SURREAL (Synthetic Humans for REAL Tasks)

SURREAL (Synthetic hUmans foR REAL tasks) is a large-scale person dataset that generates photorealistic synthetic images with labeling for human part segmentation and depth estimation, producing 6.5M frames in 67.5K short clips (about 100 frames each) of 2.6K action sequences with 145 different synthetic subjects. To ensure realism, the synthetic bodies are created using the SMPL body model, whose parameters are fit by the MoSh method given raw 3D MoCap marker data.

Released in: Learning from Synthetic Humans

Source: arXiv - Synthetic Data for Deep Learning

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Summary

*SURREAL* (Synthetic hUmans foR REAL tasks) is a large-scale person dataset that generates photorealistic synthetic images with labeling for human part segmentation and depth estimation, producing 6.5M frames in 67.5K short clips (about 100 frames each) of 2.6K action sequences with 145 different synthetic subjects. To ensure realism, the synthetic bodies are created using the SMPL body model, whose parameters are fit by the MoSh method given raw 3D MoCap marker data.

6.5M

Images in dataset

2017

Year Released

Key Links & Stats

gulvarol/surreal

SURREAL

Custom - non-commercial scientific research purposes

@INPROCEEDINGS{varol17_surreal, title = {Learning from Synthetic Humans}, author = {Varol, G{\"u}l and Romero, Javier and Martin, Xavier and Mahmood, Naureen and Black, Michael J. and Laptev, Ivan and Schmid, Cordelia}, booktitle = {CVPR}, year = {2017} }

scenebox

Modalities

  1. Still Image

Verticals

  1. Satellite

ML Task

  1. Human Pose Estimation

Related organizations

INRIA

MPI

MPG