User:AlfieCarlton

The latest figures, he says, suggest the real figure has been nearer $17bn. Figure 10 shows the testing results on two scenes of the Human3D dataset: smoking S9 and photo S9. The results of each method are displayed in row-wise. Specifically, the shadows of the legs and the right hand are differently rendered due to the erroneous pose estimated using the method in Pavllo et al. Admittedly, for some pose scenes, e.g., Phone, Eat, our method does not achieve the best performance. Each column indicates a different pose scene, e.g., walking, eating, etc. We highlight the best and second best results in each column in bold and underline formats respectively. Furthermore, in the second part of Table 6, we show the results with ground-truth (GT) 2D input. For both datasets, we use the standard evaluation metrics MPJPE and P-MPJPE to measure the offset between the estimation result and ground-truth (GT) relative to the root node in millimeters (Ionescu et al. To further validate the accuracy, we trace these individual joints across frames in the corresponding video sequence and measure their MPJPE in the temporal space. Note that our approach, indicated by the green bar, achieves minimum MPJPE among all the other methods in most of the joints.

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