ContactOpt: Optimizing Contact to Improve Grasps (2104.07267v1)

Abstract: Physical contact between hands and objects plays a critical role in human grasps. We show that optimizing the pose of a hand to achieve expected contact with an object can improve hand poses inferred via image-based methods. Given a hand mesh and an object mesh, a deep model trained on ground truth contact data infers desirable contact across the surfaces of the meshes. Then, ContactOpt efficiently optimizes the pose of the hand to achieve desirable contact using a differentiable contact model. Notably, our contact model encourages mesh interpenetration to approximate deformable soft tissue in the hand. In our evaluations, our methods result in grasps that better match ground truth contact, have lower kinematic error, and are significantly preferred by human participants. Code and models are available online.

• The paper introduces ContactOpt, an algorithm that optimizes hand poses using deep learning and differentiable models to improve grasp quality.
• It employs DeepContact for mesh-based contact estimation and DiffContact for gradient optimization, reducing MPJPE by nearly 70%.
• Evaluations on ContactPose and HO-3D datasets demonstrate improved intersection volume and contact consistency preferred by human evaluators.

Critical Evaluation of "ContactOpt: Optimizing Contact to Improve Grasps"

The paper "ContactOpt: Optimizing Contact to Improve Grasps" introduces a novel algorithm that enhances the quality of hand-object interactions by refining hand poses for more realistic and precise contact optimization. This paper is of significant importance in fields such as robotics, virtual reality, and simulation where realistic hand-object interaction is crucial.

Overview and Methodology

The main contribution of the paper is the ContactOpt algorithm, which refines hand poses by optimizing physical contact. The approach consists of two primary components: DeepContact and DiffContact. DeepContact leverages a deep learning framework to estimate desirable contact areas between the object and the hand based on their respective meshes. Meanwhile, DiffContact, acting as a differentiable contact model, allows for efficient gradient-based optimization of hand poses to achieve these target contact points. The innovation of permitting mesh interpenetration in DiffContact is particularly noteworthy, as it simulates the deformable nature of soft tissue and results in more lifelike contact representations.

Through two types of evaluations—refining small inaccuracies and addressing large inaccuracies—the efficacy of ContactOpt in improving hand-object interactions is demonstrated. Using datasets such as ContactPose and HO-3D, the method is shown to reduce kinematic error significantly and is preferred by human evaluators over existing grasping techniques.

Results and Discussion

The paper provides strong numerical evidence supporting the performance improvements achieved by ContactOpt. Notably, the Intersection Volume, Mean Per-Joint Position Error (MPJPE), and coverage of contact significantly improved when using ContactOpt over the initial pose estimates. For instance, poses refined by ContactOpt exhibited a notable reduction in intersection volume and kinematic error while simultaneously increasing contact consistency with thermal maps in the dataset.

Experimentation reveals that even when challenged with substantial inaccuracies, ContactOpt maintains a robust performance, decreasing MPJPE by nearly 70% in some scenarios, highlighting its potential as a post-processing tool for enhancing grasp realism in existing systems.

Implications and Future Research

The proposed ContactOpt system represents an impactful advance for hand-object interaction modeling by introducing contact optimization as a core enhancement strategy. From a practical perspective, its ability to integrate with existing image-based pose estimators without comprising the quality of the hand-object interaction demonstrates its versatility and adaptability. The algorithm's compatibility with various datasets without requiring retraining is a valuable trait in AI applications where data variability is a hurdle.

The method also opens pathways for further research into enhancing contact realism in human-model interactions. Future investigations might focus on expanding the model's adaptability to more varied object meshes, improving computational efficiency through more sophisticated optimization techniques, or integrating additional physical constraints such as friction or object weight, which are crucial for tasks requiring precision in manipulation.

Overall, "ContactOpt: Optimizing Contact to Improve Grasps" provides a substantial contribution to the field of computer vision and robotics, offering a scalable solution for enhancing hand-object interaction tasks. Its adaptability and precision make it a promising tool for both academic research and commercial application in AI-driven interactive systems.