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REVIEW 2 major objections 2 minor 32 references

Improving Text-Instance Alignment Of Foreground Conditioned Out-Painting Via Customized Concept Embedding

T0 review · 2 major / 2 minor · reviewed 2026-06-27 · grok-4.3

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Pith's one-line read Customized concept embeddings reduce artifacts in foreground-conditioned outpainting by aligning text prompts with specific visual instances.

desk verdict The paper adds a CCE-Module to customize embeddings for less artifact-prone foreground outpainting, but the abstract leaves the size of the gain and comparisons to prior embedding tweaks unclear. read the letter →

arxiv 2606.10892 v1 pith:UYYYZQAG submitted 2026-06-09 cs.CV cs.AI

Yihao Zhao , Xuan Han , Bin He , Mingyu You This is my paper · ORCID
classification cs.CVcs.AI
keywords foregroundconditionedoutpaintingcustomizedconceptembeddingtext-instancealignmentdiffusionmodelsartifactreductionCCE-Diffusionimagegenerationsemantic
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper seeks to improve foreground conditioned outpainting by addressing artifacts that appear when text prompts generate backgrounds with elements semantically similar to the foreground object. It identifies the root cause as misalignment between the instance and the embeddings from generic text concepts. The proposed CCE-Diffusion framework introduces a CCE-Module that customizes these embeddings to better match the specific visual instance, using an Instance-Aware Loss for optimization and a Semantic-Preserving Prompt Template to avoid distorting other prompt words. This approach is presented as a plug-and-play addition that works with existing methods. A sympathetic reader would care because it promises higher quality, lower cost image generation for product displays without changing the underlying diffusion models.

What carries the argument

The CCE-Module, which customizes concept embeddings derived from text prompts to align with a given foreground instance.

What would settle it

Running the CCE-Module on a set of test images and finding that the number of artifact regions remains the same or increases compared to the baseline FCO method without the module.

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Extended reading notes

Core claim

The authors claim that by customizing concept embeddings with the CCE-Module to bridge generic noun semantics and specific visual instances, guided by an Instance-Aware Loss and protected by a Semantic-Preserving Prompt Template, the misalignment causing artifacts in text-driven FCO is corrected, leading to outputs with significantly reduced artifacts as shown in qualitative and quantitative evaluations.

Load-bearing premise

The premise that misalignment between the instance and text-derived concept embeddings is the primary cause of artifacts, and that customizing the embeddings will fix this without introducing new distortions.

Editorial extensions

If this is right

  • The CCE-Module integrates with various FCO methods as a plug-and-play component.
  • Outputs show significantly reduced artifacts in the synthesized backgrounds.
  • The Instance-Aware Loss guides optimization without requiring changes to the base diffusion model.
  • The Semantic-Preserving Prompt Template prevents distortion of other words in the prompt.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Similar customization techniques could apply to other diffusion-based image editing tasks involving specific objects.
  • This might allow for more automated e-commerce workflows where background generation is more reliable.
  • Further work could explore whether the module adapts well to complex scenes with multiple foreground elements.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes the Customized Concept Embedding Diffusion (CCE-Diffusion) framework for Foreground Conditioned Outpainting (FCO). It attributes semantic artifacts in generated backgrounds to misalignment between instance and text-derived concept embeddings, and introduces a CCE-Module to customize embeddings, guided by an Instance-Aware Loss and a Semantic-Preserving Prompt Template. The method is presented as plug-and-play for existing FCO approaches, with claims that both qualitative and quantitative evaluations show significant artifact reduction.

Significance. If the empirical claims hold, the work offers a modular, training-free enhancement to text-conditioned diffusion outpainting pipelines. This could be useful for e-commerce applications requiring background generation around product instances, as the plug-and-play design avoids retraining base models while targeting a specific failure mode.

