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Hairstyle transfer is challenging due to hair structure differences in thesource and target hair. Therefore, we propose Latent Optimization of Hairstylesvia Orthogonalization (LOHO), an optimization-based approach using GANinversion to infill missing hair structure details in latent space duringhairstyle transfer. Our approach decomposes hair into three attributes:perceptual structure, appearance, and style, and includes tailored losses tomodel each of these attributes independently. Furthermore, we propose two-stageoptimization and gradient orthogonalization to enable disentangled latent spaceoptimization of our hair attributes. Using LOHO for latent space manipulation,users can synthesize novel photorealistic images by manipulating hairattributes either individually or jointly, transferring the desired attributesfrom reference hairstyles. LOHO achieves a superior FID compared with thecurrent state-of-the-art (SOTA) for hairstyle transfer. Additionally, LOHOpreserves the subject's identity comparably well according to PSNR and SSIMwhen compared to SOTA image embedding pipelines. Code is available athttps://github.com/dukebw/LOHO.

LOHO: Latent Optimization of Hairstyles via Orthogonalization