# Real-Time Interaction for 3D Pixel Human in Virtual Environment
## 5 Experiments and Results
### Comparative Experiment  
In our comparative experiments, we compared our method, which is shown in Figure 8(a1,a2), to three baseline methods. The first baseline method is a traditional 2D video conference, as shown in Figure 8(b1,b2). Because a typical 2D video conference containing background information is too obtrusive in a virtual environment and could affect user experiences, we used the RVM [26] to remove the background and used human-only live video streaming in the 2D video conference. The second baseline method is an artificial avatar, as shown in Figure 8(c1,c2). We used MediaPipe's 3D pose landmark to enable the artificial avatar to follow human movements in real time through inverse kinematics. The third baseline method was a skeleton avatar, as shown in Figure 8(d1,d2). This approach adds a 3D arm skeleton generated from MediaPipe's 3D pose landmarks to the 2D video conference baseline. The joints are represented as spheres and the parts connecting joints are represented as rods. When conducting our experiments, our method used \(\frac{1}{4}\) of the original video resolution (\(640\times 480\)), resulting in an avatar resolution of \(160\times 120\). All participants performed the experiments using PC as the user interface, which adapted a monocular webcam as the video stream input. The comparative experiments involved interacting with a virtual object in a virtual meeting room from the first-person and third-person perspectives. The first-person view is how users see their arms in scenarios interacting with virtual objects. The third-person perspective is how a user appears to other participants in a virtual interaction scenario. The interactive virtual objects included a cup and laptop that could be raised by avatar hands, and a toy duck that could be moved and rotated by avatar hands. Our subjects performed virtual object interaction experiments in the first-person and third-person perspectives for all four methods, as shown in Figure 9, and scored the four methods from one to five points based on three questions, with one point representing the worst experience and five points representing the best experience. * Can the avatar reflect the shape and texture of the arms of a real person? * How was the interactive experience with the virtual objects? * How realistic was the meeting? Was there a feeling of coexisting in the same space with other participants? Q1 evaluates whether the avatar's arms are realistic and Q2 evaluates which avatar is the most convenient and natural for object interaction. Q3 is devoted to evaluating the performance of different avatars in terms of replicating meeting scenes in the real world. The user survey results for the three question are presented in Figure 10.  
Figure 8: Different methods in the comparative experiments. (**a1,a2**) Pixel avatar. (**b1,b2**) 2D video conference. (**c1,c2**) Artificial avatar. (**d1,d2**) Skeleton avatar. The first row is the third-person view, and the second row is the first-person view. Figure 10: Results of the comparative experiments. The error bars in the chart represent standard deviations (SDs). Figure 9: Different methods interact with virtual objects in the third-person perspective. (**a**) Pixel avatar. (**b**) 2D video conference. (**c**) Artificial avatar. (**d**) Skeleton avatar. For Q1, the mean value of our method (mean = 4.47, SD = 0.71) is higher than that of the 2D video conference (mean = 3.00, SD = 0.82), artificial avatar (mean = 2.00, SD = 0.82), and skeleton avatar (mean = 3.06, SD = 0.98). Through t-test analysis, we can conclude that our method significantly outperforms the 2D video conference (t(30) = 5.40, \(p<0.05\)), artificial avatar (t(30) = 9.08, \(p<0.05\)), and skeleton avatar (t(30) = 4.63, \(p<0.05\)), meaning it can better reflect user clothing and arms. The artificial avatar uses a virtual avatar that is entirely unrelated to the user. Therefore, it cannot reflect the shape and texture of the user's arms, and its score is the lowest.