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SEM-CS: SEMANTIC CLIPSTYLER FOR TEXT-BASED IMAGE STYLE TRANSFER

Chanda Grover Kamra, Indra Deep Mastan, Debayan Gupta

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Lecture 11 Oct 2023

CLIPStyler demonstrated image style transfer with realistic textures using only a style text description (instead of requiring a reference style image). However, the ground semantics of objects in the style transfer output is lost due to style spill-over on salient and background objects (content mismatch) or over-stylization. To solve this, we propose Semantic CLIPStyler (Sem-CS), that performs semantic style transfer. Sem-CS first segments the content image into salient and non-salient objects and then transfers artistic style based on a given style text description. The semantic style transfer is achieved using global foreground loss (for salient objects) and global background loss (for non-salient objects). Our empirical results, including DISTS, NIMA and user study scores, show that our proposed framework yields superior qualitative and quantitative performance. Our code is available at github.com/chandagrover/sem-cs.

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  • SPS
    Members: Free
    IEEE Members: $11.00
    Non-members: $15.00