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Does FLUX Already Know How to Perform Physically Plausible Image Composition?

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 7.3Download Report PDF
Image EditingImage CompositionDiffusion Models
Abstract:

Image composition aims to seamlessly insert a user-specified object into a new scene, but existing models struggle with complex lighting (e.g., accurate shadows, water reflections) and diverse, high-resolution inputs. Modern text-to-image diffusion models (e.g., SD3.5, FLUX) already encode essential physical and resolution priors, yet lack a framework to unleash them without resorting to latent inversion, which often locks object poses into contextually inappropriate orientations, or brittle attention surgery. We propose SHINE, a training-free framework for Seamless, High-fidelity Insertion with Neutralized Errors. SHINE introduces manifold-steered anchor loss, leveraging pretrained customization adapters (e.g., IP-Adapter) to guide latents for faithful subject representation while preserving background integrity. Artifact-suppression guidance and adaptive background blending are proposed to further eliminate low-quality outputs and visible seams. To address the lack of rigorous benchmarks, we introduce ComplexCompo, featuring diverse resolutions and challenging conditions such as low lighting, strong illumination, intricate shadows, and reflective surfaces. Experiments on ComplexCompo and DreamEditBench show state-of-the-art performance on standard metrics (e.g., DINOv2) and human-aligned scores (e.g., DreamSim, ImageReward, VisionReward). Code and benchmark will be publicly available upon publication.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

SHINE proposes a training-free framework for seamless object insertion into complex scenes, emphasizing physically plausible lighting effects such as shadows and reflections. The paper resides in the 'Training-Free Diffusion Insertion' leaf, which contains only three papers including SHINE itself. This is a relatively sparse research direction within the broader taxonomy of 50 papers across 15 leaf nodes, suggesting that training-free approaches leveraging pretrained diffusion models without fine-tuning remain an emerging area. The sibling papers in this leaf represent the most directly comparable prior work in this specific methodological niche.

The taxonomy tree reveals that SHINE's leaf sits within 'Diffusion-Based Object Insertion', which also includes 'Trained Diffusion Insertion Models' (four papers) and 'Spatial and Geometric Control for Insertion' (three papers). Neighboring branches address 'Neural 3D Representation-Based Insertion' (four papers using NeRF or Gaussian splatting) and 'Video Object Insertion' (one paper). The taxonomy's scope notes clarify that training-free methods exclude fine-tuning approaches, while the broader 'Generative Object Insertion Methods' branch excludes compositional scene generation from scratch. SHINE's focus on complex lighting conditions and high-resolution inputs positions it at the intersection of training-free flexibility and physical realism, a boundary less explored than either pure training-free methods or fully trained models.

Among 30 candidates examined, none clearly refute any of SHINE's three contributions: the training-free framework itself, the ComplexCompo benchmark, and the Manifold-Steered Anchor loss. Each contribution was assessed against 10 candidates with zero refutable overlaps identified. The framework contribution appears most novel given the sparse training-free leaf (only two sibling papers). The benchmark contribution addresses a documented gap in rigorous evaluation under challenging lighting and resolution conditions. The MSA loss mechanism, leveraging pretrained customization adapters for latent guidance, shows no direct precedent among the examined candidates, though the limited search scope (30 papers) means exhaustive coverage of adapter-based guidance techniques cannot be claimed.

Based on the limited literature search of 30 semantically related candidates, SHINE's contributions appear substantively novel within the training-free diffusion insertion space. The sparse taxonomy leaf and absence of refutable overlaps suggest meaningful differentiation from prior work, though the analysis cannot rule out relevant methods outside the top-30 semantic matches or recent concurrent developments not captured in this search scope.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
30
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: seamless object insertion into complex scenes. The field has organized itself around three main branches. Generative Object Insertion Methods form the largest branch, encompassing diffusion-based approaches (both training-free methods like FreeInsert[15] and fine-tuned variants), GAN-based techniques such as GIRAFFE[16], and specialized insertion frameworks including AnyDoor[34] for identity-preserving insertion and VideoAnyDoor[13] for temporal consistency. Scene Composition and Understanding addresses the complementary challenge of analyzing and reasoning about spatial arrangements, covering placement prediction (Generative Location Modeling[5]), 3D scene representations (3DSceneEditor[10]), and multimodal recognition systems (Multimodal Scene Recognition[8]). Supporting Techniques and Applications bridges these areas with relighting methods (Relighting Object Insertions[2]), augmentation strategies (Visual Context Augmentation[22]), and domain-specific tools for interactive design and AI-augmented pipelines. A particularly active line of work explores training-free diffusion insertion, balancing flexibility against physical realism. Methods like Photorealistic Object Insertion[19] and FreeInsert[15] avoid costly fine-tuning but often struggle with lighting consistency and geometric plausibility. FLUX Physical Composition[0] sits within this training-free cluster, emphasizing physically grounded composition that addresses shadow casting, perspective alignment, and material interaction—challenges that nearby works like Photorealistic Object Insertion[19] tackle through post-processing refinements. In contrast, approaches such as Generative Object Insertion[3] and Insert Anything[12] invest in learned priors to achieve tighter scene integration at the cost of reduced generalization. The tension between zero-shot adaptability and scene-aware realism remains a central open question, with recent efforts exploring hybrid strategies that combine diffusion priors with explicit 3D reasoning or semantic guidance to bridge this gap.

Claimed Contributions

SHINE: training-free image composition framework

The authors introduce SHINE, a training-free framework that leverages pretrained text-to-image diffusion models (e.g., FLUX, SD3.5) to perform physically plausible image composition without requiring model retraining or latent inversion. SHINE comprises three core components: Manifold-Steered Anchor loss, Degradation-Suppression Guidance, and Adaptive Background Blending.

10 retrieved papers
ComplexCompo benchmark dataset

The authors present ComplexCompo, a new benchmark dataset for evaluating image composition methods under challenging real-world conditions. Unlike existing benchmarks with fixed 512×512 resolution, ComplexCompo includes 300 composition pairs with varying resolutions, both landscape and portrait orientations, and complex lighting scenarios.

10 retrieved papers
Manifold-Steered Anchor (MSA) loss

The authors propose MSA loss, a novel optimization objective that uses pretrained open-domain customization adapters to steer noisy latents toward faithfully representing the reference subject while maintaining the structural integrity of the background scene. This approach avoids the limitations of traditional inversion-based methods.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

SHINE: training-free image composition framework

The authors introduce SHINE, a training-free framework that leverages pretrained text-to-image diffusion models (e.g., FLUX, SD3.5) to perform physically plausible image composition without requiring model retraining or latent inversion. SHINE comprises three core components: Manifold-Steered Anchor loss, Degradation-Suppression Guidance, and Adaptive Background Blending.

Contribution

ComplexCompo benchmark dataset

The authors present ComplexCompo, a new benchmark dataset for evaluating image composition methods under challenging real-world conditions. Unlike existing benchmarks with fixed 512×512 resolution, ComplexCompo includes 300 composition pairs with varying resolutions, both landscape and portrait orientations, and complex lighting scenarios.

Contribution

Manifold-Steered Anchor (MSA) loss

The authors propose MSA loss, a novel optimization objective that uses pretrained open-domain customization adapters to steer noisy latents toward faithfully representing the reference subject while maintaining the structural integrity of the background scene. This approach avoids the limitations of traditional inversion-based methods.

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