ItemConference Paper

Exploring Conditional Generative Models for Sketch-to-Image Translation: cGAN, cVAE, and Conditional Diffusion Models

Title
Exploring Conditional Generative Models for Sketch-to-Image Translation: cGAN, cVAE, and Conditional Diffusion Models
Creator
Jaisree, M.K.; Madhumitha, P.; Umamaheswari, D.; Loveline Zeema, J.
Description
Creating realistic facial pictures from hand-drawn sketches is of significant utility in forensic investigations because eyewitness drawings are frequently the only visual leads for suspect identification. Turning a hand-drawn sketch into a realistic image is a difficult task. This is because sketches lack detailed information, they are abstracted, and ambiguous. Most of the conventional image creation and generation techniques tend to lose facial structure, identity, and realism. This makes it a great area for generative AI. This paper is a comparative analysis of three generative models: Conditional GANs, Conditional VAEs, and Conditional Diffusion Models. We evaluate these models on the sketch-to-image synthesis problem using the CUHK Face Sketch Dataset. We recognize and compare how every model handles the challenge of generating images from sketches of faces, with an emphasis on producing realistic images, maintaining identity and diversity. The paper demonstrates the advantages and disadvantages of each approach. It also offers insights into their usefulness for forensic applications and suggests directions for future improvements through combined or specialized generative structures. 2025 IEEE.
Source
3rd International Conference on Emerging Applications of Material Science and Technology, ICEAMST 2025;pp.1177-1182
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Conditional Diffusion model; Conditional GAN; Conditional VAE; CUHK dataset; Forensic application; Sketch-to-Image synthesis
Coverage
Jaisree M.K., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India; Madhumitha P., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India; Umamaheswari D., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India; Loveline Zeema J., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833158707-9;
Format
online
Language
English
Type
Conference paper

Citation

Jaisree, M.K.; Madhumitha, P.; Umamaheswari, D.; Loveline Zeema, J., “Exploring Conditional Generative Models for Sketch-to-Image Translation: cGAN, cVAE, and Conditional Diffusion Models,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25977.

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