Learning to Estimate 3D Hand Pose from Single RGB ImagesLearning to Estimate 3D Hand Pose from Single RGB ImagesZimmermann, Christian and Brox, Thomas2017
Paper summaryukrdailoThis paper estimate 3D hand shape from **single** RGB images based on deep learning. The overall pipeline is the following:
https://i.imgur.com/H72P5ns.png
1. **Hand Segmentation** network is derived from this [paper](https://arxiv.org/pdf/1602.00134.pdf) but, in essence, any segmentation network would do the job. Hand image is cropped from the original image by utilizing segmentation mask and resized to a fixed size (256x256) with bilinear interpolation.
2. **Detecting hand keypoints**. 2D Keypoint detection is formulated as predicting score map for each hand joints (fixed size = 21). Encoder-decoder architecture is used.
3. **3D hand pose estimation**.
https://i.imgur.com/uBheX3o.png
- In this paper, the hand pose is represented as $w_i = (x_i, y_i, z_i)$, where $i$ is index for a particular hand joint. This representation is further normalized $w_i^{norm} = \frac{1}{s} \cdot w_i$, where $s = ||w_{k+1} - w_{k} ||$, and relative position to a reference joint $r$ (palm) is obtained as $w_i^{rel} = w_i^{norm} - w_r^{norm}$.
- The network predicts coordinates within a canonical frame and additionally estimate the transformation into the canonical frame (as opposite to predicting absolute 3D coordinates). Therefore, the network predicts $w^{c^*} = R(w^{rel}) \cdot w^{rel}$ and $R(w^{rel}) = R_y \cdot R_{xz}$.
Information whether left/right hand is the input is concatenated to flattened feature representation. The training loss is composed of a separate term for canonical coordinates and canonical transformation matrix L2 losses.
Contribution:
- Apparently, the first method to perform 3D hand shape estimation from a single RGB image rather than using both RGB and depth sensors;
- Possible extension to sign language recognition problem by attaching classifier on predicted 3D poses.
While this approach quite accurately predicts hand 3D poses among frames, they often fluctuate among frames. Probably several techniques (i.e. optical flow, RNN, post-processing smoothing) can be used for ensuring temporal consistency and make predictions more stable across frames.
This paper estimate 3D hand shape from **single** RGB images based on deep learning. The overall pipeline is the following:
https://i.imgur.com/H72P5ns.png
1. **Hand Segmentation** network is derived from this [paper](https://arxiv.org/pdf/1602.00134.pdf) but, in essence, any segmentation network would do the job. Hand image is cropped from the original image by utilizing segmentation mask and resized to a fixed size (256x256) with bilinear interpolation.
2. **Detecting hand keypoints**. 2D Keypoint detection is formulated as predicting score map for each hand joints (fixed size = 21). Encoder-decoder architecture is used.
3. **3D hand pose estimation**.
https://i.imgur.com/uBheX3o.png
- In this paper, the hand pose is represented as $w_i = (x_i, y_i, z_i)$, where $i$ is index for a particular hand joint. This representation is further normalized $w_i^{norm} = \frac{1}{s} \cdot w_i$, where $s = ||w_{k+1} - w_{k} ||$, and relative position to a reference joint $r$ (palm) is obtained as $w_i^{rel} = w_i^{norm} - w_r^{norm}$.
- The network predicts coordinates within a canonical frame and additionally estimate the transformation into the canonical frame (as opposite to predicting absolute 3D coordinates). Therefore, the network predicts $w^{c^*} = R(w^{rel}) \cdot w^{rel}$ and $R(w^{rel}) = R_y \cdot R_{xz}$.
Information whether left/right hand is the input is concatenated to flattened feature representation. The training loss is composed of a separate term for canonical coordinates and canonical transformation matrix L2 losses.
Contribution:
- Apparently, the first method to perform 3D hand shape estimation from a single RGB image rather than using both RGB and depth sensors;
- Possible extension to sign language recognition problem by attaching classifier on predicted 3D poses.
While this approach quite accurately predicts hand 3D poses among frames, they often fluctuate among frames. Probably several techniques (i.e. optical flow, RNN, post-processing smoothing) can be used for ensuring temporal consistency and make predictions more stable across frames.