SketchyScene: Richly-Annotated Scene Sketches

Changqing Zou*1    Qian Yu*2    Ruofei Du1    Haoran Mo3    Yi-Zhe Song2    Tao Xiang2   
Chengying Gao3    Baoquan Chen**4    Hao Zhang5

1University of Maryland       2Queen Mary University of London       3Sun Yat-sen University      
4Shandong University       5Simon Fraser University

ECCV 2018

[Paper] [Code]


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.


Dataset

*From left to right: reference image, synthesized sketchy scene ("L" is used to mark the category alignment), ground-truth of semantic and instance segmentation.


*Examples of augmented scene sketches based on a scene sketch template (the 1st image).

7265 (train 5617 + val 535 + test 1113) [Download]

(Further data will come soon)


paper thumbnail

Paper

ECCV, 2018.

Citation

Changqing Zou, Qian Yu, Ruofei Du, Haoran Mo, Yi-Zhe Song, Tao Xiang, Chengying Gao, Baoquan Chen and Hao Zhang. "SketchyScene: Richly-Annotated Scene Sketches", in ECCV, 2018.

Bibtex

Code: TensorFlow



Demo

[Try]


Acknowledgement

This work was partially supported by the China National 973 Program (2015CB352501), NSFC-ISF(61561146397), NSERC 611370, and the China Scholarship Council (CSC).