Sapiens: Foundation for Human Vision Models
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.
66c7f3b3ab23d3d7df974707
Rawal Khirodkar
admin_assigned
2024-08-30T13:10:09.902Z
6413702057d68cf865da8314
/avatars/332e72602a4c6ac0a997b295bb9b2f7d.svg
Rawal Khirodkar
user
rawalkhirodkar
66c7f3b3ab23d3d7df974708
Timur Bagautdinov
admin_assigned
2024-08-30T13:10:16.104Z
63be0fe19a15a3e9419d3d7d
/avatars/145760938083ec031e30d20d321d9185.svg
Timur Bagautdinov
user
psycharo
66c7f3b3ab23d3d7df974709
Julieta Martinez
66c7f3b3ab23d3d7df97470a
Su Zhaoen
66c7f3b3ab23d3d7df97470b
Austin James
66c7f3b3ab23d3d7df97470c
Peter Selednik
66c7f3b3ab23d3d7df97470d
Stuart Anderson
66c7f3b3ab23d3d7df97470e
Shunsuke Saito
claimed_verified
2024-08-26T07:15:45.623Z
630688edd37ce67e0e50e548
/avatars/1681e465d7649f67f94e1b69d236cb1e.svg
Shunsuke Saito
user
psyth
Sapiens: Foundation for Human Vision Models
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.
Sapiens, a family of vision models for human-centric tasks, achieves superior performance with self-supervised pretraining and can be easily fine-tuned for 2D pose estimation, body-part segmentation, depth estimation, and surface normal prediction.
self-supervised pretraining model adaptation fine-tuning high-resolution inference generalization parameter scaling human-centric tasks 2D pose estimation body-part segmentation depth estimation surface normal prediction Humans-5K Humans-2K Hi4D THuman2