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DoodleFormer: Creative Sketch Drawing with Transformers
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
  • Ankan Kumar Bhunia, Mohamed bin Zayed University of Artificial Intelligence
  • Salman Khan, Mohamed bin Zayed University of Artificial Intelligence & Australian National University
  • Hisham Cholakkal, Mohamed bin Zayed University of Artificial Intelligence
  • Rao Muhammad Anwer, Mohamed bin Zayed University of Artificial Intelligence & Aalto University
  • Fahad Shahbaz Khan, Mohamed bin Zayed University of Artificial Intelligence & Linköping University
  • Jorma Laaksonen, Aalto University
  • Michael Felsberg, Linköping University
Document Type
Conference Proceeding

Creative sketching or doodling is an expressive activity, where imaginative and previously unseen depictions of everyday visual objects are drawn. Creative sketch image generation is a challenging vision problem, where the task is to generate diverse, yet realistic creative sketches possessing the unseen composition of the visual-world objects. Here, we propose a novel coarse-to-fine two-stage framework, DoodleFormer, that decomposes the creative sketch generation problem into the creation of coarse sketch composition followed by the incorporation of fine-details in the sketch. We introduce graph-aware transformer encoders that effectively capture global dynamic as well as local static structural relations among different body parts. To ensure diversity of the generated creative sketches, we introduce a probabilistic coarse sketch decoder that explicitly models the variations of each sketch body part to be drawn. Experiments are performed on two creative sketch datasets: Creative Birds and Creative Creatures. Our qualitative, quantitative and human-based evaluations show that DoodleFormer outperforms the state-of-the-art on both datasets, yielding realistic and diverse creative sketches. On Creative Creatures, DoodleFormer achieves an absolute gain of 25 in Frèchet inception distance (FID) over state-of-the-art. We also demonstrate the effectiveness of DoodleFormer for related applications of text to creative sketch generation, sketch completion and house layout generation. Code is available at: © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Publication Date
  • Body parts,
  • Coarse to fine,
  • Creatives,
  • Global dynamics,
  • Image generations,
  • Sketchings,
  • State of the art,
  • Vision problems,
  • Visual objects,
  • Visual world,
  • Drawing (graphics),
  • Computer Vision and Pattern Recognition (cs.CV),
  • Graphics (cs.GR)

IR conditions: non-described

Citation Information
A.K. Bhunia,, "DoodleFormer: Creative sketch drawing with transformers", in Computer Vision (ECCV 2022), Lecture Notes in Computer Science, vol 13677, pp. 338-355, October 2022, doi: 10.1007/978-3-031-19790-1_21