Research
Computer Vision
Creative Sketch Generation
May 4, 2021
Abstract
Sketching or doodling is a popular creative activity that people engage in. However, most existing work in automatic sketch understanding or generation has focused on sketches that are quite mundane. In this work, we introduce two datasets of creative sketches – Creative Birds and Creative Creatures – containing 10k sketches each along with part annotations. We propose DoodlerGAN – a part-based Generative Adversarial Network (GAN) – to generate unseen compositions of novel part appearances. Quantitative evaluations as well as human studies demonstrate that sketches generated by our approach are more creative and of higher quality than existing approaches. In fact, in Creative Birds, subjects prefer sketches generated by DoodlerGAN over those drawn by humans!
AUTHORS
Publisher
ICLR 2021
Research Topics
Related Publications
June 11, 2025
Computer Vision
IntPhys 2: Benchmarking Intuitive Physics Understanding In Complex Synthetic Environments
Florian Bordes, Quentin Garrido, Justine Kao, Adina Williams, Mike Rabbat, Emmanuel Dupoux
June 11, 2025
June 10, 2025
Computer Vision
A Shortcut-aware Video-QA Benchmark for Physical Understanding via Minimal Video Pairs
Benno Krojer, Mojtaba Komeili, Candace Ross, Quentin Garrido, Koustuv Sinha, Nicolas Ballas, Mido Assran
June 10, 2025
June 10, 2025
Robotics
V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
Mido Assran, Adrien Bardes, David Fan, Quentin Garrido, Russell Howes, Mojtaba Komeili, Matthew Muckley, Ammar Rizvi, Claire Roberts, Koustuv Sinha, Artem Zholus, Sergio Arnaud, Abha Gejji, Ada Martin, Francois Robert Hogan, Daniel Dugas, Piotr Bojanowski, Vasil Khalidov, Patrick Labatut, Francisco Massa, Marc Szafraniec, Kapil Krishnakumar, Yong Li, Xiaodong Ma, Sarath Chandar, Franziska Meier, Yann LeCun, Michael Rabbat, Nicolas Ballas
June 10, 2025
April 14, 2025
Graphics
Autoregressive Distillation of Diffusion Transformers
Yeongmin Kim, Sotiris Anagnostidis, Yuming Du, Edgar Schoenfeld, Jonas Kohler, Markos Georgopoulos, Albert Pumarola, Ali Thabet, Artsiom Sanakoyeu
April 14, 2025
April 30, 2018
Computer Vision
NAM – Unsupervised Cross-Domain Image Mapping without Cycles or GANs | Facebook AI Research
Yedid Hoshen, Lior Wolf
April 30, 2018
December 11, 2019
Speech & Audio
Computer Vision
Hyper-Graph-Network Decoders for Block Codes | Facebook AI Research
Eliya Nachmani, Lior Wolf
December 11, 2019
April 30, 2018
NLP
Speech & Audio
Identifying Analogies Across Domains | Facebook AI Research
Yedid Hoshen, Lior Wolf
April 30, 2018
November 01, 2018
NLP
Computer Vision
Non-Adversarial Unsupervised Word Translation | Facebook AI Research
Yedid Hoshen, Lior Wolf
November 01, 2018
Help Us Pioneer The Future of AI
We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.
Our approach
Latest news
Foundational models
Meta © 2025