Multi-view Hand Reconstruction with a Point-Embedded Transformer

TL;DR

This paper introduces POEM, a multi-view hand mesh reconstruction model that embeds a static basis point in stereo space, enabling effective feature fusion and demonstrating strong generalizability in real-world scenarios.

Contribution

The paper proposes embedding a static basis point in stereo space and a training strategy with diverse multi-view data, improving multi-view hand reconstruction's accuracy and generalizability.

Findings

High accuracy in multi-view hand mesh reconstruction.

Strong generalization to diverse camera configurations.

Open-source implementation available.

Abstract

This work introduces a novel and generalizable multi-view Hand Mesh Reconstruction (HMR) model, named POEM, designed for practical use in real-world hand motion capture scenarios. The advances of the POEM model consist of two main aspects. First, concerning the modeling of the problem, we propose embedding a static basis point within the multi-view stereo space. A point represents a natural form of 3D information and serves as an ideal medium for fusing features across different views, given its varied projections across these views. Consequently, our method harnesses a simple yet effective idea: a complex 3D hand mesh can be represented by a set of 3D basis points that 1) are embedded in the multi-view stereo, 2) carry features from the multi-view images, and 3) encompass the hand in it. The second advance lies in the training strategy. We utilize a combination of five large-scale multi-view datasets and employ randomization in the number, order, and poses of the cameras. By processing such a vast amount of data and a diverse array of camera configurations, our model demonstrates notable generalizability in the real-world applications. As a result, POEM presents a highly practical, plug-and-play solution that enables user-friendly, cost-effective multi-view motion capture for both left and right hands. The model and source codes are available at https://github.com/JubSteven/POEM-v2.