major comments (2)
  1. [§4] §4 (Quantitative Evaluation): The central claim that CCE-Diffusion 'significantly reduces artifacts' rests on unspecified metrics, baselines, datasets, and controls. Without these details (e.g., how artifact regions are detected or scored, number of test instances, statistical tests), the strength of the evidence cannot be assessed and the improvement cannot be verified as load-bearing.
  2. [§3.2] §3.2 (Instance-Aware Loss): The premise that misalignment is the primary cause of artifacts and that the proposed loss fixes it without side effects is not supported by an ablation isolating the loss from the prompt template or module architecture. This is load-bearing because the entire causal story and plug-and-play claim depend on the loss being both necessary and sufficient.
minor comments (2)
  1. [Abstract] Abstract: The sentence claiming 'both qualitative and quantitative evaluations demonstrate...' should briefly name the evaluation protocol or at least the number of methods tested to give readers an immediate sense of scope.
  2. [§3] Notation: The distinction between 'concept embeddings' and 'instance embeddings' is used throughout but never given a precise mathematical definition or dimensionality; adding a short notation paragraph in §3 would improve clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address the two major comments below and will revise the manuscript to strengthen the presentation of our quantitative results and ablations.

read point-by-point responses

  1. Referee: [§4] §4 (Quantitative Evaluation): The central claim that CCE-Diffusion 'significantly reduces artifacts' rests on unspecified metrics, baselines, datasets, and controls. Without these details (e.g., how artifact regions are detected or scored, number of test instances, statistical tests), the strength of the evidence cannot be assessed and the improvement cannot be verified as load-bearing.

    Authors: We agree that §4 currently lacks the level of detail needed to fully evaluate the quantitative claims. In the revised manuscript we will expand this section to explicitly describe the artifact detection and scoring procedure, the evaluation metrics, the datasets used, the number of test instances, the full set of baselines, and any statistical tests applied. These additions will make the evidence load-bearing and verifiable. revision: yes

  2. Referee: [§3.2] §3.2 (Instance-Aware Loss): The premise that misalignment is the primary cause of artifacts and that the proposed loss fixes it without side effects is not supported by an ablation isolating the loss from the prompt template or module architecture. This is load-bearing because the entire causal story and plug-and-play claim depend on the loss being both necessary and sufficient.

    Authors: We acknowledge that an ablation isolating the Instance-Aware Loss from the Semantic-Preserving Prompt Template and module architecture is not present in the current version. To better substantiate the causal role of the loss, the revised manuscript will include such an ablation study. This will clarify whether the loss is necessary and sufficient for the observed artifact reduction while preserving the plug-and-play nature of the CCE-Module. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity detected

full rationale

The provided manuscript text consists of a high-level method description for a plug-and-play CCE-Module that customizes embeddings to address misalignment in FCO diffusion models, supported by claimed qualitative and quantitative evaluations. No equations, derivations, fitted parameters presented as predictions, or load-bearing self-citations appear in the abstract or described full text. The central claims rest on empirical improvements rather than any self-referential reduction of outputs to inputs by construction, making the work self-contained against external benchmarks with no circular steps.

Assumptions & free parameters 0 free parameters · 0 assumptions · 0 invented entities

No technical details available from abstract alone; cannot enumerate free parameters, axioms, or invented entities.

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Cite this review

Pith. "Pith review of Improving Text-Instance Alignment Of Foreground Conditioned Out-Painting Via Customized Concept Embedding." pith.science (2026). https://pith.science/paper/UYYYZQAG

@misc{pith2026260610892,
  author       = {Pith},
  title        = {Pith review of: Improving Text-Instance Alignment Of Foreground Conditioned Out-Painting Via Customized Concept Embedding},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UYYYZQAG}},
  note         = {Machine review of arXiv:2606.10892}
}
read the original abstract

To showcase products, merchants often incur substantial costs creating high-quality display images. Foreground Conditioned Outpainting (FCO) meets this demand, allowing users to create desired backgrounds for foreground instances at a low cost by adjusting the text prompt. However, existing text-driven FCO methods exhibit critical flaws in their outputs, most notably the presence of artifacts, which refer to regions in the synthesized background that share the same semantics as the foreground instance. Such artifacts diminish the object's prominence and degrade image quality. We attribute the issue to the misalignment between the given instance and text-derived concept embeddings. To address this, we propose the Customized Concept Embedding Diffusion (CCE-Diffusion) framework. Its core is a CCE-Module to customize concept embeddings, bridging the gap between generic noun semantics and a specific visual instance. An Instance-Aware Loss guides the module's optimization, while a Semantic-Preserving Prompt Template prevents customized embeddings from distorting other words in the prompt. Both qualitative and quantitative evaluations demonstrate that CCE-Diffusion significantly reduces artifacts in the outputs. As a plug-and-play component, the CCE-Module can integrate with various FCO methods, enhancing their performance.

