Pith. sign in

REVIEW 2 cited by

DialogPaint: A Dialog-based Image Editing Model

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2303.10073 v2 pith:RCT7KVES submitted 2023-03-17 cs.CV

classification cs.CV
keywords imagedialogpainteditingapproachdialogueinteractionsmodelusers
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

We introduce DialogPaint, a novel framework that bridges conversational interactions with image editing, enabling users to modify images through natural dialogue. By integrating a dialogue model with the Stable Diffusion image transformation technique, DialogPaint offers a more intuitive and interactive approach to image modifications. Our method stands out by effectively interpreting and executing both explicit and ambiguous instructions, handling tasks such as object replacement, style transfer, and color modification. Notably, DialogPaint supports iterative, multi-round editing, allowing users to refine image edits over successive interactions. Comprehensive evaluations highlight the robustness and versatility of our approach, marking a significant advancement in dialogue-driven image editing.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Early Timestep Zero-Shot Candidate Selection for Instruction-Guided Image Editing

    cs.CV 2025-04 conditional novelty 7.0 of 10

    ELECT selects the best random seed for instruction-guided image editing by scoring background consistency from early diffusion latents, reducing inference cost by about 41 percent and turning about 40 percent of previ...

  2. GenTune: Toward Traceable Prompts to Improve Controllability of Image Refinement in Environment Design

    cs.HC 2025-08 conditional novelty 5.0 of 10

    GenTune improves AI image refinement by tracing image regions back to prompt labels and allowing element-level, semantic-guided edits.

Pith tools