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MureObjectStitch: Multi-reference Image Composition

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arxiv 2411.07462 v3 pith:YRJPDJPE submitted 2024-11-12 cs.CV

Jiaxuan Chen , Bo Zhang , Qingdong He , Jinlong Peng , Li Niu This is my paper
classification cs.CV
keywords foregroundimagemodelcompositionobjectgenerativeimagesmulti-reference
verification ladder T0 review T1 audit T2 compute T3 formal
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Generative image composition aims to regenerate the given foreground object in the background image to produce a realistic composite image. The existing methods are struggling to preserve the foreground details and adjust the foreground pose/viewpoint at the same time. In this work, we propose an effective finetuning strategy for generative image composition model, in which we finetune a pretrained model using one or more images containing the same foreground object. Moreover, we propose a multi-reference strategy, which allows the model to take in multiple reference images of the foreground object. The experiments on MureCOM dataset verify the effectiveness of our method. The code and model have been released at https://github.com/bcmi/MureObjectStitch-Image-Composition.

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Cited by 3 Pith papers

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

  1. HOComp: Interaction-Aware Human-Object Composition

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A diffusion-transformer method that composes a foreground object into a human image with MLLM-chosen interaction regions, pose keypoint supervision, and appearance/background consistency losses, plus a new paired dataset.

  2. ORIDa: Object-centric Real-world Image Composition Dataset

    cs.CV 2025-06 conditional novelty 6.0 of 10

    ORIDa is a public real-world dataset of 200 objects in 30,000+ images with multiple positions per scene, designed for object compositing training and evaluation.

  3. MV-CoLight: Efficient Object Compositing with Consistent Lighting and Shadow Generation

    cs.CV 2025-05 conditional novelty 6.0 of 10

    A feed-forward two-stage compositing framework that harmonizes inserted objects across views using a Hilbert-ordered Gaussian color mapping, trained and evaluated on a new 480k-scene synthetic dataset.

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