Figures

Figures reproduced from arXiv: 2606.10892 by the authors.

Figure 1
Figure 1. Two types of the artifacts phenomenon. Artifacts are indicated by red dashed lines. However, the existing text-driven FCO methods still ex￾hibit critical flaws in their outputs, with artifacts [6, 1] rank￾ing among the most prominent issues. Artifacts refer to ob￾jects or regions in the synthesized background that have the same semantics as the foreground instance [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 3
Figure 3. The process by which four different templates in￾fluence the semantics of text prompts. Red denotes generic concept embeddings, while green represents the visual fea￾tures of an instance. to generate customized concept embeddings, and a novel Semantic-Preserving Prompt Template to ensure alignment with the input prompt y. The trained CCE-Module is designed as a plug-and-play component, allowing for easy integration … view at source ↗
Figure 5
Figure 5. Qualitative results of our method demonstrating: (a) controllability over different backgrounds, (b) robustness to object variations, and (c) diversity in generated results. Method Artifacts Image Quality SAM ↓ TI ↑ IR ↑ LA ↑ US ↑ BLD [1] 0.986 26.06 0.18 5.04 1.35 SD-Inpaint. [3] 0.317 26.26 0.35 5.19 3.41 PowerPaint [4] 0.820 26.13 0.34 5.05 2.66 LayerDiff. [5] 0.046 26.15 0.22 5.27 3.78 BrushNet [6] 0.190 26.62 0… view at source ↗
Figures from the paper (2 more)
Figure 6
Figure 6. Figure 6: Qualitative comparison for the ablation study on the proposed module and loss (a), and for different prompt tem￾plates (b) [PITH_FULL_IMAGE:figures/full_fig_p004_6.png]
Figure 7
Figure 7. Figure 7: Quantitative analysis across different cross-attention map resolutions. The x-axis indicates the map resolution, and the y-axis represents the corresponding metric score. metrics, and Ours (B-Net) reduces BrushNet’s artifacts while maintaining comparable quality. Compa…

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Reference graph

Works this paper leans on

32 extracted references · 2 canonical work pages

  1. [1]

    Improving Text-Instance Alignment Of Foreground Conditioned Out-Painting Via Customized Concept Embedding

    INTRODUCTION In fields like e-commerce, the high cost and slow pace of tradi- tional workflows struggle to meet the demand for high-quality product imagery. To address this, text-driven Foreground Conditioned Out-painting (FCO) technology has emerged as a powerful, low-cost solution. Prominent FCO approaches [ 1, 2, 3, 4, 5, 6, 7] address the task of synt...

    work page Pith review arXiv 2026

  2. [2]

    x should be artifact-free and conform to y

    METHOD Given an instance image c ∈ Rh×w×ch, the FCO model gen- erates an image x guided by a text prompt y. x should be artifact-free and conform to y. We adopt Stable Diffusion [ 3] as our base model which is trained with the ϵ − prediction objective, formulated as: Lϵ−pred = E(z0,y),t [ ∥ϵt − Φ(zt, y, t)∥2 2 ] (1) Here, zt is a noisy latent created by a...

  3. [3]

    EXPERIMENTS 3.1. Experimental Settings Dataset Our training dataset, derived from OpenImage v7 [ 22], contains 580K images with 800K high-quality in- Method Artifacts Image Quality SAM ↓ TI ↑ IR ↑ LA ↑ US ↑ Baseline [ 13] 0.224 26.24 0.54 5.29 4.33 + CCE-Module 0.115 25.41 0.45 5.14 4.20 Ours 0.086 26.37 0.63 5.33 4.45 Table 2. Quantitative results for th...

  4. [4]

    CONCLUSION In this work, to address the artifact issue in the FCO task, we propose a framework with a CCE-Module and the Instance- Aware Loss for precise instance-related customized concept embeddings, and a Semantic-Preserving Prompt Template to maintain textual semantics. Our method significantly re- duces artifacts and improves overall image quality wh...

