Document Type

Article

Publication Title

arXiv

Abstract

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 terms of Frèchet inception distance (FID) over the state-of-the-art. We also demonstrate the effectiveness of DoodleFormer for related applications of text to creative sketch generation and sketch completion. © 2021, CC BY.

DOI

doi.org/10.48550/arXiv.2112.03258

Publication Date

12-6-2021

Keywords

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)

Comments

Preprint: arXiv

  • Archived with thanks to arXiv
  • Preprint License: CC by 4.0
  • Uploaded 24 March 2022

Share

COinS