Style2Paints Research* (S2PR) is a non-commercial research group sharing the interest in artistic creation techniques. The current S2PR group consists of 4 researchers, 4 engineers, and 22 artists. The main products of S2PR involves tools in line drawing processing, color manipulation, illumination editing, shadow drawing, etc. S2PR has made presentations in scientific conferences like ACM SIGGRAPH, IEEE CVPR, and commercial exhibitions like Anime EXPO.

* Style2Paints Research was founded by Lvmin Zhang in 2018 - Join Us / Twitter / Github

2022.08.15 - Lvmin's article is accepted to SIGGRAPH ASIA 2022, journal track.
2022.06.15 - See some recent announcements of Style2Paints (Project SEPA) here.
2022.01.09 - See some recent announcements of Style2Paints (Project SEPA) here.
2021.06.09 - An article on shadow drawing is accepted to ICCV 2021 as Oral.
2021.06.01 - The Project SEPA is decided to be released before 2022.
2021.03.22 - The next version of Style2Paints will be called Project SEPA. See also the twitter post.

Interactivity, Creativity, and Possibility!
Style2Paints is a line drawing coloring software. This software is somewhat popular (>14k github stars) mainly because (1) it is easy to install and artists do not need to manage environments like CUDA, (2) the result quality is good, and (3) the outputs are layered and friendly to many painting workflows.
Physical illumination and painted illumination are different!
Painting light is a project conducted to investigate how artists add illumination and lighting effects to their artworks, and how we can simulate this procedure to assist this workflow.
An region segmentation dataset for illustrations and artworks!
Lots of applications in the field of creative researches use regions to process images but the region data for artworks and illustrations are rare. Style2Paints Research presents this dataset in the hope that several related tasks can be made a bit easier.

Lvmin Zhang Jinyue Jiang Chengze Li Xinrui Wang

Sprite-from-Sprite: Cartoon Animation Decomposition with Self-supervised Sprite Estimation,
in ACM Transactions on Graphics (SIGGRAPH ASIA 2022, Journal Track).
Lvmin Zhang, Tien-Tsin Wong, and Yuxin Liu.
The "sprites" in real-world cartoons are unique: artists may draw arbitrary sprite animations for expressiveness, or alternatively, artists may also reduce their workload by tweening and adjusting contents. Can we use these properties to do a "reverse engineering" to get the original sprites in digital animation? Know more ...
SmartShadow: Artistic Shadow Drawing Tool for Line Drawings, in IEEE International Conference on Computer Vision (ICCV) 2021 Oral (3%).
Lvmin Zhang, Jinyue Jiang, Yi Ji, and Chunping Liu.
A flexible shadow drawing tool for line drawings, supporting interactive editing of cartoon-style shadows. Know more ...
Screenshots from Screen Photography,
in SIGGRAPH '21: ACM SIGGRAPH 2021 Posters.
Lvmin Zhang and Chengze Li.
Screenshot is a frequently used tool in our daily life, while the screenshot capturing techniques are not much discussed in computer graphics and image processing researches. Might we be able to achieve a computer graphic solution to directly convert a screen photography to a screenshot, which looks like as if it was taken using software? Know more ...
Generating Digital Painting Lighting Effects via RGB-space Geometry,
in ACM Transactions on Graphics (Presented in SIGGRAPH 2020). This paper does not use machine learning!
Lvmin Zhang, Edgar Simo-Serra, Yi Ji, and Chunping Liu.
A project conducted to investigate how artists apply lighting effects to their artworks, and how we can assist such workflow. The main idea is to observe the real painting behaviors and procedures of artists so that we can model the illumination in their artworks. Know more ...
Generating Manga from Illustrations via Mimicking Manga Workflow, in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
Lvmin Zhang, Xinrui Wang, Qingnan Fan, Yi Ji, and Chunping Liu.
We propose a data-driven framework to convert a digital illustration into three corresponding components: manga line drawing, regular screentone, and irregular screen texture. These components can be directly composed into manga images and can be further retouched for more plentiful manga creations. Know more ...
User-Guided Line Art Flat Filling with Split Filling Mechanism, in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
Lvmin Zhang, Chengze Li, Edgar Simo-Serra, Yi Ji, Tien-tsin Wong, and Chunping Liu.
We present a deep learning framework for user-guided line art flat filling that explicitly controls the "influence areas" of the user colour scribbles, i.e., the areas where the user scribbles should propagate and influence, to manipulate the colours of image details and avoid colour contamination between scribbles, and simultaneously, leverages data-driven colour generation to facilitate content creation. Know more ...
DanbooRegion: An Illustration Region Dataset,
in European Conference on Computer Vision (ECCV) 2020.
Lvmin Zhang, Yi Ji, and Chunping Liu
Region is a fundamental element of various cartoon animation techniques and artistic painting applications. Achieving satisfactory region is essential to the success of these techniques. To assist diversiform region-based cartoon applications, we use semi-automatic method to annotate regions for in-the-wild artworks. Know more ...
Erasing Appearance Preservation in Optimization-based Smoothing, in European Conference on Computer Vision (ECCV) 2020 Spotlight (5%).
This paper does not use machine learning!
Lvmin Zhang, Chengze Li, Yi Ji, Chunping Liu, and Tien-tsin Wong
Optimization-based smoothing can be formulated as a smoothing energy and an appearance preservation energy. We show that partially "erasing" the energy facilitate the smoothing. Know more ...
Learning to Cartoonize Using White-box Cartoon Representations,
in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
Xinrui Wang and Jinze Yu
By observing the cartoon painting behavior and consulting artists, we propose to separately identify three white-box representations of cartoon images. Know more ...
Two-stage Sketch Colorization,
in ACM Transactions on Graphics (SIGGRAPH Asia 2018).
Lvmin Zhang, Chengze Li, Tien-tsin Wong, Yi Ji, and Chunping Liu
With the advances of neural networks, automatic or semi-automatic colorization of sketch become feasible and practical. We present a state-of-the-art semi-automatic (as well as automatic) colorization from line art. Our improvement is accounted by a divide-and-conquer scheme. We divide this complex colorization task into two simplier and goal-clearer subtasks, drafting and refinement. Know more ...
Style Transfer for Anime Sketches with Enhanced Residual U-net and Auxiliary Classifier GAN, in Asian Conference on Pattern Recognition (ACPR) 2017.
Spotlight (5%) - The most cited paper of ACPR 2017 -
Lvmin Zhang, Yi Ji, and Xin Lin
We integrate U-net to apply exemplar style to the grayscale sketch with auxiliary classifier generative adversarial network. The whole process is automatic and fast, and the results are creditable in the quality of artistic style as well as colorization. Know more ...
Anime Expo 2018 (Industrial Talk)
Anime Expo is the largest anime exhibition in United States and one of the two largest anime exhibitions in the world (another one is Japan Comic Market). S2PR presented an industrial talk on "deep learning for artists" in Anime Expo 2018.
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