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Research map / Image generation / Autoregressive and latent generation

Scribbler: Controlling Deep Image Synthesis With Sketch and Color

CVPR 2017 · Image generation › Autoregressive and latent generation · approach: Classical generative image synthesis · 535 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add adversarial loss to content loss (ablation)Scribbler sketch-to-photo synthesis · Faces, cars, and bedroomsvisual realism and diversityHelped
Use the deeper encoder-decoder architecture instead of Sketch InversionScribbler sketch-to-photo synthesis · Complex bedroom scenes and simplified sketchessharpness and hallucination of missing detailsHelped
Add multiple sketch styles to training and fine-tune (ablation)Scribbler face sketch-to-photo synthesis · Arbitrary hand-drawn face sketchesgeneralizationHelped
Add random cropping and sketch-line cut-off augmentation (ablation)Scribbler sketch-to-photo synthesis · Sketches with missing detailshallucination of missing detailsHelped
Add sparse color strokes to the inputScribbler guided sketch colorization · Bedroom and car sketchessatisfaction of user color constraintsHelped
Use adversarial loss during sketch reconstruction (ablation)Scribbler · Exaggerated or cartoonish face sketchesfaithfulness to sketch constraintsHurt
Use adversarial loss to satisfy rare color controls (ablation)Scribbler colorization · Rare color requests, such as purple cars or red treesfaithfulness to color controlsHurt
Use a weak discriminator and do not condition it on the inputScribbler adversarial training · Training stabilitystabilityHelped

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