Datasets:

pradhaansbhat
/

Thinking-In-Boxes

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 | 📄arXiv | 🎨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):

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:

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

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:

@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}
}