World's most popular 3D human datasets

To create ground truth synthetic data for computer vision research and simulation

Synthetic Human Data For CV Research

Large & Diverse Dataset

We offer more than 5000 individual full-body human 3D scans to enable our customers to feed their machine learning models with great diversity and variance in regard to important criteria such as ethnicity, age, clothing and poses.

Metadata Annotations

Our datasets include important and relevant categorization and keyword tagging metadata to allow research scientists to easily create annotated ground truth data to solve computer vision problems with supervised learning.

Authentic Scans

All 3D models are scanned copies of real-world human actors. State-of-the-art scanning technology is used to create realistic human datasets that enable research scientists to generate reliable synthetic data for ML models.

Consistent Data

Our datasets are technically homogeneous to allow fast and uncomplicated batching of data. This means, among many other things, uniform naming conventions, uniform rig hierarchy and uniform texture sizes.

OUR HUMAN DATASETS ARE USED BY THE BEST IN THE CV INDUSTRY

Research Papers

CVPR 2021 POSA: Populating 3D Scenes by Learning Human-Scene Interaction

Mohamed Hassan; Partha Ghosh; Joachim Tesch; Dimitrios Tzionas; Michael J. Black
Max Planck Institute for Intelligent Systems, Tubingen, Germany

CVPR 2021 Learning High Fidelity Depths of Dressed Humans by Watching Social Media Dance Videos

Yasamin Jafarian; Hyun Soo Park
University of Minnesota

CVPR 2021 AGORA: Avatars in Geography Optimized for Regression Analysis

Priyanka Patel; Chun-Hao P. Huang; Joachim Tesch; David T. Hoffmann; Shashank Tripathi; Michael J. Black
Max Planck Institute for Intelligent Systems, Tubingen, Germany; University of Freiburg; Bosch Center for Artificial Intelligence

CVPR 2021 Semi-supervised Synthesis of High-Resolution Editable Textures for 3D Humans

Bindita Chaudhuri; Nikolaos Sarafianos; Linda Shapiro; Tony Tung
University of Washington; Facebook Reality Labs Research, Sausalito

CVPR 2020 ARCH: Animatable Reconstruction of Clothed Humans

Zeng Huang; Yuanlu Xu; Christoph Lassner; Hao Li; Tony Tung
Facebook Reality Labs, Sausalito, USA; University of Southern California, USA

CVPR 2020 PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization

Shunsuke Saito; Tomas Simon; Jason Saragih; Hanbyul Joo
University of Southern California; Facebook Reality Labs; Facebook AI Research

ECCV 2020 SimPose: Effectively Learning DensePose and Surface Normals of People from Simulated Data

Tyler Zhu; Per Karlsson; Christoph Bregler
Google Research

ECCV 2020 BCNet: Learning Body and Cloth Shape from A Single Image

Boyi Jiang; Juyong Zhang; Yang Hong; Jinhao Luo; Ligang Liu; Hujun Bao
University of Science and Technology of China; State Key Lab of CAD&CG, Zhejiang University

CVPR 2019 Learning to Reconstruct People in Clothing from a Single RGB Camera

Thiemo Alldieck; Marcus Magnor; Bharat Lal Bhatnagar; Christian Theobalt; Gerard Pons-Moll
Computer Graphics Lab, TU Braunschweig, Germany; Max Planck Institute for Informatics, Saarland Informatics Campus, Germany

ICCV 2019 PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization

Shunsuke Saito; Zeng Huang; Ryota Natsume; Shigeo Morishima; Angjoo Kanazawa; Hao Li
University of Southern California; USC Institute for Creative Technologies; Waseda University; University of California Berkeley; Pinscreen

Available Datasets

Dataset 01

Posed People

Posed People are lifelike static scans, a 1:1 detailed capture from our scanner including finest details. These 3D models correspond to reality to a very high degree which elevates accuracy and authenticity.

Dataset Details

Size:3350 Single Models
170 Group Models
Individuals / Entities:203 scanned individual people
Clothing Variations:Avg. ~ 16.5 variations per individual

Technical Features

Polycount:100k & 30k resolution (two versions) triangulated
Texture Size:8k & 2k resolution (two versions)
Textures Included:Diffuse map
Normal map
Alpha maps for surface separation
Formats Available:OBJ & FBX
3ds Max
Cinema 4D
Maya
SketchUp
Rhino
Blender

Dataset Diversity (Single Models)

Gender
Female
1958
Male
1402
Age
< 18 yo
387
19-30 yo
1942
31-49 yo
670
50+ yo
336
Ethnicity
Asian
470
Black
530
Caucasian
1969
Other
394

Dataset 02

Rigged People

Rigged People are rigged scans in A or T pose. So they can be put into every possible pose, or they may even be animated. This is ideal for generating the greatest possible pose variation.

Dataset Details

Size:793 single models
Individuals / Entities:129 scanned individual people
Clothing Variations:avg. ~ 6.15 variations per individual

Technical Features

Polycount:~ 10-15k (retopologized quads)
Texture Size:8k resolution
Textures Included:Diffuse map
Normal map
Gloss Map
Alpha maps for surface separation
Formats Available:FBX
3ds Max
Cinema 4D
Maya
UE 4
Unity

Dataset Diversity

Gender
Female
464
Male
329
Age
< 18 yo
105
19-30 yo
497
31-49 yo
142
50+ yo
49
Ethnicity
Asian
142
Black
150
Caucasian
403
Other
99

About HumanDataset

HumanDataset is a service by Renderpeople to meet the needs of the computer vision industry. Since 2013 Renderpeople is one of the world leaders in the production, development and distribution of scanned 3D People models as stock footage.

Renderpeople datasets have already been used for many years in the field of 3D visualization and graphics. Now, they also offer an extreme added value in the field of computer vision research. Numerous internationally renowned companies, research institutes and universities have already worked with Renderpeople datasets to feed machine learning models or simulations with detailed scanned human 3D data to work on difficult CV applications like 3D human perception and reconstruction or pose estimation.

Authentic high-resolution 3D scans help to generate ground truth synthetic training data for machine learning. A modern state-of-the-art 3D dataset fulfills the most important requirements for a successful ML-driven research approach: versatility, volume and quality. In addition, a 3D dataset offers various annotation possibilities for supervised learning that are not feasible with a conventional dataset of 2D imagery.

Contact Us

Feel free to get in touch with us. We are happy to provide you with individual services and advice for your specific concerns and needs.

Newsletter

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Dataset 01 - Posed | Download your preferred file format:

Please note: Unfortunately, a download of sample data on mobile devices is not possible. Please use another device.

3ds Max

Maya

Cinema 4D

Cinema 4D

Sketch Up

OBJ & FBX

Dataset 02 - Rigged | Download your preferred file format:

Please note: Unfortunately, a download of sample data on mobile devices is not possible. Please use another device.

3ds Max

Cinema 4D

Maya

Unreal Engine 4

Unity

FBX

PDF Product Catalogs | Please choose your download:

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