Sapiens
Collection
Foundation models for human tasks. Code: https://github.com/facebookresearch/sapiens
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69 items
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Updated
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19
Model Details
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. 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 parameters from 0.3 to 2 billion. Sapiens consistently surpasses existing baselines across various human-centric benchmarks.
Model Description
- Developed by: Meta
- Model type: Vision Transformers
- License: Creative Commons Attribution-NonCommercial 4.0
More Resources
- Repository: https://github.com/facebookresearch/sapiens
- Paper: https://arxiv.org/abs/2408.12569
- Demos: Sapiens Gradio Spaces
- Project Page: https://about.meta.com/realitylabs/codecavatars/sapiens
- Additional Results: https://rawalkhirodkar.github.io/sapiens
- HuggingFace Collection: https://huggingface.co/collections/facebook/sapiens-66d22047daa6402d565cb2fc
Uses
- pose estimation (keypoints 17, keypoints 133, keypoints 308)
- body-part segmentation (28 classes)
- depth estimation
- surface normal estimation
Model Zoo
Note: This repository does not host any checkpoints but contains links to all the model repositories.
We provide checkpoints in three formats:
- original: weights can be finetuned for your use case along with inference.
- torchscript: (inference only) weights ported to torchscript.
- bfloat16: (inference only) for large scale processing, weights ported to bfloat16 (A100 gpu only + pytorch-2.3).
| Model Name | Original | TorchScript | BFloat16 |
|---|---|---|---|
| sapiens-pretrain-0.3b | link | link | link |
| sapiens-pretrain-0.6b | link | link | link |
| sapiens-pretrain-1b | link | link | link |
| sapiens-pretrain-2b | link | link | link |
| sapiens-pose-0.3b | link | link | link |
| sapiens-pose-0.6b | link | link | link |
| sapiens-pose-1b | link | link | link |
| sapiens-seg-0.3b | link | link | link |
| sapiens-seg-0.6b | link | link | link |
| sapiens-seg-1b | link | link | link |
| sapiens-seg-2b | link | link | link |
| sapiens-depth-0.3b | link | link | link |
| sapiens-depth-0.6b | link | link | link |
| sapiens-depth-1b | link | link | link |
| sapiens-depth-2b | link | link | link |
| sapiens-normal-0.3b | link | link | link |
| sapiens-normal-0.6b | link | link | link |
| sapiens-normal-1b | link | link | link |
| sapiens-normal-2b | link | link | link |
Helper models for bounding box detection or background removal.