Representative Feature Extraction During Diffusion Process for Sketch Extraction with One Example

Autor:	Yun, Kwan, Kim, Youngseo, Seo, Kwanggyoon, Seo, Chang Wook, Noh, Junyong
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Artificial Intelligence Computer Science - Graphics 68T01 I.4.9
Druh dokumentu:	Working Paper
Popis:	We introduce DiffSketch, a method for generating a variety of stylized sketches from images. Our approach focuses on selecting representative features from the rich semantics of deep features within a pretrained diffusion model. This novel sketch generation method can be trained with one manual drawing. Furthermore, efficient sketch extraction is ensured by distilling a trained generator into a streamlined extractor. We select denoising diffusion features through analysis and integrate these selected features with VAE features to produce sketches. Additionally, we propose a sampling scheme for training models using a conditional generative approach. Through a series of comparisons, we verify that distilled DiffSketch not only outperforms existing state-of-the-art sketch extraction methods but also surpasses diffusion-based stylization methods in the task of extracting sketches. Comment: 8 pages(main paper), 8 pages(supplementary material)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2401.04362 Zobrazit plný text záznamu View this record from Arxiv