Paper Info
Title
DoodleFormer: Creative Sketch Drawing with Transformers
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 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: https://github.com/ankanbhunia/doodleformer .
Year
DOI
Venue
2022
10.1007/978-3-031-19790-1_21
Computer Vision – ECCV 2022
DocType
ISSN
Citations 
Conference
0302-9743
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Ankan Kumar Bhunia111.70
Salman Khan238741.05
Hisham Cholakkal3488.40
Muhammad Anwer Rao412911.22
Fahad Shahbaz Khan5162269.24
Jorma Laaksonen61162176.93
Michael Felsberg72419130.29