Multiscale Feature Fusion Network for Monocular Complex Hand Pose Estimation
Zhi Zhan , Guang Luo
Hand pose estimation based on a single RGB image has low accuracy due to the complexity of the pose, local self-similarity of finger features, and occlusion. A multiscale feature fusion network (MS-FF) for monocular vision gesture pose estimation is proposed to address this problem. The network can take full advantage of different channel information to enhance important gesture information, and it can simultaneously extract features from feature maps of different resolutions to obtain as much detailed feature information and deep semantic information as possible. The feature maps are merged to obtain the hand pose results. The InterHand2.6M dataset and Rendered Handpose Dataset (RHD) are used to train the MS-FF. Compared with the other methods (which can estimate interacting hand poses from a single RGB image), the MS-FF obtains the smallest average error of hand joints on RHD, verifying its effectiveness.
Introduction: Hand pose estimation aims to identify and localize key points of human hands in images, and it has a wide range of applications in virtual reality (VR) and augmented reality (AR) [1]. Methods based on deep learning have obvious advantages over traditional methods, both in processing speed and prediction accuracy. However, owing to the complexity and diversity of the photographic environment, such as hand shapes and occlusion, the robustness of hand pose estimation methods is low.
Hand pose estimation methods can be categorized as either depth- [2-5,15] and RGB-based [6-14,16]. Most methods rely on depth images, such as Chen et al. [2] extracted effective joint features through the initially estimated hand pose as guiding information, then fused the joint features of the same fingers, and finally regressed the hand pose by fusing the finger features. However, the method of connecting five fingers and the palm at the same time can cause loss in accuracy. According to Zhang et al. [4] made full use of the information between the adjacent joints of the fingers to estimate the depth coordinates. Then, 2D hand joint estimation and depth estimation of a part of the hand joints were used as the bootstrap information to obtain depth coordinates of all the hand joints.
Deep images are often limited by the application context, so RGB images have been used for hand pose estimation. Simon et al. [6] estimated 2D hand poses from multi-view images and extended them to the 3D space. However, this method could not estimate hand pose from a single RGB image. Spurr et al. [7] used RGB images to train an encoder-decoder model to estimate the complete 3D hand pose with different inputs. However, the method did not make full use of the hand structure. Yang et al. [9] learned the hand pose and hand images by a disentangled variational autoencoder to achieve image synthesis and hand pose estimation, but the disentangled process may lose useful information. Since most datasets only have single hand sequences, estimating complex gestures is relatively difficult. For this reason, Moon et al. [16] constructed a dataset containing single and interacting hand sequences. Additionally, the InterNet model was proposed to estimate hand poses by a single RGB image. Due to the influence of occlusion, the method cannot estimate complex hand pose well. However, the edge information in the hand pose estimation is usually ignored, due to the presence of occlusion, this information is especially important for extracting the information of the occluded part. Simultaneously, because the fingertip is a small object, it is relatively difficult to recognize the joint at the fingertips. To address this, a robust Multi-Scale Feature Fusion Network (MS-FF) is presented in this paper. The main contributions of this method are as follows:
  1. MS-FF more accurately estimates hand poses in an RGB image and better copes with complex application scenarios, so as to better deal with difficult-to-recognize joints and inaccurate gesture recognition in occlusion scenes;
  2. Channels contain different implicit information. We need to focus on the information that is more important for recognizing gestures. A channel conversion module adjusts the weights of channels to enhance important information;
  3. Fingertips occupy a small percentage of an image, and are relatively difficult to identify. A global regression module generates different resolutions with rich semantic information, to better utilize image edge details and deep information, which is important in estimating finger poses;
  4. The global regression module may not accurately identify occluded joints. A local optimization module is designed with deeper information in the feature map. It fuses all level feature maps, correcting joints that do not return to the correction position, for better application to the occlusion scene;