# Animal Pose Estimation Based on 3D Priors
## 4 Proposed Method
### Pose Refinement
#### 4.3.1 Optimization-Based Pose Refinement
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Method** & **Ear** & **Nose** & **Shoulder** & \
\begin{tabular}{c} **Front** \\ **Paw** \\ \end{tabular} & **Hip** & **Knee** &
\begin{tabular}{c} **Back** \\ **Paw** \\ \end{tabular} & **Tail** & **Center** & **Mean** \\ \hline GT+\(\mathcal{N}(0,3)\) & 75.9 & 75.2 & 75.5 & 75.9 & 75.0 & 74.4 & 74.1 & 75.6 & 75.8 & 75.3 \\ Ours (Optimization) & **75.6** & **77.4** & **78.3** & **76.5** & **78.3** & **76.9** & **75.1** & **80.7** & **81.1** & **77.2** \\ Ours (Fully supervised) & 85.7 & 86.2 & 89.4 & 83.5 & 86.9 & 87.9 & 87.3 & 86.2 & 95.1 & 87.3 \\ Ours (Self-supervised) & 67.2 & 65.0 & 77.7 & 69.2 & 71.9 & 76.3 & 69.4 & 71.3 & 38.7 & 67.3 \\ \hline GT+\(\mathcal{N}(0,4)\) & 55.0 & 54.1 & 53.8 & 53.5 & 55.2 & 54.5 & 54.0 & 54.8 & 53.0 & 54.2 \\ Ours (Optimization) & **55.2** & **57.4** & **61.4** & **54.3** & **60.3** & **59.9** & **56.0** & **59.8** & **64.5** & **58.0** \\ Ours (Fully supervised) & 73.8 & 71.7 & 83.0 & 71.0 & 79.0 & 78.9 & 77.3 & 73.3 & 90.6 & 77.3 \\ Ours (Self-supervised) & 49.0 & 52.8 & 61.2 & 50.4 & 61.5 & 58.3 & 49.9 & 55.2 & 36.7 & 51.9 \\ \hline GT+\(\mathcal{N}(0,5)\) & 40.0 & 38.5 & 38.7 & 40.6 & 39.3 & 39.3 & 39.7 & 40.3 & 39.7 & 39.7 \\ Ours (Optimization) & **41.0** & **42.8** & **46.0** & **40.6** & **48.3** & **44.8** & **41.3** & **45.5** & **54.3** & **44.2** \\ Ours (Fully supervised) & 62.0 & 60.8 & 75.1 & 59.5 & 69.7 & 69.0 & 67.4 & 63.7 & 82.8 & 67.3 \\ Ours (Self-supervised) & 37.0 & 38.9 & 48.3 & 37.4 & 49.0 & 40.9 & 36.4 & 43.0 & 34.4 & 39.7 \\ \hline GT+\(\mathcal{N}(0,6)\) & 29.2 & 29.1 & 29.5 & 29.0 & 29.5 & 30.3 & 29.0 & 28.5 & 30.5 & 29.4 \\ Ours (Optimization) & **29.8** & **32.5** & **38.5** & **30.2** & **40.3** & **35.7** & **30.3** & **34.8** & **43.6** & **34.2** \\ Ours (Fully supervised) & 51.0 & 50.0 & 67.6 & 50.8 & 61.3 & 58.4 & 58.0 & 54.1 & 74.2 & 57.8 \\ Ours (Self-supervised) & 26.8 & 31.3 & 36.7 & 27.3 & 41.5 & 31.5 & 25.8 & 31.0 & 27.8 & 30.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: [EMAIL_ADDRESS] accuracy on SA-Tiger with different levels of Gaussian noise added to the ground truth(GT) pose (%). The best results are marked in bold. in TD-Tiger were split into training and verification sets, with 6523 images for training and 1765 images for verification. The [EMAIL_ADDRESS] accuracies obtained using the proposed method and different initial pose estimation methods on TD-Tiger are shown in Table 3. As can be seen from the results, while the proposed method maintained comparable accuracy for high-quality images, it obviously improved the accuracy for poor-quality images (i.e., in \((0,45]\)). It should be noted that the initial pose for high-quality images was already fairly accurate (i.e., in \((65,100]\)), and refinement may not be necessary. The success of the proposed method proves that the deviated key points can be effectively pulled back toward their true positions by exploiting 3D prior constraints on the animal poses. ### Results on the Amur Dataset

HRNet [25] and ResNet [47] trained for tiger pose estimation have been provided by MMPose [58]. Following the MMPose [58], we trained the Hourglass [43] using 2193 images from the training set in the ATRW [24]. For a fair comparison, we retrained CC-SSL [15] and UDA [16]. The training strategy was as follows: The source domain was the Synthetic Animal [15], which was the same as CC-SSL [15] and UDA [16]; the target domain training data were the training set in the ATRW [24]. As can be seen, with the proposed method, all the initial models had improvements in performance or were comparable to initial results. For example, UDA [16] achieved a 4.4% improvement in the interval \((0,45]\); Meanwhile, in the interval \((65,100]\), the proposed method was also comparable. On the whole, the proposed method had noticeably superior performance in challenging samples. This justifies the effectiveness of the proposed 3D constraints for 2D tiger pose estimation, particularly in challenging cases.