# CFAM: Estimating 3D Hand Poses from a Single RGB Image with Attention
## 4 Experiments
### 3D Hand Pose Estimation with CFAM  
To better estimate the 3D hand poses based on the previous pose, we propose the CFAM module, which includes the attention mechanism and the fusion of RGB images and 2D heat map information. To show that every step of our network design is effective, we used three strategies for comparison:  
Strategy 1: adding the channel attention mechanism. Strategy 2: adding the fusion of RGB images and 2D score maps without adding the channel attention mechanism. Strategy 3: adding the full CFAM. Table 5 and Figure 3(b) show the effect of our approach on the RHD dataset, while Table 6 shows the effect on each finger. The channel attention mechanism can have certain auxiliary effects on feature acquisition. Therefore, the result of strategy 1 was slightly better than that of [PERSON]'s method, but the effect was not significantly improved, and the AUC was increased by approximately \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Evaluation Index** & **Method** & **Wrist** & **Thumb** & **Index** & **Middle** & **Ring** & **Little** \\ \hline \multirow{4}{*}{Mean Error (pix)} & CPMAtt & 24.8 & 13.47 & 13.66 & 13.23 & 14.12 & 15.39 \\  & CPMAtt\_gt & **4.34** & **8.53** & **7.4** & **4.5** & **5.04** & **6.32** \\  & CPM (our run) & 24.62 & 17.23 & 16.29 & 14.3 & 16.04 & 19.45 \\  & CPM\_gt (our run) & 6.11 & 12.2 & 9.59 & 6.1 & 7.65 & 10.89 \\ \hline \multirow{4}{*}{Media Error (pix)} & CPMAtt & 16.12 & **3.4** & **2.87** & 3.13 & 3.25 & 3.31 \\  & CPMatt\_gt & **3.36** & 3.66 & 3.31 & **3.06** & **3.14** & **3.05** \\  & CPM (our run) & 15.97 & 5.84 & 4.67 & 4.59 & 5.59 & 5.93 \\  & CPM\_gt (our run) & 4.47 & 6.15 & 4.6 & 4.36 & 4.99 & 5.04 \\ \hline \multirow{4}{*}{AUC (0–50 pix)} & CPMAtt & 0.44 & 0.75 & 0.77 & 0.8 & 0.78 & 0.75 \\  & CPMatt\_gt & **0.86** & **0.76** & **0.79** & **0.85** & **0.83** & **0.8** \\ \cline{1-1}  & CPM (our run) & 0.44 & 0.64 & 0.67 & 0.74 & 0.7 & 0.62 \\ \cline{1-1}  & CPM\_gt (our run) & 0.8 & 0.65 & 0.71 & 0.8 & 0.75 & 0.68 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: 2D keypoint estimation results on the RHD dataset. Our method achieved the best results on different fingers. The best results are highlighted in bold. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Evaluation Index** & **CPMAtt** & **CPMAtt\_gt** & **CPM (Our Run)** & **CPM\_gt (Our Run)** \\ \hline AUC (0–30 pix) & 0.753 & **0.811** & 0.663 & 0.724 \\ Median Error (pix) & 3.81 & **3.25** & 5.83 & 5 \\ Mean Error (pix) & 14.49 & **6.26** & 17.04 & 9.14 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The mean error and AUC of the 2D keypoints estimation results on the RHD dataset. By adding the channel attention mechanism, CPMAtt was superior to a convolutional pose machine (CPM) [17] in terms of the AUC and error. Even in the _HandSegNet_ cropped picture, our experimental AUC was better than that of the CPM method on the GT-cropped picture. Regardless of whether the image being tested was segmented by the GT or _HandSegNet_, our model was an improvement. The AUC was increased by nearly 9 percentage points, and the mean error was reduced by nearly 3 pixels. The best results are highlighted in bold. one percent. Strategy 2 added RGB image-assisted training, and the improvement was significant. Our CFAM (strategy 3) combined the features of RGB images and the 2D score map, reducing the error from strategy 2 by more than 1 mm and reducing the error from [PERSON]'s framework by more than 4 mm. Strategy 3 was better than strategy 2, and strategy 1 was better than [PERSON]'s framework. The main reason for the improvement was the addition of channel attention, but the improvement of strategy 3 was greater than that of strategy 1, and the accuracy was also improved.