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Paper · 2011.10039 · ICLR · 2021

Creative Sketch Generation

Devi Parikh, Songwei Ge, Vedanuj Goswami, C Zitnick, Tu-Berlin Bird, Quickdraw Bird

arXiv · PDF · Open in the Atlas

Code that ran

We lifted 12 functions out of this paper's own repositories and ran 10 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.

RepositoryRoleRan
facebookresearch/DoodlerGAN canonical 10 of 12
FunctionStatusWhere it lives
Discriminator Ran facebookresearch/DoodlerGAN/part_generator.py
code served (permissive licence) · get_code("3c595cc17d5b85ba")
DiscriminatorBlock Ran facebookresearch/DoodlerGAN/part_generator.py
code served (permissive licence) · get_code("20a4f6204affb3ef")
EncoderBlock_unet Ran facebookresearch/DoodlerGAN/part_generator.py
code served (permissive licence) · get_code("ed29dc3ccb2b5b3b")
Encoder_unet Ran facebookresearch/DoodlerGAN/part_generator.py
code served (permissive licence) · get_code("36558825ea54044c")
Flatten Ran facebookresearch/DoodlerGAN/part_generator.py
code served (permissive licence) · get_code("8818002d32451ab2")
GeneratorBlock Ran facebookresearch/DoodlerGAN/part_generator.py
code served (permissive licence) · get_code("85aff785262093b0")
Generator_unet Ran facebookresearch/DoodlerGAN/part_generator.py
code served (permissive licence) · get_code("3add9642c3b47498")
RGBBlock Ran facebookresearch/DoodlerGAN/part_generator.py
code served (permissive licence) · get_code("ad92582b3ff8e62d")
StyleVectorizer Ran facebookresearch/DoodlerGAN/part_generator.py
code served (permissive licence) · get_code("a1c88e05be5f5cae")
leaky_relu Ran facebookresearch/DoodlerGAN/part_generator.py
code served (permissive licence) · get_code("870b0f7a0a7cfeb6")
Conv2DMod Not yet run facebookresearch/DoodlerGAN/part_generator.py
code served (permissive licence) · get_code("0efd23bf3e72a0fa")
StyleGAN2_cond_unet Not yet run facebookresearch/DoodlerGAN/part_generator.py
code served (permissive licence) · get_code("df6d3540e200d9f5")

Repositories linked to this paper

Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.

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 DoodlerGANa 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! Our code, datasets and demo can be found at songweige.github.io/ projects/creative_sketech_generation/home.html.

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