Saturday, 16 Oct 2021

SKETCHES are created by humans through an iterative process and reflect one’s sketching skills, taste, world perception and even character IN JUST A SET OF SPARSE LINES. Being the result of semantic, perceptual, or conceptual processing, sketches are distinctive from photos.

While the Computer Vision and Machine Learning communities have firmly invested in reasoning with photos, sketch data just recently got into the spotlight. This shift of focus on using sketch data has already started to cause a profound impact on many facets of research on computer vision, computer graphics, machine learning, HCI and artificial intelligence at large. Sketch has not only been used for image RETRIEVAL, 3D MODELING, USER INTERFACE DESIGN, but also as a key enabler in our fundamental understanding of VISUAL ABSTRACTION, CREATIVITY, and EXPRESSIVITY. Developing such understanding is impossible with photos.

This (series of) workshop aims to BRING TOGETHER RESEARCHERS FROM INTERDISCIPLINARY RESEARCH FIELDS in the community to consolidate cross-discipline insights, IDENTIFY AND ENCOURAGE NEW RESEARCH DIRECTIONS, and ultimately foster the growth of the sketch research community.






Aaron Hertzmann is a Principal Scientist at Adobe Research. He was a Professor at University of Toronto for 10 years, and has also worked at Pixar Animation Studios, University of Washington, Microsoft Research, Mitsubishi Electric Research Lab, and Interval Research Corporation. He has published over 100 papers in computer graphics, computer vision, machine learning, HCI, perception, and art. He is an Affiliate Professor at University of Washington, an ACM Fellow, an IEEE Fellow, and the Editor-in-Chief of Foundations and Trends in Computer Graphics and Vision.

Alla Sheffer is a full professor at the University of British Columbia, Canada. She is the recipient of the Canadian Human Computer Communications Society Achievement Award'18 and a UBC Killam Research Award'19. She is a fellow of the Royal Society of Canada, a fellow of IEEE, and a member of the SIGGRAPH Academy. She has served as an Associate Editor of all three major computer graphics journals (ACM Transactions on Graphics, IEEE Transactions on Visualization Computer Graphics, and Eurographics Computer Graphics Forum). She holds six recent patents on methods for sketch analysis and hexahedral mesh generation

Hongbo Fu is a Professor in the School of Creative Media, City University of Hong Kong. He has been actively working on projects in the fields of computer graphics and human-computer interaction. His research has led to more than 100 scientific publications. Over 50 out of those publications appeared at the top graphics/vision/HCI journals and conferences including ACM Transactions on Graphics (SIGGRAPH or SIGGRAPH Asia), CVPR, ECCV, CHI, IEEE Transactions on Visualization and Computer Graphics, Computer Graphics Forum, and UIST. His works have been selected as the Program Highlights by SIGGRAPH and SIGGRAPH Asia for multiple times, and have received the Best Paper Awards from CAD/Graphics 2015 and UIST 2019, as well as the Best Demo Awards at the Emerging Technologies program, SIGGRAPH Asia in both 2013 and 2014.

Devi Parikh is an Associate Professor in the School of Interactive Computing at Georgia Tech, and a Research Scientist at Facebook AI Research (FAIR). She was an Assistant Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech and a Research Assistant Professor at Toyota Technological Institute at Chicago (TTIC). She is a recipient of an NSF CAREER award, an IJCAI Computers and Thought award, a Sloan Research Fellowship, four Google Faculty Research Awards, an Amazon Academic Research Award, a Forbes’ list of 20 “Incredible Women Advancing A.I. Research” recognition, and a Marr Best Paper Prize awarded at the International Conference on Computer Vision (ICCV).



The topics of primary interest for this workshop are those that study and exploit an expressive power of sketches. We encourage interdisciplinary research lying at the intersection of CV (computer vision), ML (machine learning) and CG (computer graphics), HCI (human-computer interaction), or UX (user experience). Specifically, topics include but are not limited to:

Sketch understanding and representation:
   - sketch and human perception
   - sketch social and cultural impact
   - synthetic 2D/3D sketch synthesis
   - sketch abstraction
   - synthetic sketch quality assessment
   - sketch recognition
   - sketch segmentation/grouping
   - sketch captioning
   - sketch representation

Sketch and human creativity:
   - sketch for human creativity
   - sketch-based ideation and product prototyping
   - sketch-based modeling
   - sketch-based editing
   - sketching interfaces
   - sketching in AR/VR, gesture recognition for sketching
   - style transfer in scene sketching, portraits or animation

Multi-modal sketch applications:
   - 2D sketch-based image retrieval (SBIR)
   - 2D/3D sketch-based 3D shape retrieval
   - novel sketch-based datasets and its applications


The submitted papers must be an unpublished work on the topics summarized above. Papers will undergo full double-blind peer review by 3-5 program committee members. There is neither a rebuttal nor a second review cycle. The papers will be published in conjunction with ICCV 2021 proceedings. The Best Paper will be selected by a program committee.


Please submit your work using the CMT website.
For formatting instructions and LaTeX templates please refer to the ICCV submission guidelines. The page limit is 8 pages. We accept supplemental material in 'pdf' or 'zip' formats.


