Rawal Khirodkar · He Wen · Julieta Martinez · Yuan Dong · Su Zhaoen · Shunsuke Saito
A family of high-resolution transformers pretrained on 1 billion human images, achieving state-of-the-art performance across diverse human-centric tasks — pose estimation, body-part segmentation, surface normals, pointmaps, and human matting.
🤗 Demos: Pose · Seg · Normal · Pointmap · Matting
- May 15, 2026: Sapiens2-1B human matting model is released.
- April 24, 2026: Initial Sapiens2 release — pose, body-part segmentation, surface normals, and pointmaps.
Run a pretrained backbone forward pass — only torch and safetensors needed:
import os
import torch
from safetensors.torch import load_file
from sapiens.backbones.standalone.sapiens2 import Sapiens2
# Build the model and load a pretrained checkpoint
model = Sapiens2(arch="sapiens2_1b", img_size=(1024, 768), patch_size=16).eval().cuda() # img_size is (H, W)
ckpt = os.path.expanduser("~/sapiens2_host/pretrain/sapiens2_1b_pretrain.safetensors")
model.load_state_dict(load_file(ckpt))
# Forward pass on a single image (RGB; ImageNet normalization recommended)
x = torch.randn(1, 3, 1024, 768).cuda()
with torch.no_grad():
features = model(x)[0] # dense backbone featuresThe Quick Start snippet above imports from a single self-contained file — torch (plus safetensors for checkpoint loading) is all you need. Drop the file into your project and you're done:
curl -O https://raw.githubusercontent.com/facebookresearch/sapiens2/main/sapiens/backbones/standalone/sapiens2.pyFor Sapiens v1, grab sapiens.py instead.
| Model | Params | FLOPs | Embed dim | Layers | Heads |
|---|---|---|---|---|---|
| Sapiens2-0.1B | 0.114 B | 0.342 T | 768 | 12 | 12 |
| Sapiens2-0.4B | 0.398 B | 1.260 T | 1024 | 24 | 16 |
| Sapiens2-0.8B | 0.818 B | 2.592 T | 1280 | 32 | 16 |
| Sapiens2-1B | 1.462 B | 4.715 T | 1536 | 40 | 24 |
| Sapiens2-1B (4K) | 1.607 B | — | 1536 | 40 | 24 |
| Sapiens2-5B | 5.071 B | 15.722 T | 2432 | 56 | 32 |
All models use patch size 16 and are trained at 1024×768 (H×W) resolution, except Sapiens2-1B (4K) which is trained at 4096×3072 with use_tokenizer=True.
Clone the repository:
git clone https://github.com/facebookresearch/sapiens2.git
cd sapiens2
export SAPIENS_ROOT=$(pwd)Install (requires Python ≥3.12 and PyTorch ≥2.7):
pip install -e .Download checkpoints from MODEL_ZOO.md. Place downloaded files under $SAPIENS_CHECKPOINT_ROOT (default: ~/sapiens2_host):
sapiens2_host/
├── pretrain/
│ ├── sapiens2_{0.1b,0.4b,0.8b,1b,5b}_pretrain.safetensors
│ └── sapiens2_1b_4k_pretrain.safetensors
├── pose/
│ └── sapiens2_{0.4b,0.8b,1b,5b}_pose.safetensors
├── seg/
│ └── sapiens2_{0.4b,0.8b,1b,5b}_seg.safetensors
├── normal/
│ └── sapiens2_{0.4b,0.8b,1b,5b}_normal.safetensors
├── pointmap/
│ └── sapiens2_{0.4b,0.8b,1b,5b}_pointmap.safetensors
├── matting/
│ └── sapiens2_1b_matting.safetensors
└── detector/ # [optional] only needed for pose inference
└── detr-resnet-101-dc5/
| Task | Description | Inference | Train |
|---|---|---|---|
| Pose Estimation | 308 whole-body keypoints | docs/POSE.md | docs/train/POSE.md |
| Body-Part Segmentation | 29 body parts | docs/SEG.md | docs/train/SEG.md |
| Surface Normal Estimation | per-pixel normals | docs/NORMAL.md | docs/train/NORMAL.md |
| Pointmap Estimation | per-pixel 3D points | docs/POINTMAP.md | docs/train/POINTMAP.md |
| Human Matting | alpha matte + foreground | docs/MATTING.md | docs/train/MATTING.md |
We would like to acknowledge the contributions of DINOv3, OpenMMLab, and Accelerate, which this project benefits from.
For questions or issues, please open an issue on GitHub. See CONTRIBUTING and the Code of Conduct.
This project is licensed under the Sapiens2 License.
If you use Sapiens2 in your research, please consider citing us.
@article{khirodkarsapiens2,
title={Sapiens2},
author={Khirodkar, Rawal and Wen, He and Martinez, Julieta and Dong, Yuan and Su, Zhaoen and Saito, Shunsuke},
journal={arXiv preprint arXiv:2604.21681},
year={2026}
}