Controllable Hand Grasp Generation for HOI and Efficient Evaluation Methods
Ishant, Rongliang Wu, Joo Hwee Lim

TL;DR
This paper introduces a novel framework for controllable hand grasp generation in HOI tasks using higher order geometric representations, improving quality, controllability, and evaluation efficiency over existing methods.
Contribution
It proposes higher order geometric representations inspired by spectral graph theory to enhance hand pose generation and introduces an efficient evaluation framework addressing biases in traditional metrics.
Findings
HOR's improve hand pose quality
The diffusion method outperforms SOTA
New evaluation metrics are more stable and unbiased
Abstract
Controllable affordance Hand-Object Interaction (HOI) generation has become an increasingly important area of research in computer vision. In HOI generation, the hand grasp generation is a crucial step for effectively controlling the geometry of the hand. Current hand grasp generation methods rely on 3D information for both the hand and the object. In addition, these methods lack controllability concerning the hand's location and orientation. We treat the hand pose as the discrete graph structure and exploit the geometric priors. It is well established that higher order contextual dependency among the points improves the quality of the results in general. We propose a framework of higher order geometric representations (HOR's) inspired by spectral graph theory and vector algebra to improve the quality of generated hand poses. We demonstrate the effectiveness of our proposed HOR's in…
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Taxonomy
TopicsHand Gesture Recognition Systems · Gaze Tracking and Assistive Technology · Robotics and Automated Systems
MethodsDiffusion
