3D2M Dataset: A 3-Dimension diverse Mesh Dataset

TL;DR

The paper introduces a diverse 3D facial mesh dataset with extensive ethnic and gender variation, designed to support advancements in 3D facial reconstruction and related applications.

Contribution

It provides a comprehensive, annotated 3D facial mesh dataset with broad demographic diversity, addressing the need for robust data in facial reconstruction research.

Findings

Dataset includes 188 3D facial meshes with diverse ethnicities.

Meshes are annotated with key facial landmarks.

Supports applications like facial retargeting and real-time video representation.

Abstract

Three-dimensional (3D) reconstruction has emerged as a prominent area of research, attracting significant attention from academia and industry alike. Among the various applications of 3D reconstruction, facial reconstruction poses some of the most formidable challenges. Additionally, each individuals facial structure is unique, requiring algorithms to be robust enough to handle this variability while maintaining fidelity to the original features. This article presents a comprehensive dataset of 3D meshes featuring a diverse range of facial structures and corresponding facial landmarks. The dataset comprises 188 3D facial meshes, including 73 from female candidates and 114 from male candidates. It encompasses a broad representation of ethnic backgrounds, with contributions from 45 different ethnicities, ensuring a rich diversity in facial characteristics. Each facial mesh is accompanied by key points that accurately annotate the relevant features, facilitating precise analysis and manipulation. This dataset is particularly valuable for applications such as facial re targeting, the study of facial structure components, and real-time person representation in video streams. By providing a robust resource for researchers and developers, it aims to advance the field of 3D facial reconstruction and related technologies.