Human 3D models for CV, ML and Sim

The human data solution by Renderpeople to create ground truth synthetic data for machine learning and simulation.

a versatile human data solution for CV, ML & Sim

What we have to offer? A lot!

We offer 3D models and corresponding data based on an ever growing library of human full body scans. To date, we have already scanned 273 unique individuals, more every week. Per person we capture an average of over 30 unique scans. That's more than 10,000 unique scans in our database.

All Rights You Need

We operate 100% GDPR compliant and have explicit consent to use all data for applications like ML in accordance with Art.6 Sect.1 and Art.9 Sect.2 GDPR. This includes personal and health metadata.

3D Labels

We annotate our 3D models with all body markers you need.


We offer ID and grayscale masks of all individual body areas for surface and material separation.

Body Shape Scans

For every individual in our dataset, we capture a neutral A-Pose in tight body shape emphasizing clothes. In addition to this, there is a hairnet variation, too. These scans are ideal for measuring body dimensions or applying 3D body models like SMPL.

Every Angle

For every scan captured, we offer a set of up to 335 multi view images as .JPG or .RAW including the corresponding camera EXIF data and camera parameters like location, orientation, etc. This is a total of 6500+ Mpx resolution of imagery per scan.

More Variety

For every single person we not only capture dozens of poses, but also 3 different and authentic everyday outfits. These outfits can be further augmented by performing color transformations. Moreover, we provide detailed metadata (like piece of clothing and colors) for each outfit. Due to this variety of options, synthetic data can be augmented almost infinitely.

in Everyday Clothes

We capture each person in each of their clothing variations in as neutral a sitting, standing, and walking pose as possible, as well as in an A-Pose. That is, in addition to all our other 'everyday' scans, there is a set of comparable poses without objects among all individuals and clothing variants.

Realism Matters

All 3D models that we have processed into finished static or rigged products come with a set of PBR textures. We can provide BaseColor, Normals, Roughness, Metallness, AO, Curvature, and more on request.

Body Measurements

We provide you with accurate body measurement data for each individual. E.g. arm length, chest circumference, wingspan, head circumference, foot length, and more. For this we use our body shape scans.

Synthetic Human Data For CV, ML & Sim

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.


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.


Research Papers

CVPR 2022 gDNA: Towards Generative Detailed Neural Avatars

Xu Chen; Tianjian Jiang; Jie Song; Jinlong Yang; Michael J. Black; Andreas Geiger; Otmar Hilliges
ETH Zürich; University of Tübingen; Max Planck Institute for Intelligent Systems, Tübingen

CVPR 2022 Photorealistic Monocular 3D Reconstruction of Humans Wearing Clothing

Thiemo Alldieck; Mihai Zanfir; Cristian Sminchisescu
Google Research

PUBLIC 2021 PeopleSansPeople: A Synthetic Data Generator for Human-Centric Computer Vision

Salehe Erfanian Ebadi; You-Cyuan Jhang; Alex Zook; Saurav Dhakad; Adam Crespi; Pete Parisi; Steven Borkman; Jonathan Hogins; Sujoy Ganguly
Unity Technologies

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

Dataset Diversity (Single Models)

< 18 yo
19-30 yo
31-49 yo
50+ yo

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
UE 4

Dataset Diversity

< 18 yo
19-30 yo
31-49 yo
50+ yo

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.


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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


Cinema 4D

Cinema 4D

Sketch Up


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


Unreal Engine 4



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