Image-Based CLIP-Guided Essence Transfer

24 Oct 2021  ·  Hila Chefer, Sagie Benaim, Roni Paiss, Lior Wolf ·

The conceptual blending of two signals is a semantic task that may underline both creativity and intelligence. We propose to perform such blending in a way that incorporates two latent spaces: that of the generator network and that of the semantic network... For the first network, we employ the powerful StyleGAN generator, and for the second, the powerful image-language matching network of CLIP. The new method creates a blending operator that is optimized to be simultaneously additive in both latent spaces. Our results demonstrate that this leads to blending that is much more natural than what can be obtained in each space separately. read more

PDF Abstract

Datasets


  Add Datasets introduced or used in this paper

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods