[R] PeopleSansPeople: Unity's Human-Centric Synthetic Data Generator

• PeopleSansPeople

  5 294 3.0 C#

  Unity's privacy-preserving human-centric synthetic data generator

  

Webpage: https://unity-technologies.github.io/PeopleSansPeople/ Paper: https://arxiv.org/abs/2112.09290 Source code: https://github.com/Unity-Technologies/PeopleSansPeople Papers with code: https://paperswithcode.com/paper/peoplesanspeople-a-synthetic-data-generator                                    https://paperswithcode.com/dataset/peoplesanspeople Demo video: https://youtu.be/mQ_DUdB70dc Summary: PeopleSansPeople is a human-centric data generator provided by Unity Technologies that contains highly-parametric and simulation-ready 3D human assets, parameterized lighting and camera system, parameterized environment generators, and fully-manipulable and extensible domain randomizers. PeopleSansPeople can generate RGB images with sub-pixel-perfect 2D/3D bounding box, COCO-compliant human keypoints, and semantic/instance segmentation masks in JSON annotation files. All packaged in macOS and Linux executable binaries capable of generating 1M+ datasets. In addition we release a template Unity environment for lowering the barrier of entry and getting you started with creating your own highly-parameterized human-centric synth data generator. We affectionately named our synthetic data generator PeopleSansPeople, as it is a data generator aimed at human-centric computer vision without using human data which bears serious privacy, safety, ethical, bias, and legal concerns. Benchmarks: The domain randomization we used for our benchmarks are naïve, brute-forced sweeps through the pre-chosen range of parameters; as such we end up generating psychedelic-looking scenes, which turned out to train more performant models for human-centric computer vision.Using PeopleSansPeople we benchmarked a Detectron2 Keypoint R-CNN variant. Results indicate synthetic pre-training with our data outperforms results of training on real data alone or pre-training with ImageNet, both in limited and abundant data regimes.We envisage that this freely-available data generator should enable a wide range of research into the emerging field of simulation to real transfer learning in the critical area of human-centric computer vision.

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