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Weakly-Supervised 3D Hand Reconstruction with Knowledge Prior and Uncertainty Guidance

Yufei Zhang, Jeffrey O. Kephart, Qiang Ji

TL;DR

The paper addresses the ill-posed problem of monocular 3D hand reconstruction by proposing a weakly-supervised framework that embeds comprehensive hand knowledge from biomechanics, functional anatomy, and physics as differentiable priors, enabling training with only 2D landmark annotations. It additionally models image observation uncertainty with a heteroscedastic Negative Log-Likelihood loss, improving robustness to occlusion and depth ambiguity. The approach yields substantial improvements over prior weakly-supervised methods, achieving roughly a 21% gain on the FreiHAND dataset, and demonstrates strong data-efficiency and generalization across multiple datasets. This work enhances practical deployment for VR/AR and HCI by reducing dependence on expensive 3D supervision while providing principled uncertainty estimates.

Abstract

Fully-supervised monocular 3D hand reconstruction is often difficult because capturing the requisite 3D data entails deploying specialized equipment in a controlled environment. We introduce a weakly-supervised method that avoids such requirements by leveraging fundamental principles well-established in the understanding of the human hand's unique structure and functionality. Specifically, we systematically study hand knowledge from different sources, including biomechanics, functional anatomy, and physics. We effectively incorporate these valuable foundational insights into 3D hand reconstruction models through an appropriate set of differentiable training losses. This enables training solely with readily-obtainable 2D hand landmark annotations and eliminates the need for expensive 3D supervision. Moreover, we explicitly model the uncertainty that is inherent in image observations. We enhance the training process by exploiting a simple yet effective Negative Log Likelihood (NLL) loss that incorporates uncertainty into the loss function. Through extensive experiments, we demonstrate that our method significantly outperforms state-of-the-art weakly-supervised methods. For example, our method achieves nearly a 21\% performance improvement on the widely adopted FreiHAND dataset.

Weakly-Supervised 3D Hand Reconstruction with Knowledge Prior and Uncertainty Guidance

TL;DR

The paper addresses the ill-posed problem of monocular 3D hand reconstruction by proposing a weakly-supervised framework that embeds comprehensive hand knowledge from biomechanics, functional anatomy, and physics as differentiable priors, enabling training with only 2D landmark annotations. It additionally models image observation uncertainty with a heteroscedastic Negative Log-Likelihood loss, improving robustness to occlusion and depth ambiguity. The approach yields substantial improvements over prior weakly-supervised methods, achieving roughly a 21% gain on the FreiHAND dataset, and demonstrates strong data-efficiency and generalization across multiple datasets. This work enhances practical deployment for VR/AR and HCI by reducing dependence on expensive 3D supervision while providing principled uncertainty estimates.

Abstract

Fully-supervised monocular 3D hand reconstruction is often difficult because capturing the requisite 3D data entails deploying specialized equipment in a controlled environment. We introduce a weakly-supervised method that avoids such requirements by leveraging fundamental principles well-established in the understanding of the human hand's unique structure and functionality. Specifically, we systematically study hand knowledge from different sources, including biomechanics, functional anatomy, and physics. We effectively incorporate these valuable foundational insights into 3D hand reconstruction models through an appropriate set of differentiable training losses. This enables training solely with readily-obtainable 2D hand landmark annotations and eliminates the need for expensive 3D supervision. Moreover, we explicitly model the uncertainty that is inherent in image observations. We enhance the training process by exploiting a simple yet effective Negative Log Likelihood (NLL) loss that incorporates uncertainty into the loss function. Through extensive experiments, we demonstrate that our method significantly outperforms state-of-the-art weakly-supervised methods. For example, our method achieves nearly a 21\% performance improvement on the widely adopted FreiHAND dataset.
Paper Structure (15 sections, 8 equations, 5 figures, 3 tables)

This paper contains 15 sections, 8 equations, 5 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Fully-Supervised Approaches
  4. Weakly-Supervised Approaches
  5. Method
  6. Preliminaries
  7. Study and Incorporation of Generic Hand Knowledge
  8. Training with Negative Log-Likelihood
  9. Total Training Loss
  10. Experiment
  11. Datasets, Metrics, and Implementation Details
  12. Ablation Study
  13. Comparison with State-of-the-Art
  14. Discussion
  15. Conclusion

Figures (5)

  • Figure 1: Motivation of Studying Hand knowledge and Modeling Uncertainty. (a) T-SNE visualization van2008visualizing of hand poses from a real dataset (FreiHAND zimmermann2019freihand) and a synthetic dataset (DARTset gao2022dart). A large portion of synthetically generated hand poses can be unnatural (DARTset-Unnatural), such as the presence of invalid bending or penetration (marked by red crosses). (b) Images and 2D hand label from existing hand datasets. We mark regions with high uncertainty attributed to self-similarity (orange), motion blur (yellow), occlusion (pink), or poor image quality (purple).
  • Figure 2: Overview of the proposed method. Given a hand image, the regression model predicts the 3D hand pose and shape for recovering the 3D hand mesh through forward kinematics. The distribution of 2D hand landmark positions is specified via the projection of 3D hand and the predicted variance. The model is trained by incorporating generic hand knowledge and utilizing 2D hand landmark annotations.
  • Figure 3: Illustration of Generic Hand Knowledge. (a) Different hand joints have different degrees of freedom (DOFs) and ranges of motion hamill2006biomechanical. (b) For the four fingers, (i) mutual restrictions exist between joint bending ($\alpha$) and splaying ($\gamma$) of the MCPs (metacarpophalangeal joints); (ii) the bending of the DIP (distal interphalangeal joint) induces bending in the PIP (proximal interphalangeal joint) due to tighter ligaments schreuders2014functional. (c) Different hand digits are prevented from penetrating into each other.
  • Figure 4: Qualitative Evaluation of Incorporating Hand Knowledge. The images are from FreiHAND's test set. For each example, we present the rendered 3D hand overlaid on the input image, along with the reconstructed 3D hand viewed from a different angle. The results from left to right are obtained by incorporating the additional knowledge specified at the bottom. "F-Anatomy" denotes "Functional Anatomy". Reconstructions with notable errors are marked by red crosses.
  • Figure 5: Qualitative Evaluation of Training with the NLL. (a) Evaluation of the models trained without and with the NLL. Notable errors are marked by red crosses. Colors indicate finger identity: thumb (black), index (yellow), middle (green), ring (blue), and pinky (magenta). The width and height of the ellipse at each joint represent the magnitude of the estimated variance along the horizontal and vertical directions, respectively. (b) Training images with high uncertainty, captured by large estimated variances (averaged over all joints). The images in (a) and (b) are from FreiHAND (top), DexYCB (middle), and HO3D (bottom).