HOPE-Net: A Graph-based Model for Hand-Object Pose Estimation (2004.00060v1)

Abstract: Hand-object pose estimation (HOPE) aims to jointly detect the poses of both a hand and of a held object. In this paper, we propose a lightweight model called HOPE-Net which jointly estimates hand and object pose in 2D and 3D in real-time. Our network uses a cascade of two adaptive graph convolutional neural networks, one to estimate 2D coordinates of the hand joints and object corners, followed by another to convert 2D coordinates to 3D. Our experiments show that through end-to-end training of the full network, we achieve better accuracy for both the 2D and 3D coordinate estimation problems. The proposed 2D to 3D graph convolution-based model could be applied to other 3D landmark detection problems, where it is possible to first predict the 2D keypoints and then transform them to 3D.

HOPE-Net: A Graph-based Model for Hand-Object Pose Estimation

The paper "HOPE-Net: A Graph-based Model for Hand-Object Pose Estimation" presents an innovative approach to real-time hand-object pose estimation, an essential task in computer vision with implications across augmented reality, action recognition, robotics, and more. This paper introduces HOPE-Net, a lightweight, graph-based model designed to effectively predict both 2D and 3D hand and object poses from single RGB images.

Hand-object pose estimation is a complex problem due to the dynamic movements of hands interacting with objects and the subsequent occlusions these movements cause. The HOPE-Net model tackles this challenge by utilizing graph convolutional neural networks (GCNNs), which are adept at handling graph-structured data—a fit given the skeletal and kinematic constraints of hand-object interactions.

The HOPE-Net architecture is composed of a cascade of two GCNNs. The first network predicts 2D coordinates of hand joints and object corners, while the second network transforms these 2D coordinates into 3D representations. The strategy of initially estimating in 2D and then converting into 3D can leverage the strengths of detection-based models for 2D estimation and more complex regression-based models for 3D coordinates.

Key contributions of the paper include:

1. HOPE-Net Framework: HOPE-Net offers real-time hand-object pose estimation, a significant achievement given the computational demands often associated with deep learning models in pose estimation tasks. Its lightweight nature implies feasible deployment in applications requiring real-time processing.
2. Adaptive Graph U-Net Architecture: This novel GCNN structure significantly improves stability and performance in 3D pose estimation tasks. By integrating novel graph convolution, pooling, and unpooling layers, the model effectively captures hand-object interaction dynamics.
3. Improved Accuracy: HOPE-Net demonstrates superior performance relative to existing state-of-the-art models on datasets capturing first-person and third-person perspectives of hand-object interactions.

The experimental results indicate HOPE-Net's proficiency in handling 2D and 3D pose predictions with a high degree of accuracy. The model effectively removes noise from coordinates, highlighting its robustness in real-world scenarios where precise pose estimation is crucial.

Despite these advancements, limitations persist regarding generalization across varied object shapes. Objects with non-convex geometries fall outside the model's current capabilities, suggesting the need for expanded training datasets to encompass a broader range of object types.

The future directions posited by the authors include the incorporation of temporal information, enabling the framework to advance towards action detection and enriched contextual understanding. The potential application of the Adaptive Graph U-Net to other graph visualization tasks is vast, spanning protein classification to mesh analysis.

HOPE-Net represents a substantial contribution to pose estimation literature, advancing methodologies for accurate and efficient hand-object pose detection. As research continues, the integration of holistic datasets and temporal dynamics will undoubtedly augment the capabilities of graph-based models like HOPE-Net.