arXiv Open Access 2022

Deep Image Style Transfer from Freeform Text

Tejas Santanam Mengyang Liu Jiangyue Yu Zhaodong Yang

Lihat Sumber

Abstrak

This paper creates a novel method of deep neural style transfer by generating style images from freeform user text input. The language model and style transfer model form a seamless pipeline that can create output images with similar losses and improved quality when compared to baseline style transfer methods. The language model returns a closely matching image given a style text and description input, which is then passed to the style transfer model with an input content image to create a final output. A proof-of-concept tool is also developed to integrate the models and demonstrate the effectiveness of deep image style transfer from freeform text.

Topik & Kata Kunci

cs.CV cs.CL cs.LG

Penulis (4)

Tejas Santanam

Mengyang Liu

Jiangyue Yu

Zhaodong Yang

Format Sitasi

APA MLA BibTeX

Santanam, T., Liu, M., Yu, J., Yang, Z. (2022). Deep Image Style Transfer from Freeform Text. https://arxiv.org/abs/2212.06868

Akses Cepat

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Informasi Jurnal

Tahun Terbit: 2022
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