    2024

  5. [5]

    Blended latent diffusion,

    Omri A vrahami, Ohad Fried, and Dani Lischinski, “Blended latent diffusion,” ACM Transactions on Graphics (TOG), vol. 42, no. 4, pp. 149:1–149:11, 2023

    2023

  6. [6]

    Blended diffusion for text-driven editing of natural im- ages,

    Omri A vrahami, Dani Lischinski, and Ohad Fried, “Blended diffusion for text-driven editing of natural im- ages,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp. 18187– 18197

    2022

  7. [7]

    High-resolution image synthesis with latent dif- fusion models,

    Robin Rombach, Andreas Blattmann, Dominik Lorenz, et al., “High-resolution image synthesis with latent dif- fusion models,” in IEEE/CVF Conference on Com- puter Vision and Pattern Recognition (CVPR), 2022, pp. 10674–10685

    2022

  8. [8]

    A task is worth one word: Learning with task prompts for high-quality versatile image inpainting,

    Junhao Zhuang, Yanhong Zeng, Wenran Liu, et al., “A task is worth one word: Learning with task prompts for high-quality versatile image inpainting,” in European Conference on Computer Vision (ECCV), 2024, pp. 195– 211

    2024

Show all 32 references
  1. [9]

    Transparent im- age layer diffusion using latent transparency,

    Lvmin Zhang and Maneesh Agrawala, “Transparent im- age layer diffusion using latent transparency,” ACM Transactions on Graphics (TOG), vol. 43, no. 4, pp. 100:1–100:15, 2024

    2024

  2. [10]

    BrushNet: A plug-and-play image inpainting model with decom- posed dual-branch diffusion,

    Xuan Ju, Xian Liu, Xintao Wang, et al., “BrushNet: A plug-and-play image inpainting model with decom- posed dual-branch diffusion,” in European Conference on Computer Vision (ECCV), 2024, pp. 150–168

    2024

  3. [11]

    Black Forest Labs, “Flux,” https://github.com/ black-forest-labs/flux , 2024

    2024

  4. [12]

    GLIDE: towards photorealistic image generation and editing with text-guided diffusion mod- els,

    Alexander Quinn Nichol, Prafulla Dhariwal, Aditya Ramesh, et al., “GLIDE: towards photorealistic image generation and editing with text-guided diffusion mod- els,” in International Conference on Machine Learning (ICML), 2022, vol. 162, pp. 16784–16804

    2022

  5. [13]

    Zero-shot text-to-image generation,

    Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, et al., “Zero-shot text-to-image generation,” in International Conference on Machine Learning (ICML), 2021, vol. 139, pp. 8821–8831

    2021

  6. [14]

    PixArt-α: Fast training of diffusion transformer for photorealistic text-to-image synthesis,

    Junsong Chen, Jincheng Yu, Chongjian Ge, et al., “PixArt-α: Fast training of diffusion transformer for photorealistic text-to-image synthesis,” in International Conference on Learning Representations (ICLR), 2024

    2024

  7. [15]

    Null- text inversion for editing real images using guided dif- fusion models,

    Ron Mokady, Amir Hertz, Kfir Aberman, et al., “Null- text inversion for editing real images using guided dif- fusion models,” in IEEE/CVF Conference on Com- puter Vision and Pattern Recognition (CVPR), 2023, pp. 6038–6047

    2023

  8. [16]

    ProxEdit: improving tuning-free real image editing with proximal guidance,

    Ligong Han, Song Wen, Qi Chen, et al., “ProxEdit: improving tuning-free real image editing with proximal guidance,” in IEEE/CVF Winter Conference on Appli- cations of Computer Vision (W ACV), 2024, pp. 4279– 4289

    2024

  9. [17]

    Adding conditional control to text-to-image diffusion models,

    Lvmin Zhang, Anyi Rao, and Maneesh Agrawala, “Adding conditional control to text-to-image diffusion models,” in IEEE/CVF International Conference on Computer Vision (ICCV), 2023, pp. 3813–3824

    2023

  10. [18]

    Imagen Editor and EditBench: advancing and evaluat- ing text-guided image inpainting,

    Su Wang, Chitwan Saharia, Ceslee Montgomery, et al., “Imagen Editor and EditBench: advancing and evaluat- ing text-guided image inpainting,” in IEEE/CVF Con- ference on Computer Vision and Pattern Recognition (CVPR), 2023, pp. 18359–18369

    2023

  11. [19]

