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+<h1 align="center">PICS: Pairwise Image Compositing with Spatial Interactions</h1>
+<p align="center"><img src="assets/figure.jpg" width="100%"></p>
+
+***Check out our [Project Page](https://ryanhangzhou.github.io/pics/) for more visual demos!***
+
+<!-- Updates -->
+## ⏩ Updates
+
+**02/08/2026**
+- Release training and inference code.
+- Release training data.
+
+**03/01/2025**
+- Release checkpoints. 
+
+<!-- TODO List -->
+## 🚧 TODO List
+- [x] Release training and inference code for pairwise image compositing
+- [x] Release datasets (LVIS, Objects365, etc. in WebDataset format)
+- [x] Release pretrained models
+- [ ] Release any-object compositing code
+
+<!-- Installation -->
+## 📦 Installation
+
+### Prerequisites
+- **OS**: Linux (Tested on Ubuntu 20.04/22.04).
+- **Python**: 3.10 or higher.
+- **Package Manager**: [Conda](https://docs.anaconda.com/miniconda/install/#quick-command-line-install) is recommended.   
+
+**Hardware Requirements**
+| Stage | GPU (VRAM) | System RAM | Batch Size |
+| --- | --- | --- | --- |
+| Training | NVIDIA H100 (80GB) | 120GB | 16 |
+| Inference | NVIDIA RTX A6000 (48GB) | 64GB | 1 |
+
+### Environment setup
+  Create a new conda environment named `PICS` and install the dependencies: 
+  ```
+  conda env create --file=PICS.yml
+  conda activate PICS
+  ```
+
+### Weights preparation
+***DINOv2***: Download [ViT-g/14](https://dl.fbaipublicfiles.com/dinov2/dinov2_vitg14/dinov2_vitg14_pretrain.pth) and place it at: checkpoints/dinov2_vitg14_pretrain.pth
+
+<!-- Pretrained Models -->
+## 🤖 Pretrained Models
+<!-- Coming soon! We are currently finalizing the model weights for public release. -->
+We provide the following pretrained models (to be placed at the same directory with DINOv2):
+
+| Model | Description | size | Download |
+| --- | --- | --- | --- |
+| PICS | Full model | 18.45GB | [Download](https://drive.google.com/file/d/17JpvhRvHFjfqQDiV9RFfgjGa0iLropXK/view?usp=sharing) |
+
+
+## Minimal Example for Inference
+
+Here is an [example](run_test.py) of how to use the pretrained models for pairwise image compositing.
+Run two-object compositing mode: 
+```
+python run_test.py \
+    --input "sample" \
+    --output "results/sample" \
+    --obj_thr 2
+```
+
+
+<!-- Dataset -->
+## 📚 Dataset
+Our training set is a mixture of [LVIS](https://www.lvisdataset.org/), [VITON-HD](https://www.kaggle.com/datasets/marquis03/high-resolution-viton-zalando-dataset), [Objects365](https://www.objects365.org/overview.html), [Cityscapes](https://www.cityscapes-dataset.com/), [Mapillary Vistas](https://www.mapillary.com/dataset/vistas) and [BDD100K](https://bair.berkeley.edu/blog/2018/05/30/bdd/). 
+We provide the processed ***two-object compositing data*** in WebDataset format (.tar shards) below:
+| Model | #Sample | Size | Download |
+| --- | --- | --- | --- |
+| LVIS | 34,160 | 7.98GB | [Download](https://drive.google.com/drive/folders/1Ir1cwR7K8HALNJiS6kTTlMgKIn8f18XX?usp=sharing) |
+| VITON-HD | 11,647 | 2.53GB | [Download](https://drive.google.com/drive/folders/1317fJvvc7J1OTdbiM_Rst0C9AewIcNr2?usp=sharing) |
+| Objects365 | 940,764 | 243GB | [Download](https://drive.google.com/drive/folders/1xKLoGv8e5wkGkjdxEGpz5i9TH08vd1AA?usp=sharing) |
+| Cityscapes | 536 | 1.21GB | [Download](https://drive.google.com/drive/folders/1HYgEgZcknvEMbK2XZf2isY0pYcluGoKU?usp=sharing) |
+| Mapillary Vistas | 603 | 582MB | [Download](https://drive.google.com/drive/folders/1a0756wc2bvvHJ_8a01N0tZ_Kb_BkRZv1?usp=sharing) |
+| BDD100K | 1,012 | 204MB | [Download](https://drive.google.com/drive/folders/1zS60KPfZioU4tW1ngDK1KahE7T-TeIim?usp=sharing) |
+
+### Data organization
+```
+PICS/
+├── data/
+    ├── train/
+        ├── LVIS/
+            ├── 00000.tar
+            ├── ...
+        ├── VITONHD/
+        ├── Objects365/
+        ├── Cityscapes/
+        ├── MapillaryVistas/
+        ├── BDD100K/
+```
+
+### Data preparation instruction
+We provide a script using SAM to extract high-quality object silhouettes for the Objects365 dataset.
+To process a specific range of data shards, run:
+```
+python scripts/annotate_sam.py --is_train --index_low 00000 --index_high 10000
+```
+To process raw data (e.g., LVIS), run the following command. Replace /path/to/raw_data with your actual local data path:
+```
+python -m datasets.lvis \
+    --dataset_dir "/path/to/raw_data" \
+    --construct_dataset_dir "data/train/LVIS" \
+    --area_ratio 0.02 \
+    --is_build_data \
+    --is_train
+```
+
+## Training
+
+To train a model on the whole dataset:
+```
+python run_train.py \
+    --root_dir 'LOGS/whole_data' \
+    --batch_size 16 \
+    --logger_freq 1000 \
+    --is_joint
+```
+
+
+<!-- License -->
+## ⚖️ License
+
+This project is licensed under the terms of the MIT license.
+
+
+
+<!-- Citation -->
+<!-- ## 📜 Citation -->
+
+<!-- If you find this work helpful, please consider citing our paper: -->
+
+<!-- ```bibtex
+@inproceedings{zhou2025bootplace,
+  title={BOOTPLACE: Bootstrapped Object Placement with Detection Transformers},
+  author={Zhou, Hang and Zuo, Xinxin and Ma, Rui and Cheng, Li},
+  booktitle={Proceedings of the Computer Vision and Pattern Recognition Conference},
+  pages={19294--19303},
+  year={2025}
+}
+``` -->
+
+## 🙌 Acknowledgements
+We would like to thank the contributors to the [AnyDoor](https://huggingface.co/papers/2307.09481) repository for their open research.
+
+## Contact Us
+For any inquiries, feel free to open a GitHub issue or reach out via email.