Sapiens: Foundation for Human Vision Models
Authors: name: Rawal Khirodkar, name: Timur Bagautdinov, name: Julieta Martinez, name: Su Zhaoen, name: Austin James, name: Peter Selednik, name: Stuart Anderson, name: Shunsuke Saito

We present Sapiens, a family of models for four fundamental human-centric
vision tasks - 2D pose estimation, body-part segmentation, depth estimation,
and surface normal prediction. Our models natively support 1K high-resolution
inference and are extremely easy to adapt for individual tasks by simply
fine-tuning models pretrained on over 300 million in-the-wild human images. We
observe that, given the same computational budget, self-supervised pretraining
on a curated dataset of human images significantly boosts the performance for a
diverse set of human-centric tasks. The resulting models exhibit remarkable
generalization to in-the-wild data, even when labeled data is scarce or
entirely synthetic. Our simple model design also brings scalability - model
performance across tasks improves as we scale the number of parameters from 0.3
to 2 billion. Sapiens consistently surpasses existing baselines across various
human-centric benchmarks. We achieve significant improvements over the prior
state-of-the-art on Humans-5K (pose) by 7.6 mAP, Humans-2K (part-seg) by 17.1
mIoU, Hi4D (depth) by 22.4% relative RMSE, and THuman2 (normal) by 53.5%
relative angular error.