3D hand pose estimation that involves accurate estimation of 3D human hand keypoint locations is crucial for many human-computer interaction applications such as augmented reality. However, this task poses significant challenges due to self-occlusion of the hands and occlusions caused by interactions with objects. In this paper, we propose HandMCM to address these challenges. Our HandMCM is a novel method based on the powerful state space model (Mamba). By incorporating modules for local information injection/filtering and correspondence modeling, the proposed correspondence Mamba effectively learns the highly dynamic kinematic topology of keypoints across various occlusion scenarios. Moreover, by integrating multi-modal image features, we enhance the robustness and representational capacity of the input, leading to more accurate hand pose estimation. Empirical evaluations on three benchmark datasets demonstrate that our model significantly outperforms current state-of-the-art methods, particularly in challenging scenarios involving severe occlusions. These results highlight the potential of our approach to advance the accuracy and reliability of 3D hand pose estimation in practical applications.

As artificial intelligence (AI) technology advances, Internet of Things (IoT) devices, such as mobile phones and augmented reality devices, are increasingly becoming crucial enablers of user-device interactions. Among the various methods of interaction, hand pose recognition and analysis is a crucial method to understand the intentions of users and perform precise functions. However, to perform such functions, a substantial amount of computation and resources are required, making it challenging to implement them on small form-factor devices with low-power consumption. For this reason, improving energy efficiency is a crucial objective in real-time hand pose estimation (HPE) applied to low-power platforms with limited resources. In this article, we introduce an FPGA-based energy-efficient real-time HPE system with an integrated image signal processor (ISP). The proposed system uses several low-power design techniques, including a systolic array with dynamic on/off control per processing element (PE), to minimize power consumption and save energy when not in use. In addition, we improve area efficiency by reducing the buffer size in the systolic array using a half-size shift buffer stack. Furthermore, the use of parallel and pipelined structures improved operational efficiency, resulting in a reduction in both operational time and power consumption. The evaluation results on a KU115 FPGA board show that the system achieves an error of 7.78 mm and can process 52 fps, demonstrating its capability for real-time HPE. Moreover, this system achieves high-energy efficiency, up to 61.74 GOPs/W, making it suitable for energy-efficient and accurate HPE in low-power environments.

Extracting keypoint locations from input hand frames, known as 3D hand pose estimation, is a critical task in various human-computer interaction applications. Essentially, the 3D hand pose estimation can be regarded as a 3D point subset generative problem conditioned on input frames. Thanks to the recent significant progress on diffusion-based generative models, hand pose estimation can also benefit from the diffusion model to estimate keypoint locations with high quality. However, directly deploying the existing diffusion models to solve hand pose estimation is non-trivial, since they cannot achieve the complex permutation mapping and precise localization. Based on this motivation, this paper proposes HandDiff, a diffusion-based hand pose estimation model that iteratively denoises accurate hand pose conditioned on hand-shaped image-point clouds. In order to recover keypoint permutation and accurate location, we further introduce Joint-wise condition and local detail condition. Experimental results demonstrate that the proposed HandDiff significantly outperforms the existing approaches on four challenging hand pose benchmark datasets. Codes and pre-trained models are publicly available at https://github.com/cwc1260/HandDiff.

Scene flow estimation provides the fundamental motion perception of a dynamic scene, which is of practical importance in many computer vision applications. In this paper, we propose a novel multi-scale bidirectional recurrent architecture that iteratively optimizes the coarse-tofine scene flow estimation. In each resolution scale of estimation, a novel bidirectional gated recurrent unit is proposed to bidirectionally and iteratively augment point features and produce progressively optimized scene flow. The optimization of each iteration is integrated with the hybrid correlation that captures not only local correlation but also semantic correlation for more accurate estimation. Experimental results indicate that our proposed architecture significantly outperforms the existing state-of-theart approaches on both FlyingThings3D and KITTI benchmarks while maintaining superior time efficiency. Codes and pre-trained models are publicly available at https://github.com/cwc1260/MSBRN.

3D hand pose estimation is a critical task in various human-computer interaction applications. Numerous deep learning based estimation models in this domain have been actively explored. However, the existing models follow a non-recurrent scheme and thus require complex architectures or redundant parameters in order to achieve acceptable model capacity. To tackle this limitation, this paper proposes HandR2N2, a compact neural network that iteratively regresses the hand pose using a novel residual recurrent unit. The recurrent design allows recursive exploitation of partial layers to gradually optimize previously estimated joint locations. In addition, we exploit graph reasoning to capture kinematic dependencies between joints for better performance. Experimental results show that the proposed model significantly outperforms the existing methods on three hand pose benchmark datasets in terms of both accuracy and efficiency. Codes and pre-trained models are publicly available at https://github.com/cwc1260/HandR2N2.

With increasing applications of 3D hand pose estimation in various human-computer interaction applications, convolution neural networks (CNNs) based estimation models have been actively explored. However, the existing models require complex architectures or redundant computational resources to trade with the acceptable accuracy. To tackle this limitation, this paper proposes HandFoldingNet, an accurate and efficient hand pose estimator that regresses the hand joint locations from the normalized 3D hand point cloud input. The proposed model utilizes a folding-based decoder that folds a given 2D hand skeleton into the corresponding joint coordinates. For higher estimation accuracy, folding is guided by multi-scale features, which include both global and joint-wise local features. Experimental results show that the proposed model outperforms the existing methods on three hand pose benchmark datasets with the lowest model parameter requirement. Code is available at https://github.com/cwc1260/HandFold.