Decoupled Iterative Refinement Framework for Interacting Hands Reconstruction from a Single RGB Image

TL;DR

This paper introduces a decoupled iterative refinement framework that improves the accuracy and robustness of reconstructing interacting hands from a single RGB image by modeling spatial relationships and reducing occlusion issues.

Contribution

The proposed method uniquely combines 2D visual and 3D joint feature spaces with iterative refinement, outperforming existing methods in interacting hands reconstruction.

Findings

Outperforms all existing two-hand reconstruction methods on InterHand2.6M dataset.

Effectively models spatial relationships between hands to handle occlusion.

Achieves pixel-alignment hand reconstruction with high accuracy.

Abstract

Reconstructing interacting hands from a single RGB image is a very challenging task. On the one hand, severe mutual occlusion and similar local appearance between two hands confuse the extraction of visual features, resulting in the misalignment of estimated hand meshes and the image. On the other hand, there are complex spatial relationship between interacting hands, which significantly increases the solution space of hand poses and increases the difficulty of network learning. In this paper, we propose a decoupled iterative refinement framework to achieve pixel-alignment hand reconstruction while efficiently modeling the spatial relationship between hands. Specifically, we define two feature spaces with different characteristics, namely 2D visual feature space and 3D joint feature space. First, we obtain joint-wise features from the visual feature map and utilize a graph convolution network and a transformer to perform intra- and inter-hand information interaction in the 3D joint feature space, respectively. Then, we project the joint features with global information back into the 2D visual feature space in an obfuscation-free manner and utilize the 2D convolution for pixel-wise enhancement. By performing multiple alternate enhancements in the two feature spaces, our method can achieve an accurate and robust reconstruction of interacting hands. Our method outperforms all existing two-hand reconstruction methods by a large margin on the InterHand2.6M dataset.