Add model card with metadata (#1)

- Add model card with metadata (b2bb224fb94313ff6db360340a2b583ebf2bb2e3)


Co-authored-by: Niels Rogge <[email protected]>

README.md

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+---
+pipeline_tag: image-to-3d
+library_name: pytorch
+license: apache-2.0
+---
+
+# FLARE: Feed-forward Geometry, Appearance and Camera Estimation from Uncalibrated Sparse Views
+
+[![Website](https://img.shields.io/website-up-down-green-red/http/shields.io.svg)](https://zhanghe3z.github.io/FLARE/)
+[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97-Hugging%20Face-yellow)](https://huggingface.co/AntResearch/FLARE)
+[![Video](https://img.shields.io/badge/Video-Demo-red)](https://zhanghe3z.github.io/FLARE/videos/teaser_video.mp4)
+
+This repository contains the FLARE model, as presented in [FLARE: Feed-forward Geometry, Appearance and Camera Estimation from Uncalibrated Sparse Views](https://hf.co/papers/2502.12138). FLARE is a feed-forward model that estimates high-quality camera poses, 3D geometry, and appearance from as few as 2-8 uncalibrated images.
+
+Project Page: https://zhanghe3z.github.io/FLARE/
+
+## Run a Demo (Point Cloud and Camera Pose Estimation)
+
+To run a demo, follow these steps:
+
+1. **Install Dependencies:**  Ensure you have PyTorch and other necessary libraries installed as detailed in the [installation instructions](https://github.com/zhanghe3z/FLARE#installation).
+2. **Download Checkpoint:** Download the checkpoint from [Hugging Face](https://huggingface.co/AntResearch/FLARE/blob/main/geometry_pose.pth) and place it in the `/checkpoints/geometry_pose.pth` directory.
+3. **Run the Script:** Execute the following command, replacing `"Your/Data/Path"` and `"Your/Checkpoint/Path"` with the appropriate paths:
+
+```bash
+torchrun --nproc_per_node=1 run_pose_pointcloud.py \
+    --test_dataset "1 @ CustomDataset(split='train', ROOT='Your/Data/Path', resolution=(512,384), seed=1, num_views=8, gt_num_image=0, aug_portrait_or_landscape=False, sequential_input=False)" \
+    --model "AsymmetricMASt3R(pos_embed='RoPE100', patch_embed_cls='ManyAR_PatchEmbed', img_size=(512, 512), head_type='catmlp+dpt', output_mode='pts3d+desc24', depth_mode=('exp', -inf, inf), conf_mode=('exp', 1, inf), enc_embed_dim=1024, enc_depth=24, enc_num_heads=16, dec_embed_dim=768, dec_depth=12, dec_num_heads=12, two_confs=True, desc_conf_mode=('exp', 0, inf))" \
+    --pretrained "Your/Checkpoint/Path" \
+    --test_criterion "MeshOutput(sam=False)" --output_dir "log/" --amp 1 --seed 1 --num_workers 0
+```
+
+## Visualization
+
+After running the demo, you can visualize the results using the following command:
+
+```bash
+sh ./visualizer/vis.sh 
+```
+
+This will run a visualization script.  Refer to the Github README for more details on visualization options.
+
+## Citation
+
+```bibtex
+@misc{zhang2025flarefeedforwardgeometryappearance,
+      title={FLARE: Feed-forward Geometry, Appearance and Camera Estimation from Uncalibrated Sparse Views}, 
+      author={Shangzhan Zhang and Jianyuan Wang and Yinghao Xu and Nan Xue and Christian Rupprecht and Xiaowei Zhou and Yujun Shen and Gordon Wetzstein},
+      year={2025},
+      eprint={2502.12138},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2502.12138}, 
+}
+```