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Main Authors: Chen, Dar-Yen, Bhunia, Ayan Kumar, Koley, Subhadeep, Sain, Aneeshan, Chowdhury, Pinaki Nath, Song, Yi-Zhe
Format: Preprint
Published: 2023
Subjects:
Online Access:https://arxiv.org/abs/2312.04364
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author Chen, Dar-Yen
Bhunia, Ayan Kumar
Koley, Subhadeep
Sain, Aneeshan
Chowdhury, Pinaki Nath
Song, Yi-Zhe
author_facet Chen, Dar-Yen
Bhunia, Ayan Kumar
Koley, Subhadeep
Sain, Aneeshan
Chowdhury, Pinaki Nath
Song, Yi-Zhe
contents In this paper, we democratise caricature generation, empowering individuals to effortlessly craft personalised caricatures with just a photo and a conceptual sketch. Our objective is to strike a delicate balance between abstraction and identity, while preserving the creativity and subjectivity inherent in a sketch. To achieve this, we present Explicit Rank-1 Model Editing alongside single-image personalisation, selectively applying nuanced edits to cross-attention layers for a seamless merge of identity and style. Additionally, we propose Random Mask Reconstruction to enhance robustness, directing the model to focus on distinctive identity and style features. Crucially, our aim is not to replace artists but to eliminate accessibility barriers, allowing enthusiasts to engage in the artistry.
format Preprint
id arxiv_https___arxiv_org_abs_2312_04364
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle DemoCaricature: Democratising Caricature Generation with a Rough Sketch
Chen, Dar-Yen
Bhunia, Ayan Kumar
Koley, Subhadeep
Sain, Aneeshan
Chowdhury, Pinaki Nath
Song, Yi-Zhe
Computer Vision and Pattern Recognition
In this paper, we democratise caricature generation, empowering individuals to effortlessly craft personalised caricatures with just a photo and a conceptual sketch. Our objective is to strike a delicate balance between abstraction and identity, while preserving the creativity and subjectivity inherent in a sketch. To achieve this, we present Explicit Rank-1 Model Editing alongside single-image personalisation, selectively applying nuanced edits to cross-attention layers for a seamless merge of identity and style. Additionally, we propose Random Mask Reconstruction to enhance robustness, directing the model to focus on distinctive identity and style features. Crucially, our aim is not to replace artists but to eliminate accessibility barriers, allowing enthusiasts to engage in the artistry.
title DemoCaricature: Democratising Caricature Generation with a Rough Sketch
topic Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2312.04364