# Upper Body Pose Estimation Using Deep Learning for a Virtual Reality Avatar
## 7 Discussion  
Our study investigates the effectiveness of a deep learning method to render the mimicked upper body avatar in VR that follows the motion of a user in the physical world. The proposed learning-based method predicts the location of the joints of the upper body, including the neck, pelvis, elbows, and shoulders, from the data of the HMD and two controllers. Our pose learning method produces reliable inference of the upper body joints, validated for test data in Section 3.4. Our method predicts the avatar's upper body given a VR user's HMD and controller data, as in Section 3.5. We compare our pose regression results with the IK results in Section 4 regarding visual accuracy and performance. Our resulting avatar looks visually similar to the avatar made from an IK method for the motion test that a user blocks, sways, and lifts. This visual resemblance tells us that our method may be a substitute for a widely used IK method in visualizing an upper body self-avatar of a VR user. Section 4 also compares our and the IK methods' performance evaluation. The computation time of our method is shorter than an IK method by approximately 60%. VR applications require 60-80 frames per second and should \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Question**} & \multicolumn{2}{c}{**Group (Mean)**} & \multicolumn{2}{c}{**Ours-IK**} \\ \cline{2-5}  & **Ours** & **IK** & **t** & \(p\)** \\ \hline Q8 & 4.66 & 4.26 & 3.055 & 0.008 \\ Q9 & 4.73 & 4.26 & 3.500 & 0.003 \\ Q10 & 4.53 & 3.93 & 3.674 & 0.002 \\ Q11 & 4.73 & 4.13 & 2.304 & 0.002 \\ Q12 & 4.66 & 4.26 & 2.449 & 0.028 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Statistical analysis of the questionnaire after the ball-grabbing game. Figure 14: Average of total balls grabbed by the users in percentage in the ball-grabbing game. Our proposed method and IK. guarantee the rendering time of each frame under a specific time limit to prevent possible motion sickness. Therefore, our low-latency method is desirable to avoid the potential decrease in the feeling of presence due to break-in-presence. We conducted VR user experiments on single-user and multi-user VR applications in Section 5 and analyzed the user experiments' results in Section 6. We paid attention to reducing bias by randomly allocating the experimental units across the participant groups. These evaluations show that our result significantly differs from an IK result in most aspects, including presence, embodiment, manipulation, accuracy, delay, and naturalness. The sense of moving is the only factor whose difference is insignificant. Statistical analysis of these user evaluations reports that our hypotheses are accepted. We hypothesized in H1 and H2 that our method could be similar to an IK method but better. Our testing verifies that our method generates a natural avatar and yields self-embodiment similar to and even better than an IK method. In addition, H3 testing confirms that our lower latency enhances presence with less break-in presence. Our user evaluation supports that our method significantly differs from the IK method. Surprisingly, our user evaluation confirms that the learning-based upper body is better than the IK upper body. The IK upper body is a typical method to visualize an avatar in VR corresponding to the pose of a user due to its ease of use and naturalness. Comparable to an IK, we tested our learning method for an upper body expecting that both ways produced similar results. The resulting statistical analysis of user evaluation shows that our learning method is better in most factors than an IK method. We guess that the lower latency of our method, as shown in Section 4, plays a crucial benefit because it minimizes the possible break-in presence and embodiment during VR experiences. In our multi-user VR experiments, the users are close to each other in the same building; therefore, they share the same network server. In practice, users communicate over the network worldwide during VR experiences, increasing latency.