Learning Correlation-aware Aleatoric Uncertainty for 3D Hand Pose Estimation

2025-09-01

AI Summary

Existing 3D hand pose estimation methods typically neglect aleatoric uncertainty modeling and fail to explicitly encode structural correlations among joints. To address these limitations, we propose a lightweight, plug-and-play probabilistic framework. First, we implicitly model intrinsic joint correlations via a single linear layer—balancing accuracy and efficiency without altering the backbone architecture. Second, we introduce a probabilistic output-space formulation to enable end-to-end estimation of aleatoric uncertainty. Our method is fully compatible with mainstream architectures and requires no modifications to the underlying network. Evaluated on standard benchmarks, it achieves state-of-the-art (SOTA) pose accuracy while significantly improving uncertainty calibration—outperforming existing uncertainty modeling paradigms in both reliability and expressiveness.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Relational Probabilistic Models
• Intelligent Robots: State Estimation

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

3D hand pose estimation is a fundamental task in understanding human hands. However, accurately estimating 3D hand poses remains challenging due to the complex movement of hands, self-similarity, and frequent occlusions. In this work, we address two limitations: the inability of existing 3D hand pose estimation methods to estimate aleatoric (data) uncertainty, and the lack of uncertainty modeling that incorporates joint correlation knowledge, which has not been thoroughly investigated. To this end, we introduce aleatoric uncertainty modeling into the 3D hand pose estimation framework, aiming to achieve a better trade-off between modeling joint correlations and computational efficiency. We propose a novel parameterization that leverages a single linear layer to capture intrinsic correlations among hand joints. This is enabled by formulating the hand joint output space as a probabilistic distribution, allowing the linear layer to capture joint correlations. Our proposed parameterization is used as a task head layer, and can be applied as an add-on module on top of the existing models. Our experiments demonstrate that our parameterization for uncertainty modeling outperforms existing approaches. Furthermore, the 3D hand pose estimation model equipped with our uncertainty head achieves favorable accuracy in 3D hand pose estimation while introducing new uncertainty modeling capability to the model. The project page is available at https://hand-uncertainty.github.io/.

Problem

• Estimating aleatoric uncertainty in 3D hand pose estimation
• Modeling joint correlations with computational efficiency
• Addressing self-similarity and occlusion challenges in hand tracking

Innovation

• Aleatoric uncertainty modeling for 3D hand pose
• Single linear layer captures joint correlations
• Probabilistic distribution formulation for output space