# Towards Single 2D Image-Level Self-Supervision for 3D Human Pose and Shape Estimation
## 4 Experiments
### Ablation Study  
To analyze the effectiveness of our entire training objective (Equation (1)), we conducted ablation experiments on our losses, as presented in Table 5. In our initial experiment, we used the combination of three images' manipulation task losses (i.e., jigsaw puzzling, rotation prediction, and part in-painting) and one RGB rendering loss \(L_{\text{RGB}}\) for the \"mesh training stage\". However, given only these four types of losses, we observed that 3D meshes were trained in the wrong way, inferring camera scale parameters to be very small, as this might be an easier way to reduce the RGB rendering loss \(L_{\text{RGB}}\). To relieve this, we proposed the addition of more constraints in our loss by the segmentation mask loss \(L_{\text{mask}}\), defined in Equation (10): we observed that the uses of loss \(L_{\text{mask}}\) and the discriminator loss \(L_{\text{adv}}\) are helpful for preventing this phenomenon. In Table 5, by comparing results from \"_Ours (self-sup)-\(L_{\text{real}}\)-\(L_{\text{mask}}\)_\"_, \"Ours (self-sup)-\(L_{\text{real}}\)_\"_, and \"_Ours (self-sup)_\"_, we could conclude that results improved as more supervision was used. The corresponding qualitative results are also shown in Figure 4. Furthermore, we experimented on different configurations of datasets for training in the same table (Table 5): Rows 3-4 of Table 5 show the accuracy using fewer datasets for training where \"H36M\" and \"Full w/o Y\" denote the Human3.6M dataset and full datasets without the collection of wild YouTube data, respectively. \"Full\" denotes the full training data that we described in Section 4.1, including the collection of wild YouTube data compared to \"Full w/o Y\". We can see that involving more image data results in improved accuracy. \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**FG-BG Seg.**} & \multicolumn{2}{c}{**Part Seg.**} \\ \cline{2-5}  & **Acc. (\(\uparrow\))** & **F1 (\(\uparrow\))** & **Acc. (\(\uparrow\))** & **F1 (\(\uparrow\))** \\ \hline \multicolumn{5}{l}{**Optimization-based**} \\ \hline SMPLify _oracle_[(9)] & 92.17 & 0.88 & 88.82 & 0.67 \\ SMPLify [(9)] & 91.89 & 0.88 & 87.71 & 0.64 \\ SMPLify on [(37)] & 92.17 & 0.88 & 88.24 & 0.64 \\ Bodynet [(65)] & 92.75 & 0.84 & - & - \\ \hline \multicolumn{5}{l}{**Fully-supervised \& Semi-supervised**} \\ \hline HMR [(5)] & 91.67 & 0.87 & 87.12 & 0.60 \\ [PERSON] et al. [(49)] & 91.46 & 0.87 & 88.69 & 0.66 \\ TexturePose [(50)] & 91.82 & 0.87 & 89.00 & 0.67 \\ [PERSON] et al. [(64)] & 91.84 & 0.87 & 89.08 & 0.67 \\ Ours (_semi-sup_) & **93.28** & **0.90** & **89.90** & **0.70** \\ \hline \multicolumn{5}{l}{**Weakly-supervised**} \\ \hline HMR unpaired [(5)] & 91.30 & 0.86 & 87.00 & 0.59 \\ [PERSON] et al. [(64)] & 91.70 & 0.87 & 87.12 & 0.60 \\ Ours (_weakly-sup_) & **93.31** & **0.90** & **89.86** & **0.70** \\ \hline \multicolumn{5}{l}{**Self-supervised**} \\ \hline [PERSON] et al. [(64)] & 91.46 & 0.86 & 87.26 & **0.64** \\ Ours (_self-sup_) & **92.62** & **0.89** & **87.72** & 0.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Evaluation of FG-BG and six-part segmentation with LSP test set. First 4 rows: _optimization-based_ methods. Last 10 rows: _regression-based_ methods. Figure 4: Qualitative examples depending on losses: (**a**) input, (**b**) results from '_Ours (self-sup)-\(L_{\text{real}}\)_', \(L_{\text{mask}}\)', (**c**) results from ‘_Ours (self-sup)-\(L_{\text{real}}\)_’, and (**d**) results from ‘_Ours (self-sup)-\(\prime\)_’. Results become better involving more supervision. Figure 3: Qualitative examples of three databases (Human3.6M, 3 DPW, and LSP). (**a**) Input image; (**b–d**), (**e–g**), and (**h–j**) denote our results obtained from self/weakly and semi-supervised models, respectively. (**b**,**e**,**h**) show images with joints and 3D meshes, (**c**,**f**,**i**) show images with part-segmentation masks, and (**d**,**g**,**j**) show neural mesh rendered images, respectively.