Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Jaisree, M.K.; Madhumitha, P.; Umamaheswari, D.; Loveline Zeema, J. Title A name given to the resource Exploring Conditional Generative Models for Sketch-to-Image Translation: cGAN, cVAE, and Conditional Diffusion Models Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 3rd International Conference on Emerging Applications of Material Science and Technology, ICEAMST 2025;pp.1177-1182 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICEAMST67459.2025.11335717" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICEAMST67459.2025.11335717</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105033674265?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105033674265?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 Description An account of the resource 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. Subject The topic of the resource Conditional Diffusion model; Conditional GAN; Conditional VAE; CUHK dataset; Forensic application; Sketch-to-Image synthesis Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833158707-9; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online