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FLARE

This repository contains the FLARE model, as presented in FLARE: Feed-forward Geometry, Appearance and Camera Estimation from Uncalibrated Sparse Views. FLARE is a feed-forward model that estimates high-quality...

Fuentehuggingface.co/AntResearch/FLARE/tree/586c455b70f32c56a4c98d805f83871c2fbaf5f3
TipoOtherVersiónRevision 586c455Ccalidad

Fuente del modelo

Tipo
Other
Tamaño del archivo
5,30 GB
Versión
vRevision 586c455
Actualizado
8 oct 2026
Descargas
0
Fuente
Hugging Face

Descripción de la fuente

FLARE: Feed-forward Geometry, Appearance and Camera Estimation from Uncalibrated Sparse Views

Website Hugging Face Video

This repository contains the FLARE model, as presented in FLARE: Feed-forward Geometry, Appearance and Camera Estimation from Uncalibrated Sparse Views. 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.
  2. Download Checkpoint: Download the checkpoint from Hugging Face 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:
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:

sh ./visualizer/vis.sh

This will run a visualization script. Refer to the Github README for more details on visualization options.

Citation

@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}, 
}

Fuentes

1 fuente
Hugging FacePrincipalVersión 586c455b70f32c56a4c98d805f83871c2fbaf5f3
huggingface.co/AntResearch/FLARE/tree/586c455b70f32c56a4c98d805f83871c2fbaf5f3

Verificado 25 ago

Artefactos del modelo

1 artefacto

geometry_pose.pth

pth · 5,30 GB · SHA-256 03d46e974cd1…95b7 · Hugging Face

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Extractos de fuentes

2 extractos
Readme25 agohuggingface.co/AntResearch/FLARE

--- 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 c...

Note25 agohuggingface.co/AntResearch/FLARE

Source context: 0 downloads · 1 likes · Pipeline image-to-3d · Library pytorch · Repo AntResearch/FLARE