# 3D2M Dataset: A 3-Dimension diverse Mesh Dataset

arXiv: https://arxiv.org/abs/2410.07415 · HTML: https://mlsanity.com/paper/2410.07415/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 33 references that are in the map.
- First posted on arXiv 9 Oct 2024.

## Builds on (most cited first, 14 of 14)

- [FaceScape: a Large-scale High Quality 3D Face Dataset and Detailed Riggable 3D Face Prediction](https://mlsanity.com/paper/2003.13989/) (2020)
- [Recovering 3D Human Mesh from Monocular Images: A Survey](https://mlsanity.com/paper/2203.01923/) (2022)
- [Imitator: Personalized Speech-driven 3D Facial Animation](https://mlsanity.com/paper/2301.00023/) (2022)
- [FDNeRF: Few-shot Dynamic Neural Radiance Fields for Face Reconstruction and Expression Editing](https://mlsanity.com/paper/2208.05751/) (2022)
- [Instant Multi-View Head Capture through Learnable Registration](https://mlsanity.com/paper/2306.07437/) (2023)
- [DAD-3DHeads: A Large-scale Dense, Accurate and Diverse Dataset for 3D Head Alignment from a Single Image](https://mlsanity.com/paper/2204.03688/) (2022)
- [Textured Mesh Quality Assessment: Large-Scale Dataset and Deep Learning-based Quality Metric](https://mlsanity.com/paper/2202.02397/) (2022)
- [Survey on 3D face reconstruction from uncalibrated images](https://mlsanity.com/paper/2011.05740/) (2020)
- [Fast Facial Landmark Detection and Applications: A Survey](https://mlsanity.com/paper/2101.10808/) (2021)
- [Do We Really Need to Collect Millions of Faces for Effective Face Recognition?](https://mlsanity.com/paper/1603.07057/) (2016)
- [Understanding bias in facial recognition technologies](https://mlsanity.com/paper/2010.07023/) (2020)
- [Headset: Human emotion awareness under partial occlusions multimodal dataset](https://mlsanity.com/paper/2402.09107/) (2024)
- [VGGHeads: 3D Multi Head Alignment with a Large-Scale Synthetic Dataset](https://mlsanity.com/paper/2407.18245/) (2024)
- [3D Face Style Transfer with a Hybrid Solution of NeRF and Mesh Rasterization](https://mlsanity.com/paper/2311.13168/) (2023)

## Abstract (arXiv)

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.