Update README.md

README.md

@@ -1,3 +1,107 @@
 ---
 license: cc-by-4.0
+task_categories:
+- image-to-image
+tags:
+- Generative Modeling
+- Image Editing
+- Geometric Editing
+- 3D Vision
+size_categories:
+  - 100K<n<1M
+language:
+- en
+configs:
+  - config_name: 100K-Syn
+    data_files:
+      - split: train
+        path: data/100K-Syn/*.tar
+  - config_name: 10K-Objectron
+    data_files:
+      - split: train
+        path: data/10K-Objectron/*.tar
+  - config_name: 10K-Syn
+    data_files:
+      - split: train
+        path: data/10K-Syn/*.tar
 ---
+
+# Thinking-In-Boxes: 3D Editing in Real Images Made Easy
+
+[🌐Project Page](https://thinking-in-boxes.github.io/) | [📄arXiv](https://arxiv.org/abs/2606.20556) | [🎨Code (Coming Soon)](#)
+
+This is the training dataset used in the paper **Thinking In Boxes: 3D Editing in Real Images Made Easy**
+
+Thinking-In-Boxes is an Image-to-Image Generative model for Geometric Image Editing. 
+This dataset consists of images of 1-2 object scenes placed on a floor and rendered from two viewpoints. 
+In addition, it includes our scene representation where objects as represented as 3D Coloured Boxes placed on a shaded floor, which disambiguates object and camera transformations.
+
+## Dataset Structure
+
+This dataset contains three independent subsets, each stored as WebDataset `.tar` shards:
+
+| Subset | Folder | Approx. size | Description |
+|---|---|---|---|
+| 100K-Syn | `data/100K-Syn/` | 100,000 scenes | Synthetic 2-object scenes used in Stage-1 finetuning |
+| 10K-Objectron | `data/10K-Objectron/` | 10,000 scenes | Real-world Objectron-derived scenes used in Stage-2 finetuning |
+| 10K-Syn | `data/10K-Syn/` | 10,000 scenes | Synthetic 2-object scenes used in Stage-2 finetuning |
+
+Each "scene" (sample) contains 4 files:
+
+- `bbox_0.png`, `bbox_1.png` — scene representations for source and target configurations.
+- `rgb_0.png`, `rgb_1.png` — RGB renders representing source and target configurations.
+
+**Note**: The *.png files for the 100K-Syn and 10K-Syn subsets are in 512x512 resolution, and 1440x1920 resolution for the 10K-Objectron subset.
+
+## Usage
+
+Each subset is loaded independently via `data_dir` (all share the same `train` split label — `data_dir` is what distinguishes them, not `split`):
+
+```python
+from datasets import load_dataset
+
+ds_100K_Syn = load_dataset("pradhaansbhat/Thinking-In-Boxes", data_dir="data/100K-Syn", split="train")
+ds_10K_Objectron = load_dataset("pradhaansbhat/Thinking-In-Boxes", data_dir="data/10K-Objectron", split="train")
+
+print(ds_100K_Syn[0].keys())
+# dict_keys(['bbox_0.png', 'bbox_1.png', 'rgb_0.png', 'rgb_1.png', '__key__', '__url__'])
+```
+
+Alternatively, using the named configs defined above:
+
+```python
+from datasets import load_dataset
+
+ds_100K_Syn = load_dataset("pradhaansbhat/Thinking-In-Boxes", "100K-Syn", split="train")
+ds_10K_Objectron = load_dataset("pradhaansbhat/Thinking-In-Boxes", "10K-Objectron", split="train")
+ds_10K_Syn = load_dataset("pradhaansbhat/Thinking-In-Boxes", "10K-Objectron", split="train")
+```
+
+### Merging subsets for training
+
+```python
+from datasets import concatenate_datasets
+from torch.utils.data import DataLoader
+
+merged = concatenate_datasets([ds_10K_Objectron, ds_10K_Syn])  # e.g. combine the two 10K sets
+loader = DataLoader(merged, batch_size=2, shuffle=True, num_workers=8)
+```
+
+**Note**: `__key__` values (e.g. `scene_000000`) are unique within each subset but **not** guaranteed unique across subsets after merging. This has no effect on training but is worth knowing if you rely on `__key__` for deduplication or lookups across merged data.
+
+## Citation
+
+If you find our work useful, please consider citing:
+
+```bibtex
+@misc{bhat2026thinkingboxes3dediting,
+  title         = {Thinking in Boxes: 3D Editing in Real Images Made Easy},
+  author        = {Pradhaan S Bhat and Naveen Chandra R and Rishubh Parihar and Vaibhav Vavilala and R. Venkatesh Babu and D. A. Forsyth and Anand Bhattad},
+  year          = {2026},
+  eprint        = {2606.20556},
+  archivePrefix = {arXiv},
+  primaryClass  = {cs.CV},
+  url           = {https://arxiv.org/abs/2606.20556}
+}
+```
+---