    Attend- and-excite: Attention-based semantic guidance for text- to-image diffusion models,

    Hila Chefer, Yuval Alaluf, Yael Vinker, et al., “Attend- and-excite: Attention-based semantic guidance for text- to-image diffusion models,” ACM Transactions on Graphics (TOG), vol. 42, no. 4, pp. 148:1–148:10, 2023

    2023

  12. [20]

    Prompt-to-prompt image editing with cross-attention control,

    Amir Hertz, Ron Mokady, Jay Tenenbaum, et al., “Prompt-to-prompt image editing with cross-attention control,” in International Conference on Learning Rep- resentations (ICLR), 2023

    2023

  13. [21]

    Learning transferable visual models from natural lan- guage supervision,

    Alec Radford, Jong Wook Kim, Chris Hallacy, et al., “Learning transferable visual models from natural lan- guage supervision,” in International Conference on Ma- chine Learning (ICML), 2021, vol. 139, pp. 8748–8763

    2021

  14. [22]

    Plug-and-play diffusion features for text-driven image- to-image translation,

    Narek Tumanyan, Michal Geyer, Shai Bagon, et al., “Plug-and-play diffusion features for text-driven image- to-image translation,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, pp. 1921–1930

    2023

  15. [23]

    An im- age is worth one word: Personalizing text-to-image gen- eration using textual inversion,

    Rinon Gal, Yuval Alaluf, Yuval Atzmon, et al., “An im- age is worth one word: Personalizing text-to-image gen- eration using textual inversion,” in International Con- ference on Learning Representations (ICLR), 2023

    2023

  16. [24]

    Subject- Diffusion: open domain personalized text-to-image gen- eration without test-time fine-tuning,

    Jian Ma, Junhao Liang, Chen Chen, et al., “Subject- Diffusion: open domain personalized text-to-image gen- eration without test-time fine-tuning,” in ACM SIG- GRAPH 2024 Conference Papers, 2024, p. 25

    2024

  17. [25]

    BLIP- Diffusion: pre-trained subject representation for control- lable text-to-image generation and editing,

    Dongxu Li, Junnan Li, and Steven C. H. Hoi, “BLIP- Diffusion: pre-trained subject representation for control- lable text-to-image generation and editing,” in Advances in Neural Information Processing Systems (NeurIPS), 2023

    2023

  18. [26]

    From colouring-in to pointillism: revisiting semantic segmen- tation supervision,

    Rodrigo Benenson and Vittorio Ferrari, “From colouring-in to pointillism: revisiting semantic segmen- tation supervision,” CoRR, vol. abs/2210.14142, 2022

    2022

  19. [27]

    Decoupled weight decay regularization,

    Ilya Loshchilov and Frank Hutter, “Decoupled weight decay regularization,” in International Conference on Learning Representations (ICLR), 2019

    2019

  20. [28]

    De- noising diffusion implicit models,

    Jiaming Song, Chenlin Meng, and Stefano Ermon, “De- noising diffusion implicit models,” in International Con- ference on Learning Representations (ICLR), 2021

    2021

  21. [29]

    Segment anything,

    Alexander Kirillov, Eric Mintun, Nikhila Ravi, et al., “Segment anything,” in IEEE/CVF International Con- ference on Computer Vision (ICCV), 2023, pp. 3992– 4003

    2023

  22. [30]

    Grounding DINO: marrying DINO with grounded pre- training for open-set object detection,

    Shilong Liu, Zhaoyang Zeng, Tianhe Ren, et al., “Grounding DINO: marrying DINO with grounded pre- training for open-set object detection,” in European Conference on Computer Vision (ECCV), 2024, pp. 38– 55

    2024

  23. [31]

    ImageRe- ward: learning and evaluating human preferences for text-to-image generation,

    Jiazheng Xu, Xiao Liu, Yuchen Wu, et al., “ImageRe- ward: learning and evaluating human preferences for text-to-image generation,” in Advances in Neural In- formation Processing Systems (NeurIPS), 2023

    2023

  24. [32]

    LAION-5B: an open large-scale dataset for training next generation image-text models,

    Christoph Schuhmann, Romain Beaumont, Richard Vencu, et al., “LAION-5B: an open large-scale dataset for training next generation image-text models,” in Advances in Neural Information Processing Systems (NeurIPS), 2022

    2022

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