Optimization Method of Hand Pose Estimation Based on Unified View

Optimization Method of Hand Pose Estimation Based on Unified View

Accurate estimation of three-dimensional hand pose from depth images represents a crucial task in computer vision with applications spanning human-computer interaction, virtual reality, medical diagnostics, and sign language recognition. Despite significant advancements enabled by commercial depth cameras like Microsoft Kinect and Intel RealSense, challenges persist due to hand self-occlusion and joint self-similarity, which degrade estimation accuracy and efficiency.

This paper introduces a novel optimization method called Unified View Point (UVP) that addresses these challenges by resampling input depth images into a more favorable “front-facing” viewpoint. The core innovation lies in transforming single-view depth data to mitigate occlusion while preserving spatial relationships between joints. The UVP network consists of three key components: a viewpoint transformation module, a viewpoint unification loss function, and lightweight architectural improvements.

The viewpoint transformation module serves as the foundation of this approach. Given an input depth image, a specialized network extracts rotation features represented as Euler angles. These angles define a 3D rotation matrix that reorients the original point cloud data derived from the depth image. Unlike prior multi-view methods that require fixed viewpoints or multiple cameras, this module generates synthetic views adaptively, requiring only single-view input. The transformation maintains topological consistency while reducing occlusions prevalent in non-frontal perspectives.

To ensure the transformed viewpoint optimally supports pose estimation, the paper introduces a viewpoint unification loss function. This supervisory signal guides the network toward generating views that maximize visible hand area—a characteristic of front-facing perspectives where fingers appear least occluded. Experimental comparisons demonstrate that using depth value variance as the loss metric outperforms alternative formulations like convex hull area. The loss function effectively trains the network to produce standardized views that enhance downstream pose estimation.

Recognizing computational efficiency as critical for real-time applications, the method incorporates several lightweight modifications to the rotation feature extraction network. First, upsampling layers are removed to better model the unidirectional nature of Euler angle rotations. Second, conventional convolution blocks are replaced with multi-branch depthwise separable convolutions, significantly reducing parameters while maintaining feature extraction capability. Third, stage reduction in the network architecture decreases computational overhead without sacrificing accuracy. These optimizations collectively reduce model parameters from 28.8 million to just 1.5 million while improving performance.

The UVP framework demonstrates strong empirical results across three standard benchmarks: ICVL, NYU, and MSRA datasets. On ICVL, the method achieves a mean joint position error of 4.92 mm, outperforming contemporary approaches like TriHorn-Net (5.73 mm) and Virtual View Selection (5.16 mm). Similar advantages appear on NYU (7.43 mm) and MSRA (7.02 mm), where the technique surpasses alternatives including HandFoldingNet and AWR. Notably, the system processes 159.39 frames per second on an RTX3070 GPU, demonstrating real-time capability crucial for interactive applications.

Qualitative comparisons reveal that UVP produces more stable joint predictions, particularly for peripheral fingers prone to occlusion. Where competing methods may place joints outside hand contours, the unified viewpoint approach maintains plausible spatial relationships. The dual-view feature fusion—combining original and synthesized frontal views with an 80%-20% weighting—proves more effective than single-view estimation or multi-view ensembles requiring three or more perspectives.

Ablation studies validate each component’s contribution. Integrating UVP with existing models like DeepPrior++ and A2J consistently improves their accuracy by 1-2 mm, confirming the method’s generalizability. The viewpoint unification loss provides measurable benefits, with variance-based supervision reducing error compared to area-based alternatives. Lightweight modifications not only accelerate processing but surprisingly enhance precision, suggesting that the streamlined architecture better captures rotational transformations.

The technique’s adaptability stems from its modular design—the UVP module can augment most single-view depth-based pose estimators without architectural changes. This plug-and-play characteristic distinguishes it from rigid multi-view systems requiring specific camera arrangements or dataset formats. The viewpoint transformation operates as a preprocessing stage, making the approach compatible with diverse backbone networks.

Several insights emerge from this work. First, view standardization proves more effective than view selection or aggregation for handling occlusion. While previous methods manually chose or weighted multiple fixed viewpoints, UVP’s learned transformation automatically finds an optimal frontal perspective. Second, lightweight networks can outperform complex counterparts when designed to match task-specific characteristics—here, the rotational properties of Euler angles. Third, synthetic view generation from single images provides computational advantages over multi-camera systems while achieving comparable accuracy.

Future directions include investigating why different pose estimation networks benefit unevenly from UVP augmentation, potentially leading to specialized architectures for view-transformed data. Additionally, extending the unified viewpoint concept to other articulated object tracking scenarios—such as full-body pose estimation—could demonstrate broader applicability.

In conclusion, the UVP method advances 3D hand pose estimation through intelligent view synthesis and standardization. By transforming input perspectives to reduce occlusion while maintaining computational efficiency, the technique sets new benchmarks in accuracy and speed. Its modular design and strong generalization across datasets position it as a practical solution for real-world applications requiring robust, real-time hand tracking.

doi.org/10.19734/j.issn.1001-3695.2024.03.0113

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