Add design file

README.md

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-# 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)
-
-Official implementation of **FLARE** (CVPR 2025) - a feed-forward model for joint camera pose estimation, 3D reconstruction and novel view synthesis from sparse uncalibrated views.
-
-![Teaser Video](./assets/teaser.jpg)
-
-
-<!-- TOC start (generated with https://github.com/derlin/bitdowntoc) -->
-
-- [📖 Overview](#-overview)
-- [🛠️ TODO List](#-todo-list)
-- [🌍 Installation](#-installation)
-- [💿 Checkpoints](#-checkpoints)
-- [🎯 Run a Demo (Point Cloud and Camera Pose Estimation) ](#-run-a-demo-point-cloud-and-camera-pose-estimation)
-- [👀 Visualization ](#-visualization)
-- [📜 Citation ](#-citation)
-
-<!-- TOC end -->
-
-## 📖 Overview
-We present FLARE, a feed-forward model that simultaneously estimates high-quality camera poses, 3D geometry, and appearance from as few as 2-8 uncalibrated images. Our cascaded learning paradigm:
-
-1. **Camera Pose Estimation**: Directly regress camera poses without bundle adjustment
-2. **Geometry Reconstruction**: Decompose geometry reconstruction into two simpler sub-problems
-3. **Appearance Modeling**: Enable photorealistic novel view synthesis via 3D Gaussians
-
-Achieves SOTA performance with inference times <0.5 seconds!
-
-## 🛠️ TODO List
-- [x] Release point cloud and camera pose estimation code.
-- [x] Updated Gradio demo (app.py).
-- [ ] Release novel view synthesis code. (~2 weeks)
-- [ ] Release evaluation code. (~2 weeks)
-- [ ] Release training code.
-- [ ] Release data processing code.
-
-## 🌍 Installation
-
-```
-conda create -n flare python=3.8
-conda activate flare 
-conda install pytorch torchvision pytorch-cuda=12.1 -c pytorch -c nvidia  # use the correct version of cuda for your system
-pip install -r requirements.txt
-conda uninstall ffmpeg  
-conda install -c conda-forge ffmpeg
-```
-
-
-## 💿 Checkpoints
-Download the checkpoint from [huggingface](https://huggingface.co/AntResearch/FLARE/blob/main/geometry_pose.pth) and place it in the /checkpoints/geometry_pose.pth directory.
-
-## 🎯 Run a Demo (Point Cloud and Camera Pose Estimation) 
-
-
-```bash
-sh scripts/run_pose_pointcloud.sh
-```
-
-
-```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=7, 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
-```
-
-**To run the demo using ground truth camera poses:**
-Enable the wpose=True flag in both the CustomDataset and AsymmetricMASt3R. An example script demonstrating this setup is provided in run_pose_pointcloud_wpose.sh.
-
-```bash
-sh scripts/run_pose_pointcloud_wpose.sh
-```
-
-## 👀 Visualization 
-
-```
-sh ./visualizer/vis.sh
-```
- 
-
-```
-CUDA_VISIBLE_DEVICES=0 python visualizer/run_vis.py --result_npz data/mesh/IMG_1511.HEIC.JPG.JPG/pred.npz --results_folder data/mesh/IMG_1511.HEIC.JPG.JPG/
-``` 
-
-
-## 📜 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}, 
-}
+title: FLARE
+emoji: 🔥
+colorFrom: indigo
+colorTo: red
+sdk: gradio
+sdk_version: 4.38.1
+app_file: app.py
+pinned: false
+models:
+  - zhang3z/FLARE