# CFAM: Estimating 3D Hand Poses from a Single RGB Image with Attention
## 4 Experiments
### Estimating 3D Hand Poses from a Single RGB Image  
When estimating the 3D hand pose based on the GT 2D score map, it was found that our method was superior to [PERSON]'s framework. To prove that our method was feasible throughout the process, we estimated 3D keypoints from a single RGB image, and we verified the results via the original RGB image that needed to be cropped by _HandSegNet_. The GT-cropped RGB image is the ground truth cropped RGB image, and the RGB image is the image without cropping, which needs _HandSegNet_ to crop the hand image. The method called \"Ours\" is the method that used CPMAtt to estimate the 2D keypoints and CFAM to estimate the 3D keypoints. Due to the lack of depth information, estimating a 3D hand pose from a single RGB image is challenging. The hand side information was used for the processing step. The picture will slip if the hand side changes. The right graph of Figure 5 shows the result using the STB dataset from GT-cropped images, while the left graph of Figure 6 shows the result using the RHD dataset. Tables 9-12 show the results using both datasets and the details for each finger. Our method obtained a better result on the GT-cropped RGB image. We also tested it on RGB images without GT cropping. This kind of image was cropped by _HandSegNet_ first, which may have caused a bigger error because of the error in the segmentation strategy. Under all conditions, our method attained better results; therefore, the method was robust and useful for solving this kind of task. Figure 5: AUC of 3D hand pose estimation on STB dataset from the GT 2D score maps (**left**) and the GT-cropped RGB image (**right**). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Evaluation Index** & **Methods** & **Wrist** & **Thumb** & **Index** & **Middle** & **Ring** & **Little** \\ \hline \multirow{2}{*}{Mean Error (mm)} & [PERSON] [16] & 0 & **8.74** & 10.04 & 8.93 & 8.35 & 8.42 \\  & CFAM & 0 & 9.02 & **9.1** & **8.68** & **7.76** & **7.79** \\ \hline \multirow{2}{*}{Media Error (mm)} & [PERSON] [16] & 0 & **7.72** & 8.79 & 7.57 & 6.99 & 7.24 \\  & CFAM & 0 & 7.85 & **8.45** & **7.76** & **6.71** & **6.31** \\ \hline \multirow{2}{*}{AUC (0–50 mm)} & [PERSON] [16] & 0.97 & **0.83** & 0.8 & 0.82 & 0.83 & 0.83 \\  & CFAM & 0.97 & 0.82 & **0.82** & **0.83** & **0.84** & **0.84** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: 3D hand pose estimation on the STB dataset from the GT 2D score map and the GT-cropped RGB image for each finger. The best results are highlighted in bold. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Datasets** & **RHD** & \multicolumn{2}{c}{**STB**} \\ \hline
**Method** & **[PERSON] [16]** & **Ours** & **[PERSON] [16]** & **Ours** \\ \hline AUC (0–50 mm) & 0.48 & **0.561** & 0.823 & **0.824** \\ Median Error (mm) & 24.47 & **19.61** & **7.58** & 7.78 \\ Mean Error (mm) & 30.36 & **24.6** & 8.8 & **8.75** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Estimating the 3D hand pose from the GT-cropped RGB image. The best results are highlighted in bold. Images cropped using _HandSegNet_ had a certain degree of error; we experimented with the images cropped by _HandSegNet_ to prove that our CFAM was not sensitive to these errors. As the right graph of Figure 6 and Tables 13 and 14 show, our method was still better than [PERSON]'s framework, which proved that our CFAM can be used in end-to-end 3D hand pose estimation, and our method was superior to the original method in all modules. Many hand pose estimation methods are based on segmented hand images, indicating that they are sensitive to errors in the segmentation process. Our method can more accurately estimate hand poses in the presence of segmentation errors than other methods, and it can be used in tracking and undivided hand images.