facebook/sapiens2-pretrain-5b

Sapiens2-5B

Sapiens2 is a family of high-resolution vision transformers pretrained on 1 billion human images — designed for human-centric tasks such as pose estimation, body-part segmentation, surface normals, and pointmaps.

This repository contains the 5B parameter pretrained backbone. It produces dense per-patch features suitable for fine-tuning downstream task heads.

• —📄 Paper: arXiv:2604.21681
• —🌐 Project Page: rawalkhirodkar.github.io/sapiens2
• —💻 Code: github.com/facebookresearch/sapiens2

Model Details

• —Developed by: Meta
• —Model type: Vision Transformer
• —License: Sapiens2 License
• —Task: pretrain
• —Format: safetensors
• —File: sapiens2_5b_pretrain.safetensors

Quick Start

Install the Sapiens2 repo (pip install -e .).

import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
from sapiens.backbones.standalone.sapiens2 import Sapiens2

# Build the model and load the pretrained checkpoint
model = Sapiens2(arch="sapiens2_5b", img_size=(1024, 768), patch_size=16).eval().cuda()  # img_size is (H, W)
ckpt_path = hf_hub_download(repo_id="facebook/sapiens2-pretrain-5b", filename="sapiens2_5b_pretrain.safetensors")
model.load_state_dict(load_file(ckpt_path))

# 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 features: (B, num_tokens, embed_dim)

Model Card

Sapiens2 Family

See the Sapiens2 Collection for all variants and downstream task checkpoints (pose, segmentation, normals, pointmaps).

Intended Use

• —Feature extraction for human-centric downstream tasks
• —Initialization for fine-tuning task heads (pose, segmentation, normals, pointmap)
• —Research on human-centric vision

License

Released under the Sapiens2 License.

Citation

@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}
}