Haoran MO |

莫浩然

Intelligent and Multimedia Science Laboratory

School Of Data and Computer Science

Sun Yat-sen University (SYSU)

Guangzhou, China

Email:

mohaoran1995 (at) gmail (dot) com

 

Github | Blog | CV

 

I am currently a second-year master student in Intelligent and Multimedia Science Laboratory of Sun Yat-sen University (SYSU), supervised by Prof. Chengying GAO and Prof. Ning LIU. My research interests cover Computer Vision and Computer Graphics, particularly in sketch understanding and sketch-based applications. Luckily for me, I also work closely with Prof. Changqing ZOU in Huawei Noah's Ark Lab and Prof. Edgar Simo-Serra in Waseda University.



News


Education


Publications

colorization Language-based Colorization of Scene Sketches

 

Changqing Zou#, Haoran Mo# (joint first author), Chengying Gao*, Ruofei Du and Hongbo Fu

 

ACM Transactions on Graphics (Proceedings of ACM SIGGRAPH Asia 2019)

 

Project Page | Paper | Supplementary | Code | Fast Forward Video | Slide | Abstract | Bibtex

 

Being natural, touchless, and fun-embracing, language-based inputs have been demonstrated effective for various tasks from image generation to literacy education for children. This paper for the first time presents a language-based system for interactive colorization of scene sketches, based on semantic comprehension. The proposed system is built upon deep neural networks trained on a large-scale repository of scene sketches and cartoon-style color images with text descriptions. Given a scene sketch, our system allows users, via language-based instructions, to interactively localize and colorize specific foreground object instances to meet various colorization requirements in a progressive way. We demonstrate the effectiveness of our approach via comprehensive experimental results including alternative studies, comparison with the state-of-the-art methods, and generalization user studies. Given the unique characteristics of language-based inputs, we envision a combination of our interface with a traditional scribble-based interface for a practical multimodal colorization system, benefiting various applications.

@article{zouSA2019sketchcolorization,
  title = {Language-based Colorization of Scene Sketches},
  author = {Zou, Changqing and Mo, Haoran and Gao, Chengying and Du, Ruofei and Fu, Hongbo},
  journal = {ACM Transactions on Graphics (Proceedings of ACM SIGGRAPH Asia 2019)},
  year = {2019},
  volume = 38,
  number = 6,
  pages = {233:1--233:16}
}
            
SketchyScene_eccv18 SketchyScene: Richly-Annotated Scene Sketches

 

Changqing Zou#, Qian Yu#, Ruofei Du, Haoran Mo, Yi-Zhe Song, Tao Xiang, Chengying Gao, Baoquan Chen* and Hao Zhang

 

European Conference on Computer Vision (ECCV), 2018

 

Project Page | Paper | Poster | Code | Abstract | Bibtex

 

We contribute the first large-scale dataset of scene sketches, SketchyScene, with the goal of advancing research on sketch understanding at both the object and scene level. The dataset is created through a novel and carefully designed crowdsourcing pipeline, enabling users to efficiently generate large quantities realistic and diverse scene sketches. SketchyScene contains more than 29,000 scene-level sketches, 7,000+ pairs of scene templates and photos, and 11,000+ object sketches. All objects in the scene sketches have ground-truth semantic and instance masks. The dataset is also highly scalable and extensible, easily allowing augmenting and/or changing scene composition. We demonstrate the potential impact of SketchyScene by training new computational models for semantic segmentation of scene sketches and showing how the new dataset enables several applications including image retrieval, sketch colorization, editing, and captioning, etc.

@inproceedings{Zou18SketchyScene,
  author    = {Changqing Zou and
                Qian Yu and
                Ruofei Du and
                Haoran Mo and
                Yi-Zhe Song and
                Tao Xiang and
                Chengying Gao and
                Baoquan Chen and
                Hao Zhang},
  title     = {SketchyScene: Richly-Annotated Scene Sketches},
  booktitle = {ECCV},
  year      = {2018},
  publisher = {Springer International Publishing},
  pages		= {438--454},
  doi		= {10.1007/978-3-030-01267-0_26},
  url		= {https://github.com/SketchyScene/SketchyScene}
}
            

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