REVIEW 3 cited by
OPA: Object Placement Assessment Dataset
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
read the original abstract
Image composition aims to generate realistic composite image by inserting an object from one image into another background image, where the placement (e.g., location, size, occlusion) of inserted object may be unreasonable, which would significantly degrade the quality of the composite image. Although some works attempted to learn object placement to create realistic composite images, they did not focus on assessing the plausibility of object placement. In this paper, we focus on object placement assessment task, which verifies whether a composite image is plausible in terms of the object placement. To accomplish this task, we construct the first Object Placement Assessment (OPA) dataset consisting of composite images and their rationality labels. We also propose a simple yet effective baseline for this task. Dataset is available at https://github.com/bcmi/Object-Placement-Assessment-Dataset-OPA.
Forward citations
Cited by 3 Pith papers
-
Assistant Placement Aria: A Benchmark for Egocentric Placement Assistance
A new 250-scene egocentric benchmark annotates plausible locations for panel, sitting, and TV placement in 2D and 3D with text descriptions.
-
Learning to Understand Body Language from Flight through Robust 3D Avatar Placing
A new dataset places 3D avatars into real drone video with metric placement, and training on it improves long-range human intent classification by 15–26 points.
-
CAD2DMD-SET: Synthetic Generation Tool of Digital Measurement Device CAD Model Datasets for fine-tuning Large Vision-Language Models
A synthetic data pipeline for digital measurement devices plus a real-image benchmark improves LVLM reading performance from 32.92% to 96.04% ANLS for InternVL.
Discussion (0). Continue with ORCID to comment.