Haoran MO |

莫浩然

Intelligent and Multimedia Science Laboratory

School of Computer Science and Engineering

Sun Yat-sen University (SYSU)

Guangzhou, China

Email:

mohaor (at) mail2.sysu.edu.cn

 

Github Google Scholar Resume

 


Publications (All)  (☞ Selected)

'#' indicates equal contribution. '*' indicates corresponding author.


2022

icme2022 Unpaired Motion Style Transfer with Motion-oriented Projection Flow Network

 

Yue Huang, Haoran Mo, Xiao Liang and Chengying Gao*

 

IEEE International Conference on Multimedia & Expo (ICME 2022) (oral) (CCF-B)

 

Paper Abstract Bibtex

 

Existing motion style transfer methods trained with unpaired samples tend to generate motions with inconsistent content or inconsistent number of frames when compared with the source motion. Moreover, due to the limited training samples, these methods perform worse in unseen style. In this paper, we propose a novel unpaired motion style transfer framework that generates complete stylized motions with consistent content. We introduce a motion-oriented projection flow network (M-PFN) designed for temporal motion data, which encodes the content and style motions into latent codes and decodes the stylized features produced by adaptive instance normalization (AdaIN) into stylized motions. The M-PFN contains dedicated operations and modules, e.g., Transformer, to process the temporal information of motions, which help to improve the continuity of the generated motions. Comparisons with the state-of-the-art methods show that our method effectively transfers the style of the motions while retaining the complete content and has stronger generalization ability in unseen style features.

@inproceedings{huang2022unpaired,
  title={Unpaired Motion Style Transfer with Motion-oriented Projection Flow Network},
  author={Huang, Yue and Mo, Haoran and Liang, Xiao and Gao, Chengying},
  booktitle={2022 IEEE International Conference on Multimedia and Expo (ICME)},
  pages={1--6},
  year={2022},
  organization={IEEE}
}

2021

vectorization General Virtual Sketching Framework for Vector Line Art

 

Haoran Mo, Edgar Simo-Serra, Chengying Gao*, Changqing Zou and Ruomei Wang

 

ACM Transactions on Graphics (Journal track of ACM SIGGRAPH 2021) (CCF-A)

 

Project Page Paper Supplementary Code Abstract Bibtex

 

Vector line art plays an important role in graphic design, however, it is tedious to manually create. We introduce a general framework to produce line drawings from a wide variety of images, by learning a mapping from raster image space to vector image space. Our approach is based on a recurrent neural network that draws the lines one by one. A differentiable rasterization module allows for training with only supervised raster data. We use a dynamic window around a virtual pen while drawing lines, implemented with a proposed aligned cropping and differentiable pasting modules. Furthermore, we develop a stroke regularization loss that encourages the model to use fewer and longer strokes to simplify the resulting vector image. Ablation studies and comparisons with existing methods corroborate the efficiency of our approach which is able to generate visually better results in less computation time, while generalizing better to a diversity of images and applications.

@article{mo2021virtualsketching,
  title   = {General Virtual Sketching Framework for Vector Line Art},
  author  = {Mo, Haoran and Simo-Serra, Edgar and Gao, Chengying and Zou, Changqing and Wang, Ruomei},
  journal = {ACM Transactions on Graphics (TOG)},
  year    = {2021},
  volume  = {40},
  number  = {4},
  pages   = {51:1--51:14}
}
colorization-PG2021 Line Art Colorization Based on Explicit Region Segmentation

 

Ruizhi Cao, Haoran Mo and Chengying Gao*

 

Computer Graphics Forum (Pacific Graphics 2021) (CCF-B)

 

Paper Supplementary Code Abstract Bibtex

 

Automatic line art colorization plays an important role in anime and comic industry. While existing methods for line art colorization are able to generate plausible colorized results, they tend to suffer from the color bleeding issue. We introduce an explicit segmentation fusion mechanism to aid colorization frameworks in avoiding color bleeding artifacts. This mechanism is able to provide region segmentation information for the colorization process explicitly so that the colorization model can learn to avoid assigning the same color across regions with different semantics or inconsistent colors inside an individual region. The proposed mechanism is designed in a plug-and-play manner, so it can be applied to a diversity of line art colorization frameworks with various kinds of user guidances. We evaluate this mechanism in tag-based and reference-based line art colorization tasks by incorporating it into the state-of-the-art models. Comparisons with these existing models corroborate the effectiveness of our method which largely alleviates the color bleeding artifacts.

@inproceedings{cao2021line,
  title={Line Art Colorization Based on Explicit Region Segmentation},
  author={Cao, Ruizhi and Mo, Haoran and Gao, Chengying},
  booktitle={Computer Graphics Forum},
  volume={40},
  number={7},
  year={2021},
  organization={Wiley Online Library}
}

2019

colorization Language-based Colorization of Scene Sketches

 

Changqing Zou#, Haoran Mo#(equal contribution), Chengying Gao*, Ruofei Du and Hongbo Fu

 

ACM Transactions on Graphics (Journal track of ACM SIGGRAPH Asia 2019) (CCF-A)

 

Project Page Paper Supplementary Code 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 (TOG)},
  year    = {2019},
  volume  = {38},
  number  = {6},
  pages   = {233:1--233:16}
}

2018

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) (CCF-B)

 

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{zou2018sketchyscene,
  title={Sketchyscene: Richly-annotated scene sketches},
  author={Zou, Changqing and Yu, Qian and Du, Ruofei and Mo, Haoran and Song, Yi-Zhe and Xiang, Tao and Gao, Chengying and Chen, Baoquan and Zhang, Hao},
  booktitle={Proceedings of the european conference on computer vision (ECCV)},
  pages={421--436},
  year={2018}


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