Dual-Branch Graph Transformer Network for 3D Human Mesh Reconstruction from Video

TL;DR

This paper introduces DGTR, a dual-branch graph transformer network that improves 3D human mesh reconstruction from video by effectively capturing both long-term motion dependencies and local details, outperforming existing methods.

Contribution

The paper proposes a novel dual-branch transformer architecture combining global and local feature extraction for enhanced 3D human mesh reconstruction from video.

Findings

DGTR outperforms state-of-the-art methods in accuracy.

DGTR maintains smooth motion while improving reconstruction quality.

DGTR uses fewer parameters and FLOPs, demonstrating efficiency.

Abstract

Human Mesh Reconstruction (HMR) from monocular video plays an important role in human-robot interaction and collaboration. However, existing video-based human mesh reconstruction methods face a trade-off between accurate reconstruction and smooth motion. These methods design networks based on either RNNs or attention mechanisms to extract local temporal correlations or global temporal dependencies, but the lack of complementary long-term information and local details limits their performance. To address this problem, we propose a \textbf{D}ual-branch \textbf{G}raph \textbf{T}ransformer network for 3D human mesh \textbf{R}econstruction from video, named DGTR. DGTR employs a dual-branch network including a Global Motion Attention (GMA) branch and a Local Details Refine (LDR) branch to parallelly extract long-term dependencies and local crucial information, helping model global human motion and local human details (e.g., local motion, tiny movement). Specifically, GMA utilizes a global transformer to model long-term human motion. LDR combines modulated graph convolutional networks and the transformer framework to aggregate local information in adjacent frames and extract crucial information of human details. Experiments demonstrate that our DGTR outperforms state-of-the-art video-based methods in reconstruction accuracy and maintains competitive motion smoothness. Moreover, DGTR utilizes fewer parameters and FLOPs, which validate the effectiveness and efficiency of the proposed DGTR. Code is publicly available at \href{https://github.com/TangTao-PKU/DGTR}{\textcolor{myBlue}{https://github.com/TangTao-PKU/DGTR}}.