HandOccNet: Occlusion-Robust 3D Hand Mesh Estimation Network (2203.14564v1)

Abstract: Hands are often severely occluded by objects, which makes 3D hand mesh estimation challenging. Previous works often have disregarded information at occluded regions. However, we argue that occluded regions have strong correlations with hands so that they can provide highly beneficial information for complete 3D hand mesh estimation. Thus, in this work, we propose a novel 3D hand mesh estimation network HandOccNet, that can fully exploits the information at occluded regions as a secondary means to enhance image features and make it much richer. To this end, we design two successive Transformer-based modules, called feature injecting transformer (FIT) and self- enhancing transformer (SET). FIT injects hand information into occluded region by considering their correlation. SET refines the output of FIT by using a self-attention mechanism. By injecting the hand information to the occluded region, our HandOccNet reaches the state-of-the-art performance on 3D hand mesh benchmarks that contain challenging hand-object occlusions. The codes are available in: https://github.com/namepllet/HandOccNet.

• The paper introduces a feature injection mechanism that leverages occluded hand regions to enhance 3D mesh estimation accuracy.
• It employs FIT and SET transformer modules to integrate primary and secondary features for robust prediction.
• Benchmarking on HO-3D and FPHA shows a mean joint error of 9.1 mm, outperforming previous state-of-the-art methods.

Overview of HandOccNet: Occlusion-Robust 3D Hand Mesh Estimation Network

The paper introduces HandOccNet, a novel architecture aimed at enhancing the robustness of 3D hand mesh estimation under occluded conditions commonly encountered in hand-object interaction scenarios. The primary innovation in this research is the implementation of a feature injection mechanism that leverages occluded regions of an input image to enhance feature richness and robustness. Unlike previous methods that often ignored or diminished the significance of occluded regions, HandOccNet actively utilizes them as secondary features to infer more complete hand meshes.

Methodology

The architecture consists of several key components:

• Feature Injecting Transformer (FIT): This module is responsible for integrating information from primary features, associated with visible hand regions, into secondary features, which correspond to occluded areas. FIT employs a combination of softmax-based and sigmoid-based attention mechanisms to avoid undesired high correlation scores, which can lead to less accurate feature integration.
• Self-Enhancing Transformer (SET): Following FIT, the SET module refines the feature map using a standard self-attention mechanism. This step is crucial for ensuring that the injected features preserve coherence and contribute meaningfully to the final hand mesh prediction.

Both FIT and SET exploit the ability of transformers to model correlations effectively, enhancing robustness even in severe occlusion conditions by facilitating long-range dependencies between features.

Numerical Results

HandOccNet is benchmarked on high-challenge datasets including HO-3D and FPHA, which feature realistic hand-object interactions with significant occlusions. The results demonstrate significant performance improvements over prior state-of-the-art methods in terms of mean joint and mesh error, as well as F-score metrics. Specifically, HandOccNet achieves a mean joint error of 9.1 mm on the HO-3D dataset, outperforming existing systems.

Implications and Future Work

The proposed methodology redefines the handling of occluded information in hand mesh estimation, providing a new perspective that could be extended to other areas of computer vision, such as full-body pose estimation and scene understanding, where occlusion remains a persistent challenge. By showing the benefits of treating occluded regions as valuable sources of information, HandOccNet opens up new avenues for improvement in real-time applications, particularly in augmented reality (AR) and virtual reality (VR) where interaction fidelity is critical.

Future work could involve adapting this approach to simultaneously estimate interacting objects' positions and orientations, further enhancing the contextual understanding of hand-object interactions. Moreover, integrating temporal elements or leveraging multi-view perspectives using FIT and SET could potentially lead to advancements in video data processing, presenting a promising direction for subsequent research.

In conclusion, HandOccNet serves as a foundational step towards more occlusion-robust 3D mesh estimation techniques. This contribution, focusing on the strategic use of secondary features, highlights the subtleties of effectively leveraging deep learning in the context of incomplete visual inputs.