Paper submission deadline:
Supplemental material deadline:
Acceptance notification to authors:
Camera-ready deadline:
25th July 3rd August, 11:59 pm Pacific Time
3rd August, 11:59 pm Pacific Time
11th August
17th August


A GeForce RTX 3080 GPU will be sponsored by the SketchX Research Lab,
and awarded to the Best Paper.









Yulia Gryaditskaya is a Senior Research Fellow in Computer Vision and Machine Learning at the Centre for Vision Speech and Signal Processing (CVSSP) with Dr. Yi-Zhe Song. Previously, she was a postdoctoral researcher at Inria, Sophia Antipolis, France, with Dr. Adrien Bousseau. She obtained her PhD in 2017 on Computer Vision and Graphics from the Max-Planck institute for informatics, Saarbrücken, Germany, under supervision of Prof. Karol Myszkowski and Prof. Hans-Peter Seidel. While working on her PhD, she did a research internship at Technicolor R&D, HDR and Color Group, Rennes, France, with Dr. Erik Reinhard. She got her diploma from Lomonosov Moscow State University, Moscow, Russia. Her research interests include geometric deep learning, sketch-based modeling, sketch beautification, segmentation, classification and generation, VR sketching.

Qian Yu is an associate professor at Beihang University. Before joining Beihang, she was a postdoctoral research fellow at UC Berkeley / ICSI, 2018-2019, working with Dr. Stella Yu. She received her Ph.D. degree from the Queen Mary University of London in 2018, advised by Dr. Yi-Zhe Song and Prof. Tao Xiang. Her research is on computer vision and deep learning, focusing on human sketch understanding, including synthesis, recognition, and related applications.

Yonggang Qi is an assistant professor (lecturer) at Beijing University of Posts and Telecommunications (BUPT), Beijing, China. He received his PhD degree in Signal Processing at BUPT in 2015. From 2019 to 2020, he was a visiting scholar at SketchX Lab at the Centre for Vision Speech and Signal Processing (CVSSP) in University of Surrey. He also worked as a guest PhD at Aalborg University in Denmark in 2013 and a visiting researcher at Sun Yat-sen University in China in 2014. His research interests include perceptual grouping, and sketch-based vision tasks such as sketch-based image retrieval (SBIR), sketch recognition, sketch generation and language-based sketch understanding.

Zeynep Akata is a professor of Computer Science at the Cluster of Excellence Machine Learning at the University of Tübingen. After her PhD in INRIA Rhone Alpes (France) in 2014, she worked as a post-doctoral researcher at the Max Planck Institute for Informatics (Germany) between 2014-2017, at UC Berkeley (USA) between 2016-2017 and as an assistant professor at the University of Amsterdam (The Netherlands) between 2017-19. She received a Lise-Meitner Award for Excellent Women in Computer Science in 2014 and an ERC Starting Grant in 2019. Her research interests include multimodal learning in low-data regimes such as zero- and few-shot learning as well as explainable machine learning focusing on vision and language-based deep learning.

Adrien Bousseau Adrien Bousseau is a researcher at Inria Sophia-Antipolis (France) since 2010. He did his PhD at Inria Rhône-Alpes under the supervision of Joëlle Thollot and François X. Sillion (2006-2009), and his postdoc at UC Berkeley, working with Maneesh Agrawala and Ravi Ramamoorthi (2009-2010). Adrien does research on image creation and manipulation, with a focus on drawings and photographs. Most notably he works on image stylization, image editing and relighting, vector graphics and sketch-based modeling. He received one of the three Eurographics 2011 PhD award for his research on expressive image manipulations and a young researcher award from the French National Research Agency (ANR) for his work on computer-assisted drawing. Adrien received an ERC Starting Grant in 2016 to work on drawing interpretation for 3D design.

Niloy J. Mitra leads the Smart Geometry Processing group in the Department of Computer Science at University College London. He received his PhD from Stanford University under the guidance of Leonidas Guibas. His current research focuses on developing machine learning frameworks for generating high-quality geometric and appearance models for CG applications. Niloy received the 2019 Eurographics Outstanding Technical Contributions Award, the 2015 British Computer Society Roger Needham Award, and the 2013 ACM SIGGRAPH Significant New Researcher Award. He also leads the Adobe Research London Lab.

Sven Dickinson received the B.A.Sc. degree in Systems Design Engineering from the University of Waterloo, in 1983, and the M.S. and Ph.D. degrees in Computer Science from the University of Maryland, in 1988 and 1991, respectively. He is a Professor of the Department of Computer Science at the University of Toronto. He is a Vice President and Head of the new Samsung Toronto AI Research Center, which opened in May, 2018. He has received the National Science Foundation CAREER award, the Government of Ontario Premiere's Research Excellence Award (PREA), and the Lifetime Research Achievement Award from the Canadian Image Processing and Pattern Recognition Society (CIPPRS). He currently serves on eight editorial boards, including the role of Editor-in-Chief of the IEEE Transactions on Pattern Analysis and Machine Intelligence, and the role of co-editor of the Morgan & Claypool Synthesis Lectures on Computer Vision. He is a Fellow of the International Association for Pattern Recognition (IAPR).

We thank Dr. Yi-Zhe Song for initiating this workshop and providing valuable feedback for the workshop proposal.


Contact: Please email or if any enquires.