Sapiens2-5B-Surface
Per-pixel surface-normal estimation (3-channel unit vectors in camera frame).
This repository contains the 5B Surface Normal Estimation checkpoint, finetuned from the Sapiens2-5B pretrained backbone.
- 📄 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: normal
- Base model: facebook/sapiens2-pretrain-5b
- Format: safetensors
- File:
sapiens2_5b_normal.safetensors
Quick Start
Install the Sapiens2 repo (pip install -e .), download the checkpoint, and run the demo:
# 1. Download the checkpoint to $SAPIENS_CHECKPOINT_ROOT/normal/
hf download facebook/sapiens2-normal-5b sapiens2_5b_normal.safetensors \
--local-dir ~/sapiens2_host/normal
# 2. Run the demo (edit INPUT, OUTPUT, and MODEL_NAME inside the script)
cd $SAPIENS_ROOT/sapiens/dense
./scripts/demo/normal.sh
See the Surface Normal Estimation guide for details on inputs, outputs, and visualization options.
Model Card
| Field | Value |
|---|---|
| Architecture | Sapiens2 ViT backbone + Surface Normal Estimation head |
| Backbone parameters | 5.071 B |
| Backbone FLOPs | 15.722 T |
| Embedding dim | 2432 |
| Layers | 56 |
| Attention heads | 32 |
| Inference resolution | 1024 × 768 (H × W) |
| Patch size | 16 |
Sapiens2-Surface Family
| Model | Params | FLOPs | Embed dim | Layers | Heads |
|---|---|---|---|---|---|
| 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 |
| Sapiens2-5B (this) | 5.071 B | 15.722 T | 2432 | 56 | 32 |
See the Sapiens2 Collection for all variants and other downstream task checkpoints.
Intended Use
- Surface Normal Estimation on human-centric imagery
- 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}
}