# Animal Pose Estimation Based on 3D Priors
## 1 Introduction  
In recent years, animal pose estimation has attracted more and more attention owing to its potential applications in biology, neuroscience, ecology, agriculture, and entertainment [1; 2; 3; 4]. For instance, animal pose estimation [5; 6; 7] could be employed in markerless motion capture systems to capture the movements of wild animals. Animal pose estimation [8; 9] would also support advances in entertainment, where animal animation is still largely performed manually. In neuroscience [3; 10], tracking animals is fundamental for understanding behavior or relating motion to brain activity. In bio-inspired robotics [11; 12], understanding how animals move can help to design more efficient robots. Despite the promising applications in various fields, there are few works on animal pose estimation to date. This has much to do with the challenges posed by the following factors. Firstly, research on animal pose estimation is largely limited by data scarcity. Typical human pose estimation benchmarks, such as Human3.6M [13], capture data from 15 sensors and rely on small markers attached to the subject's body to obtain motion. However, such data acquisition methods are hard to be applied to animals because it is impractical to ask animals to cooperate during data acquisition, especially in wild conditions. Secondly, due to occlusions, complex backgrounds, and unconstrained illumination conditions in the wild, the quality of the collected images is often uncontrollable, resulting in unreliable animal pose estimation. Thirdly, the variations in animal poses are far more complicated than in humans. The high deformations of animal pose (or body) make annotating and predicting animal pose quite challenging. Most existing methods for animal pose estimation focus on addressing data scarcity by transferring knowledge from other more accessible domains such as synthetic animaldata [6; 14; 15; 16] or human data [17]. However, there are domain gaps [18; 19] between synthetic images and real images, which prevent models trained on synthetic data from generalizing well to real-world images. Furthermore, 2D poses are the projection of 3D body configurations, during which the relationship between the joints in 3D poses is distorted. Consequently, the models that are trained with 2D annotations alone may not conform to the real configurations of animal poses. This problem can become severe, especially in wild-animal images with uncontrolled quality, for which large errors in animal pose estimation would be obtained using existing methods. In general, the performance can be improved by preprocessing (such as image segmentation [20; 21], image enhancement [22], etc.) or postprocessing. However, the preprocessing of wildlife images, such as denoising or enhancement, is a non-trivial task in itself. In this paper, we propose a method for estimating 2D animal poses with 3D priors, in which 3D priors are constructed with synthetic 3D poses and encoded in a 3D pose dictionary using sparse dictionary learning. As can be seen in Figure 1, taking tigers as an example, the proposed method can estimate more accurate 2D poses in poor-quality images. A preliminary version of this work has been published in the 33 rd British Machine Vision Conference (BMVC) [23]. This paper extends the BMVC paper in the following aspects: (i) We extensively compare the proposed algorithm with more algorithms. (ii) The proposed method is also evaluated on more datasets, which demonstrates its good generalization. (iii) This paper considers more widely distributed noise for more comprehensive and detailed performance evaluation and analysis. (iv) We implement the proposed method through both traditional optimization and deep learning.