# Towards Single 2D Image-Level Self-Supervision for 3D Human Pose and Shape Estimation
## 5 Conclusions  
In this paper, we present a self-supervised learning framework for recovering human 3D meshes from a single RGB image. We proposed the use of the combination of three image manipulation task losses and one neural rendering loss to enrich the feature space and boost the mesh reconstruction accuracy. We also find that the combination of RGB rendering, mask rendering, and adversarial losses is essential to properly achieve image-level self-supervision without using video or multi-view priors. Experiments were conducted on three popular benchmarks, and our algorithm achieved the best accuracy among competitive self/weakly/semi and fully-supervised algorithms. Via these results, we could conclude that single 2D image-level supervision could be a strong supervision method for training 3D human pose and shape estimation. While we showed promising results using the proposed 2D single image-level self-supervision losses, avoiding the effort of collecting other types of supervision signals, some small inconvenience remains in our framework in terms of manually setting hyper-parameters. Future work should explore the possibilities of developing a fully automatic pipeline that resolves this inconvenience by including schemes to select the optimal hyper-parameters for our losses (e.g., tile number in jigsaw puzzle loss, rotation angles for rotation prediction loss and etc.). Conceptualization, J.C., W.-H.P. and S.B. ; Methodology, J.C. and M.S. ; Software, J.C. and M.S. ; Validation, J.C., M.S. and S.B. ; Formal Analysis, C.L., S.L. ([PERSON]) and S.L. ([PERSON]); Writing--Original Draft Preparation, M.S., W.-H.P. and S.B. ; Writing--Review and Editing, M.S., W.-H.P. and S.B. ; Visualization, C.L., S.L. ([PERSON]) and D.K. ; Supervision, W.-H.P. and S.B. ; Project Administration, S.B. ; Funding Acquisition, S.B. All authors have read and agreed to the published version of the manuscript. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**Human3.6M**} & \multicolumn{2}{c}{**3 DPW**} & \multicolumn{2}{c}{**LSP**} \\ \cline{2-7}  & **MPJFE (\(\downarrow\))** & **PA-MPJFE (\(\downarrow\))** & **MPJFE (\(\downarrow\))** & **PA-MPJFE (\(\downarrow\))** & **Acc. (\(\uparrow\))** & **Acc. (\(\uparrow\))** \\ \hline _Ours (self-sup) - \(L_{\text{tot}}\) - \(L_{\text{app}}\)_ & **90.1** & **58.5** & **126.7** & **89.5** & 92.80 & **88.15** \\ _Ours (self-sup) - \(L_{\text{jogue}}\)_ - \(L_{\text{imp}}\)_ & 93.1 & 60.0 & 137.5 & 98.4 & **92.86** & 88.06 \\ _Ours (self-sup) - \(L_{\text{tot}}\) - \(L_{\text{jgsaw}}\)_ & 91.2 & 58.2 & 135.9 & 98.7 & 92.76 & 87.70 \\ \hline _Ours (self-sup)_ & 84.2 & 54.4 & 124.7 & 88. & 92.62 & 87.72 \\ \hline _Ours (weakly-sup) - \(L_{\text{tot}}\) - \(L_{\text{imp}}\)_ & **83.7** & **53.0** & 112.1 & 69.3 & 93.39 & 89.78 \\ _Ours (weakly-sup) - \(L_{\text{app}}\)_ - \(L_{\text{imp}}\)_ & **86.3** & **54.7** & 110.5 & **68.2** & 93.37 & 89.76 \\ _Ours (weakly-sup) - \(L_{\text{net}}\)_ - \(L_{\text{regular}}\)_ & 84.1 & 53.9 & **109.6** & 68.2 & **93.45** & **89.88** \\ \hline _Ours (weakly-sup)_ & 80.7 & 52.7 & 107.4 & 69.1 & 93.31 & 89.86 \\ \hline _Ours (semi-sup) - \(L_{\text{tot}}\) - \(L_{\text{imp}}\)_ & 67.7 & 46.0 & **105.8** & **64.9** & 93.18 & 89.84 \\ _Ours (semi-sup) - \(L_{\text{igsaw}}\)_ - \(L_{\text{imp}}\)_ & 68.4 & **45.9** & 108.0 & 65.7 & **93.23** & 89.79 \\ _Ours (semi-sup) - \(L_{\text{tot}}\)_ - \(L_{\text{jgsaw}}\)_ & **67.6** & 46.5 & 106.9 & 65.0 & 93.22 & **89.88** \\ \hline _Ours (semi-sup)_ & 65.8 & 44.9 & 106.0 & 66.0 & 93.28 & 89.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Ablation study on Human3.6M, 3 DPW, and LSP to analyze the effectiveness of image manipulation task losses. **Funding:** This research was supported by a grant from the R&D Program of the Korea Railroad Research Institute, Republic of Korea. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable.