# Towards Single 2D Image-Level Self-Supervision for 3D Human Pose and Shape Estimation
## 4 Experiments
### Results  
For the Human3.6M dataset, we compare our method to recent fully/semi/weakly-/self-supervised methods [5; 36; 37; 43; 47; 48; 49; 50; 64] that output the 3D human poses by protocol-2 in Table 6. Our method outperformed previous methods in all three types of supervision settings (i.e., self/weakly/semi-supervised). [PERSON] et al. [64]'s method is the competitive self-supervised learning framework using the video priors; however, our method outperformed it even without any video priors. [PERSON] et al. [54]'s method is the only baseline that outperforms our method and uses the self-supervised setting. However, the work is not directly comparable to ours, as its setting is easier as it estimates only skeletal joints and using multi-view priors. We provide full 3D meshes inferring both pose and shape and use only 2D images as the supervision signals. To show the generalization ability of our method, we evaluated it on 3 DPW, the data of which were never seen in our training process. For the 3 DPW dataset, we compared the accuracy of our method to those of recent state-of-the-art approaches [4; 5; 9; 47; 51; 64] in Table 7. For the LSP dataset, as the dataset does not contain 3D pose annotations, we only measured the accuracy and F1 score of FG-BG segmentation and six-part segmentation, respectively, for comparison with other algorithms. We compared our method to state-of-the-art optimized-based methods [9; 37; 65] and regression-based methods [5; 49; 50; 64] in Table 8. Optimization-based methods tend to have better performance on segmentation tasks than regression-based methods; however, our method outperformed most of them even in the self-supervised setting (i.e., _Ours (self-sup)_); our approach was also recorded as the best when involving more supervisions (i.e., _Ours (weakly-sup)_ and _Ours (semi-sup)_). In Figure 3, we visualized qualitative examples of our method on Human3.6M, 3 DPW, and LSP datasets. We overlaid predicted meshes, joints, part-segmentation masks, and neural rendered images on the input images. Even though our purpose was not to estimate textures at the testing stage; we could observe that our method faithfully estimates textures for unseen testing images. \begin{table}
\begin{tabular}{l c} \hline \hline
**Methods** & **PA-MPJPE(\(\downarrow\))** \\ \hline \hline
**Fully-supervised \& Semi-supervised** \\ \hline \hline [PERSON] et al. [43] & 93.9 \\ [PERSON] et al. [37] & 75.9 \\ [PERSON] et al. [36] & 59.9 \\ HMR [5] & 56.8 \\ Temporal-HMR [47] & 56.9 \\ [PERSON] et al. [48] & 54.3 \\ [PERSON] et al. [49] & 50.1 \\ TexturePose [50] & 49.7 \\ [PERSON] et al. [64] & 48.1 \\ _Ours (semi-sup)_ & **44.9** \\ \hline \hline
**Weakly-supervised** & \\ \hline HMR unpaired [5] & 66.5 \\ [PERSON] et al. [64] & 58.2 \\ [PERSON] et al. [52] & 54.5 \\ _Ours (weakly-sup)_ & **52.7** \\ \hline \hline
**Self-supervised** & \\ \hline \hline [PERSON] et al. [64] & 90.5 \\ * [PERSON] et al. [66] (_multi-view-sup_) & 77.0 \\ [PERSON] et al. [64] (_multi-view-sup_) & 74.1 \\ _Ours (self-sup)_ & **54.4** \\ \hline * [PERSON] et al. [54] (_multi-view-sup_) & 45.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation with Human3.6M(Protocol-2). Methods in the first 10 rows use equivalent 2D and 3D pose supervision. Methods in rows 11-14 and rows 15-18 are weakly-supervised and self-supervised approaches, respectively. * indicates methods that output only 3D joints, not 3D meshes.