README.md

---
license: cc-by-nc-4.0
language:
- en
tags:
- shadow
- controllable
- synthetic
pretty_name: Controllable shadow generation benchmark
size_categories:
- 1K<n<10K
---

# Overview

This is the public synthetic test set for controllable shadow generation created by Jasper Research Team. The project page for the research introduced this dataset is available at [this link](https://gojasper.github.io/controllable-shadow-generation-project/).
We created this dataset using [Blender](https://www.blender.org/). It has 3 tracks: softness control, horizontal direction control and vertical direction control. 

Example renders from the dataset below: 

## Softness control:
![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/6368c710e68400b192fffb9d/Hl0qJ3onj2Ip8az0GGJgb.jpeg)

## Horizontal direction control:
![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/6368c710e68400b192fffb9d/G9t0BQ1AwQF_xcawPZSgO.jpeg)

## Vertical direction control:
![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/6368c710e68400b192fffb9d/UnkTWbftwMlTdoECQRWsW.jpeg)

# Usage 
The dataset is formatted to be used with [WebDataset](https://huggingface.co/docs/hub/datasets-webdataset).

```python
import matplotlib.pyplot as plt 
import webdataset as wds

# Create a data iterator
url = f"pipe:curl -s -L https://huggingface.co/datasets/jasperai/controllable-shadow-generation-benchmark/blob/main/controllable-shadow-generation-benchmark.tar"
data_iter = iter(wds.WebDataset(url))

# Sample from the dataset 
data = next(data_iter)

# Visualize the image, object mask, and object shadow 
_, axs = plt.subplots(1, 3, figsize=(15, 5))
axs[0].imshow(data['image.png'])
axs[0].set_title('Image')
axs[1].imshow(data['mask.png'])
axs[1].set_title('Mask')
axs[2].imshow(data['shadow.png'])
axs[2].set_title('Shadow')

# Print the metadata
print(data['metadata.json'])

```
Example output:
![image/png](https://cdn-uploads.huggingface.co/production/uploads/6368c710e68400b192fffb9d/2pnmXOlBpHVjFXiw5vhIz.png)

Example metadata:
```python
{
  'track': 'softness_control', # Which track the image belongs to 
  'light_energy': 1000,        # Energy of the area light 
  'size': 2,                   # Size of the area light 
  'theta': 30.0,               # Polar coodinate of the area light 
  'phi': 0.0,                  # Azimuthal coodinate of the area light 
  'r': 8.0,                    # Radius of the sphere
  'light_location': '4.0,0.0,6.928203105926514', # Cartesian coordinate of the area light
  'samples': 512,              # We use Cycle rendering engine in Blender when creating the dataset.
                               # This parameter shows # of samples used by Cycle when rendering the image. 
  'resolution_x': 1024,        # Width of the image.
  'resolution_y': 1024         # Height of the image. 
}
```

# Bibtex
If you use this dataset, please consider citing our paper:
```
@misc{
  title={Controllable Shadow Generation with Single-Step Diffusion Models from Synthetic Data},
  author={Tasar, Onur and Chadebec, Clement and Aubin, Benjamin},
  year={2024},
  eprint={2412.11972},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}
```