Kiymet Akdemir

dblp:331/3781 · DBLP profile ↗
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2ranked-venue papers
2as first author
2since 2021 · last 2026
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Visual content generation and editing · 100%
Artificial intelligence
1 paper
Generative modeling · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Visual content generation and editing
image editing
1.012026
Plot'n Polish: Zero-Shot Story Visualization and Disentangled Editing with Text-to-Image Diffusion Models · AAAI 2026
Visual content generation and editing › multimodal content generation
story visualization
1.012026
Plot'n Polish: Zero-Shot Story Visualization and Disentangled Editing with Text-to-Image Diffusion Models · AAAI 2026
Visual content generation and editing › image generation › text-to-image generation
text-to-image diffusion
1.012026
Plot'n Polish: Zero-Shot Story Visualization and Disentangled Editing with Text-to-Image Diffusion Models · AAAI 2026
Machine learning › Generative modeling
diffusion model
0.312026
Plot'n Polish: Zero-Shot Story Visualization and Disentangled Editing with Text-to-Image Diffusion Models · AAAI 2026

Methods — techniques the papers use, named apart from their topics

zero-shot generation · 2.0text-to-image diffusion · 2.0
YearPublicationVenuePosition
2026 Plot'n Polish: Zero-Shot Story Visualization and Disentangled Editing with Text-to-Image Diffusion Models
abstract
Text-to-image diffusion models have demonstrated significant capabilities to generate diverse and detailed visuals in various domains, and story visualization is emerging as a particularly promising application. However, as their use in real-world creative domains increases, the need for providing enhanced control, refinement, and the ability to modify images post-generation in a consistent manner becomes an important challenge. Existing methods often lack the flexibility to apply fine or coarse edits while maintaining visual and narrative consistency across multiple frames, preventing creators from seamlessly crafting and refining their visual stories. To address these challenges, we introduce Plot'n Polish, a zero-shot framework that enables consistent story generation and provides fine-grained control over story visualizations at various levels of detail.
Kiymet Akdemir, Jing Shi 0005, Kushal Kafle, Brian L. Price, Pinar Yanardag Delul
AAAI1
2024 ORACLE: Leveraging Mutual Information for Consistent Character Generation with LoRAs in Diffusion Models
Kiymet Akdemir, Pinar Yanardag Delul
ICCC1