See and Fix the Flaws: Enabling VLMs and Diffusion Models to Comprehend Visual Artifacts via Agentic Data Synthesis
Despite recent advances in diffusion models, AI generated images still often contain visual artifacts that compromise realism. Although more thorough pre-training and bigger models might reduce artifacts, there is no assurance that they can be completely eliminated, which makes artifact mitigation a highly crucial area of study. Previous artifact-aware methodologies depend on human-labeled artifact datasets, which are costly and difficult to scale, underscoring the need for an automated approach to reliably acquire artifact-annotated datasets. In this paper, we propose ArtiAgent, which efficiently creates pairs of real and artifact-injected images. It comprises three agents: a perception agent that recognizes and grounds entities and subentities from real images, a synthesis agent that introduces artifacts via artifact injection tools through novel patch-wise embedding manipulation within a diffusion transformer, and a curation agent that filters the synthesized artifacts and generates both local and global explanations for each instance. Using ArtiAgent, we synthesize 100K images with rich artifact annotations and demonstrate both efficacy and versatility across diverse applications. Code is available at link.
699e74f6dfbcf0b800aecb74
Jaehyun Park
claimed_verified
2026-02-25T17:29:19.146Z
6682429bb1f69d3cd0104389
/avatars/f49826c08886760d6bf4c35b0e47e7c8.svg
Jaehyun Park
Cabbalett
user
Cabbalett
699e74f6dfbcf0b800aecb75
Minyoung Ahn
699e74f6dfbcf0b800aecb76
Minkyu Kim
claimed_verified
2026-03-10T09:46:23.595Z
64ae3a94714bad736d633e1b
/avatars/8ad6dc5589440c089060bcf064d68234.svg
Minkyu Kim
kimmk135
user
kimmk135
699e74f6dfbcf0b800aecb77
Jonghyun Lee
claimed_verified
2026-02-25T17:29:16.907Z
6632e41743f364c13e3418cd
/avatars/e0394f1012d38ea354ea54d53d6c5e2f.svg
Jonghyun Lee
Jhyun17
user
Jhyun17
699e74f6dfbcf0b800aecb78
Jae-Gil Lee
699e74f6dfbcf0b800aecb79
Dongmin Park
See and Fix the Flaws: Enabling VLMs and Diffusion Models to Comprehend Visual Artifacts via Agentic Data Synthesis
Despite recent advances in diffusion models, AI generated images still often contain visual artifacts that compromise realism. Although more thorough pre-training and bigger models might reduce artifacts, there is no assurance that they can be completely eliminated, which makes artifact mitigation a highly crucial area of study. Previous artifact-aware methodologies depend on human-labeled artifact datasets, which are costly and difficult to scale, underscoring the need for an automated approach to reliably acquire artifact-annotated datasets. In this paper, we propose ArtiAgent, which efficiently creates pairs of real and artifact-injected images. It comprises three agents: a perception agent that recognizes and grounds entities and subentities from real images, a synthesis agent that introduces artifacts via artifact injection tools through novel patch-wise embedding manipulation within a diffusion transformer, and a curation agent that filters the synthesized artifacts and generates both local and global explanations for each instance. Using ArtiAgent, we synthesize 100K images with rich artifact annotations and demonstrate both efficacy and versatility across diverse applications. Code is available at link.
ArtiAgent automates the creation of real-artifact image pairs using three agents for perception, synthesis, and curation in diffusion transformers.
diffusion transformer artifact injection tools patch-wise embedding manipulation artifact-aware methodologies artifact-annotated datasets perception agent synthesis agent curation agent