Controlling StyleGANs using rough scribbles via one‐shot learning

This paper tackles the challenging problem of one‐shot semantic image synthesis from rough sparse annotations, which we call “semantic scribbles.” Namely, from only a single training pair annotated with semantic scribbles, we generate realistic and diverse images with layout control over, for exampl...

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Veröffentlicht in:	Computer animation and virtual worlds 2022-09, Vol.33 (5), p.n/a
Hauptverfasser:	Endo, Yuki, Kanamori, Yoshihiro
Format:	Artikel
Sprache:	eng
Schlagworte:	Annotations Coders GAN inversion generative adversarial networks image editing Image quality Layouts Optimization Random noise Semantics Synthesis Training
Online-Zugang:	Volltext
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