Haoran MO |


Intelligent and Multimedia Science Laboratory

School Of Data and Computer Science

Sun Yat-sen University (SYSU)

Guangzhou, China


mohaoran1995 (at) gmail (dot) com


Github | Google Scholar | Resume


I am currently a first-year Ph.D. student in Intelligent and Multimedia Science Laboratory of Sun Yat-sen University (SYSU), supervised by Prof. Ruomei WANG and Prof. Chengying GAO. My research interests cover Computer Vision and Computer Graphics, particularly in sketch understanding, sketch generation and sketch-based applications. Luckily for me, I also work closely with Prof. Changqing ZOU in Huawei HMI Lab and Prof. Edgar Simo-Serra in Waseda University.




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.

  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.

  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}



      Welcome to view my gallery. I am a local Cantonese and an enthusiast for Cantonese Pop Music and Hong Kong films and TV series :)

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