Obtaining labelled data to train deep learning methods for estimating animal
pose is challenging. Recently, synthetic data has been widely used for pose
estimation tasks, but most methods still rely on supervised learning paradigms
utilising synthetic images and labels. Can training be fully unsupervised? Is a
tiny synthetic dataset sufficient? What are the minimum assumptions that we
could make for estimating animal pose? Our proposal addresses these questions
through a simple yet effective self-supervised method that only assumes the
availability of unlabelled images and a small set of synthetic 2D poses. We
completely remove the need for any 3D or 2D pose annotations (or complex 3D
animal models), and surprisingly our approach can still learn accurate 3D and
2D poses simultaneously. We train our method with unlabelled images of horses
mainly collected for YouTube videos and a prior consisting of 2D synthetic
poses. The latter is three times smaller than the number of images needed for
training. We test our method on a challenging set of horse images and evaluate
the predicted 3D and 2D poses. We demonstrate that it is possible to learn
accurate animal poses even with as few assumptions as unlabelled images and a
small set of 2D poses generated from synthetic data. Given the minimum
requirements and the abundance of unlabelled data, our method could be easily
deployed to different animals.

通过仅使用未标记的图像和一小组合成 2D 姿势，提出了一种简单而有效的自我监督方法，可以同时学习准确的 3D 和 2D 动物姿势，从而消除了对 3D 或 2D 姿势注释（或复杂的 3D 动物模型）的需求，为动物姿势的估计提供了仅需未标记图像和一小组合成数据的最低要求。

无标签马匹姿态估计：来自未标记图像和合成先验的自监督方法

A Horse with no Labels: Self-Supervised Horse Pose Estimation from  Unlabelled Images and Synthetic Prior

Automated capture of animal pose is transforming how we study neuroscience
and social behavior. Movements carry important social cues, but current methods
are not able to robustly estimate pose and shape of animals, particularly for
social animals such as birds, which are often occluded by each other and
objects in the environment. To address this problem, we first introduce a model
and multi-view optimization approach, which we use to capture the unique shape
and pose space displayed by live birds. We then introduce a pipeline and
experiments for keypoint, mask, pose, and shape regression that recovers
accurate avian postures from single views. Finally, we provide extensive
multi-view keypoint and mask annotations collected from a group of 15 social
birds housed together in an outdoor aviary. The project website with videos,
results, code, mesh model, and the Penn Aviary Dataset can be found at
this https URL

自动捕捉动物姿态正在改变我们对于神经科学和社交行为的学习，本文提出了一种模型和多视角优化方法，用于捕捉鸟类的独特形状和姿态，并引入了一个管道和实验，实现了从单个视角恢复精确的鸟类姿态的关键点、掩码、姿势和形状回归。