Im2Oil: Stroke-Based Oil Painting Rendering with Linearly Controllable Fineness Via Adaptive Sampling
This is the official implementation of the ACM MM 2022: ACM International Conference on Multimedia accepted paper "Im2Oil: Stroke-Based Oil Painting Rendering with Linearly Controllable Fineness Via Adaptive Sampling".
Our paper's official ACM digital labrary publication: https://dl.acm.org/doi/10.1145/3503161.3547759, and the arXiv version is available at: https://arxiv.org/abs/2209.13219
Our Supplementary Material (PDF) is available at
- Zhengyan Tong (Shanghai Jiao Tong University Master): 此论文idea提供者、代码原作者、论文主笔者，主要从事计算机视觉方面的研究。发表此论文时为上海交通大学电子信息与电气工程学院硕士研究生。
Email: 418004@sjtu.edu.cn - Xiaohang Wang (Shanghai Jiao Tong University Master)
- Shengchao Yuan (Shanghai Jiao Tong University Master)
- Xuanhong Chen (Shanghai Jiao Tong University Ph. D.)
- JunJie Wang (Shanghai Jiao Tong University Master)
- Xiangzhong Fang (Shanghai Jiao Tong University Professor)
- I am extremely grateful to my coworkers, who greatly help me with this research.
The input images are in the folder "input", where we have offered 50 examples.
The output results will be in the folder "output".
- To reproduce the result of A2:
python Oil-Painting.py --f "./input/A2.jpg" --p 4 - To reproduce the result of A9:
python Oil-Painting.py --f "./input/A8.jpg" --p 4 - To reproduce the result of B8:
python Oil-Painting.py --f "./input/B8.jpg" --p 4 - To reproduce the result of L8:
python Oil-Painting.py --f "./input/L8.jpg" --p 4 - To reproduce the result of P1:
python Oil-Painting.py --f "./input/P1.jpg" --p 4 - To reproduce the result of P4:
python Oil-Painting.py --f "./input/P4.jpg" --p 4 - To reproduce the result of S1:
python Oil-Painting.py --f "./input/S1.jpg" --p 4 - To reproduce the result of S6:
python Oil-Painting.py --f "./input/S6.jpg" --p 4 - To reproduce the result of S9:
python Oil-Painting.py --f "./input/S9.jpg" --p 4 - To reproduce the result of P0-4:
python Oil-Painting.py --f "./input/P0.jpg" --p 4 - To reproduce the result of P0-9:
python Oil-Painting.py --f "./input/P0.jpg" --p 9 - To reproduce the result of P0-16:
python Oil-Painting.py --f "./input/P0.jpg" --p 16
A2.mp4 |
A9.mp4 |
B8.mp4 |
L8.mp4 |
P1.mp4 |
P8.mp4 |
S1.mp4 |
S6.mp4 |
S9.mp4 |
P0-4.mp4 |
P0-9.mp4 |
P0-16.mp4 |
# config parameters (user control)
"image":"./input/S1.jpg", # input image filepath
"brush":"./brush/brush-0.png", # brush template
"p_max": 4, # the reciprocal of the Maximum Sampling Rate, use 4, 9, 16, 25, 36
"seed": 0, # np.random.seed()
"force": True, # force recomputation of the anchor Map
"SSAA" : 8, # Super-Sampling Anti-Aliasing
"freq" : 100, # save one frame every（freq) strokes drawn
"stroke_order_type": 0, # use 0 for the default size order, use 1 for random order
# default parameters (don't change)
"padding": 5, # padding
"n_iter": 15, # K-means iteration
"k_size": 5, # Sobel and Mean Filter size
"figsize": 6, # anchor map figure size
"pointsize": (8.0, 8.0), # point (mix,max) size for the anchor map
"ratio" : 3, # max_length/max_width
"threshold_hsv": (30,None,15), # threshold for hsv color space during searching
"kernel_radius" : 5, # ETF kernel_radius
"ETF_iter" : 15, # ETF iteration number
"background_dir" : None, # for ETF
@inproceedings{10.1145/3503161.3547759,
author = {Tong, Zhengyan and Wang, Xiaohang and Yuan, Shengchao and Chen, Xuanhong and Wang, Junjie and Fang, Xiangzhong},
title = {Im2Oil: Stroke-Based Oil Painting Rendering with Linearly Controllable Fineness Via Adaptive Sampling},
year = {2022},
isbn = {9781450392037},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3503161.3547759},
doi = {10.1145/3503161.3547759},
abstract = {This paper proposes a novel stroke-based rendering (SBR) method that translates images into vivid oil paintings. Previous SBR techniques usually formulate the oil painting problem as pixel-wise approximation. Different from this technique route, we treat oil painting creation as an adaptive sampling problem. Firstly, we compute a probability density map based on the texture complexity of the input image. Then we use the Voronoi algorithm to sample a set of pixels as the stroke anchors. Next, we search and generate an individual oil stroke at each anchor. Finally, we place all the strokes on the canvas to obtain the oil painting. By adjusting the hyper-parameter maximum sampling probability, we can control the oil painting fineness in a linear manner. Comparison with existing state-of-the-art oil painting techniques shows that our results have higher fidelity and more realistic textures. A user opinion test demonstrates that people behave more preference toward our oil paintings than the results of other methods. More interesting results and the code are in https://github.com/TZYSJTU/Im2Oil.},
booktitle = {Proceedings of the 30th ACM International Conference on Multimedia},
pages = {1035–1046},
numpages = {12},
keywords = {fineness control, stroke-based oil painting, adaptive sampling},
location = {Lisboa, Portugal},
series = {MM '22}
}