# Animal Pose Estimation Based on 3D Priors
## 4 Proposed Method
### Pose Refinement
#### 4.3.1 Optimization-Based Pose Refinement
The results of different methods on Amur are shown in Table 4 and Figure 7. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **(0, 45]** & **(45, 65]** & **(65, 100]** \\ \hline Hourglass [43] & 34.1 & 56.4 & 84.0 \\ Ours & **34.4** & **57.1** & **84.1** \\ \hline ResNet [47] & 35.0 & 56.8 & 84.3 \\ Ours & **36.8** & **57.4** & **84.3** \\ \hline HRNet [25] & 35.9 & 57.0 & 84.7 \\ Ours & **39.2** & **57.0** & **84.8** \\ \hline CC-SSL [15] & 32.1 & 55.4 & 75.4 \\ Ours & **35.1** & **55.9** & **76.0** \\ \hline UDA [16] & 34.4 & 55.2 & 76.5 \\ Ours & **38.8** & **56.1** & **77.2** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: [EMAIL_ADDRESS] accuracy on Amur (%). The best results are marked in bold. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **(0, 45]** & **(45, 65]** & **(65, 100]** \\ \hline Hourglass [43] & 28.3 & 55.7 & 87.6 \\ Ours & **29.4** & **57.0** & **87.6** \\ \hline ResNet [47] & 29.3 & 54.3 & 88.1 \\ Ours & **29.4** & **54.8** & **88.1** \\ \hline HRNet [25] & 29.5 & 55.4 & 87.8 \\ Ours & **30.5** & **56.1** & **87.8** \\ \hline CC-SSL [15] & 28.8 & 54.7 & 73.6 \\ Ours & **32.6** & **55.3** & **73.6** \\ \hline UDA [16] & 30.0 & 54.7 & 75.0 \\ Ours & **34.7** & **56.4** & **75.0** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: [EMAIL_ADDRESS] accuracy on TD-Tiger (%). The best results are marked in bold. ## 6 Discussions and Conclusions

### Discussions

As shown in Figure 7, the proposed method could effectively improve the animal pose estimation accuracy for poor-quality images due to occlusion, complex background, and uncontrollable illumination. In addition, we show two samples in which the animals were subject to the initial pose or the pose dictionary. In the first sample (Figure 8a), the initial poses completely violated the reasonable distribution of 2D poses, leading to irreparable errors. The second sample (Figure 8b) was not only limited by the initial pose but also by the pose dictionary. The training data for 3D pose dictionary learning were synthetic. If more real data can be used for training, a pose dictionary that is more robust to noise can be obtained. We will consider this in future work. Moreover, the dictionary encoded prior knowledge for specific classes of animals. It can be generalized between animal species with similar shapes, e.g., cats and tigers, but not between arbitrary animals. Therefore, obtaining a more diverse dictionary might be a potential way to further improve the estimation performance. Figure 7: Qualitative Results: (**a**) underexposed; (**b**) motion blur; (**c**) complex background (similar to tiger body texture); (**d**) overexposure. The red point, green point, and blue point represent the ground truth, the initially estimated pose, and the refined pose, respectively. The number represents the index of the joint [24]. Both the red and white dashed circles indicate significant refinement. ### Conclusions

In this paper, we presented a method to estimate 2D animal poses with 3D constraints. The 3D constraints were constructed from synthetic 3D poses and encoded in the 3D pose dictionary using sparse dictionary learning. Extensive experiments were conducted to evaluate the proposed method. Experimental results showed that the constraints provided by the 3D pose dictionary produced more reasonable animal pose estimation. In addition, we constructed two datasets: Cat for 3D pose dictionary learning and Amur for algorithm evaluation. The findings revealed in this paper can promote the further development of animal pose estimation. Conceptualization, X.D. and Q.Z. ; methodology, X.D. and Q.Z. ; validation, X.D. ; writing--original draft preparation, X.D. ; writing--review and editing, X.D., S.L. and Q.Z. ; supervision, Q.Z. and H.Y. ; funding acquisition, Q.Z. All authors have read and agreed to the published version of the manuscript. This work is supported by the National Natural Science Foundation of China (Grant No. 62176170), and the Science and Technology Department of Tibet (Grant No.