  In recent years, person detection and human pose estimation have made great
strides, helped by large-scale labeled datasets. However, these datasets had no
guarantees or analysis of human activities, poses, or context diversity.
Additionally, privacy, legal, safety, and ethical concerns may limit the
ability to collect more human data. An emerging alternative to real-world data
that alleviates some of these issues is synthetic data. However, creation of
synthetic data generators is incredibly challenging and prevents researchers
from exploring their usefulness. Therefore, we release a human-centric
synthetic data generator PeopleSansPeople which contains simulation-ready 3D
human assets, a parameterized lighting and camera system, and generates 2D and
3D bounding box, instance and semantic segmentation, and COCO pose labels.
Using PeopleSansPeople, we performed benchmark synthetic data training using a
Detectron2 Keypoint R-CNN variant [1]. We found that pre-training a network
using synthetic data and fine-tuning on various sizes of real-world data
resulted in a keypoint AP increase of $+38.03$ ($44.43 \pm 0.17$ vs. $6.40$)
for few-shot transfer (limited subsets of COCO-person train [2]), and an
increase of $+1.47$ ($63.47 \pm 0.19$ vs. $62.00$) for abundant real data
regimes, outperforming models trained with the same real data alone. We also
found that our models outperformed those pre-trained with ImageNet with a
keypoint AP increase of $+22.53$ ($44.43 \pm 0.17$ vs. $21.90$) for few-shot
transfer and $+1.07$ ($63.47 \pm 0.19$ vs. $62.40$) for abundant real data
regimes. 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.
