) in-the-wild images of a
particular animal species (say, horse). Contrary to existing works that rely on
pre-defined template shapes, we do not assume any form of 2D or 3D ground-truth
annotations, nor do we leverage any multi-view or temporal information.
Moreover, each input image ensemble can contain animal instances with varying
poses, backgrounds, illuminations, and textures. Our key insight is that 3D
parts have much simpler shape compared to the overall animal and that they are
robust w.r.t. animal pose articulations. Following these insights, we propose
LASSIE, a novel optimization framework which discovers 3D parts in a
self-supervised manner with minimal user intervention. A key driving force
behind LASSIE is the enforcing of 2D-3D part consistency using self-supervisory
deep features. Experiments on Pascal-Part and self-collected in-the-wild animal
datasets demonstrate considerably better 3D reconstructions as well as both 2D
and 3D part discovery compared to prior arts. Project page:
chhankyao.github.io/lassie/

本工作提出了一个实际问题设置来估计动物（如马）的 3D 姿态和形状，该问题仅提供了几张（10-30）野外图像作为输入。利用 LASSIE 优化框架，发现 3D 部件并利用自我监督的深度特征强制实施 2D-3D 部件一致性，实验结果表明相比于过去的方法，本文提出的方法在 3D 重建和 3D 部分发现方面表现更好。