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arxiv: 2604.08836 · v1 · submitted 2026-04-10 · 💻 cs.CV

CatalogStitch: Dimension-Aware and Occlusion-Preserving Object Compositing for Catalog Image Generation

Sanyam Jain , Pragya Kandari , Manit Singhal , He Zhang , Soo Ye Kim This is my paper

Pith reviewed 2026-05-10 16:41 UTC · model grok-4.3

classification 💻 cs.CV
keywords object compositingcatalog image generationdimension-aware maskingocclusion preservationgenerative modelsimage editingmask computationhybrid restoration
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The pith

CatalogStitch uses automatic dimension-aware masking and occlusion-preserving restoration to eliminate manual edits in generative catalog image compositing.

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

The paper aims to show that two new techniques can remove the need for tedious manual adjustments when using generative models to insert products into catalog scenes. It demonstrates that a dimension-aware mask computation can adapt the placement area for products of varying sizes without user input, while an occlusion-aware hybrid restoration method ensures that any elements blocking the product are perfectly preserved pixel by pixel. A new benchmark called CatalogStitch-Eval tests these in realistic scenarios with aspect ratio mismatches and heavy occlusions. If correct, this would turn advanced AI compositing tools into practical, hands-off solutions for e-commerce and catalog production workflows.

Core claim

CatalogStitch introduces model-agnostic techniques consisting of a dimension-aware mask computation algorithm that automatically adapts the target region to different product dimensions and an occlusion-aware hybrid restoration method that guarantees pixel-perfect preservation of occluding elements, thereby automating corrections previously requiring manual intervention and enabling seamless object compositing in catalog image generation.

What carries the argument

The dimension-aware mask computation algorithm, which adapts the target region based on product dimensions, and the occlusion-aware hybrid restoration method, which preserves occluding elements by identifying and copying pixels.

If this is right

  • Users can insert products into backgrounds using only the product image and scene without adjusting masks.
  • Post-generation restoration of occluded parts is automated, removing post-editing needs.
  • Evaluations show consistent improvements when applied to ObjectStitch, OmniPaint, and InsertAnything.
  • CatalogStitch-Eval provides a benchmark for aspect-ratio mismatch and occlusion-heavy scenarios.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • These automation steps could extend to other generative image tasks involving object placement, such as scene editing in photography software.
  • If the methods prove robust across more models, they might standardize compositing pipelines in production environments.
  • Future work could test integration with real-time catalog generation systems for dynamic product catalogs.

Load-bearing premise

That feeding the automatically adjusted masks into existing compositing models will always yield seamless results without new artifacts and that the hybrid restoration method can reliably copy occluding pixels perfectly in all cases.

What would settle it

Applying the method to a catalog image with a large dimension mismatch and complex occlusions where the output still shows visible seams or altered occluding elements would disprove the claim.

Figures

Figures reproduced from arXiv: 2604.08836 by He Zhang, Manit Singhal, Pragya Kandari, Sanyam Jain, Soo Ye Kim.

Figure 1
Figure 1. Figure 1: Challenge 1: Product Dimension Mismatch. When re￾placing a product with different proportions, freeform and bound￾ing box masks distort the product shape. Our dimension-aware mask preserves correct proportions. Recent advances in generative AI, particularly diffusion￾based object compositing methods like ObjectStitch [8], Paint-by-Example [9], and AnyDoor [1], offer a promis￾ing foundation. These methods c… view at source ↗
Figure 2
Figure 2. Figure 2: Challenge 2: Occlusion Destruction. Foreground el￾ements (table/decor items) occluding the target product are cor￾rupted during compositing. Our hybrid restoration preserves them pixel-perfectly. rupted during compositing. 1.2 Our Contributions We present CatalogStitch, a collection of model-agnostic techniques that extend existing compositing methods for real-world catalog image generation: 1. Dimension-A… view at source ↗
Figure 3
Figure 3. Figure 3: CatalogStitch overview. Two lightweight, model-agnostic modules wrap a baseline compositor. The target mask is adapted to the replacement product ratio; occluders are segmented, cached, and inpainted away before compositing, then restored from the original pixels at the final step. Dimension-Aware Mask Computation Input masks Mt and Mp Measure bbox size, centroid, and aspect ratios ARt, ARp |ARt − ARp| ≤ τ… view at source ↗
Figure 4
Figure 4. Figure 4: Module-level flow charts. Left: dimension-aware mask computation preserves the original placement when the mismatch is small and otherwise expands the target region around the original centroid. Right: occlusion-aware restoration detects overlapping entities, caches their exact pixels, applies generative inpainting to remove occluders and expose a clean background, runs the compositor on the clean backgrou… view at source ↗
Figure 5
Figure 5. Figure 5: Dimension-aware mask computation across models. Left inputs: background and product. Mask overlay visualizes the dim-aware (blue) and bounding-box (pink) regions. Right: outputs under Freeform, BBox, and Dim-Aware masks for OmniPaint, In￾sertAnything, and ObjectStitch. Dim-aware masks preserve object proportions more reliably than Freeform and BBox masks across all models [PITH_FULL_IMAGE:figures/full_fig… view at source ↗
Figure 6
Figure 6. Figure 6: Occlusion-aware hybrid restoration across models. Left inputs: background and product. Mask overlay and occluder com￾posite are visualizations of the occlusion regions. Right: Freeform, BBox, and Dim-Aware outcomes for InsertAnything and ObjectStitch, shown before and after occluder restoration. Our pipeline segments occluders, caches their pixels, inpaints them away to expose a clean background for compos… view at source ↗
read the original abstract

Generative object compositing methods have shown remarkable ability to seamlessly insert objects into scenes. However, when applied to real-world catalog image generation, these methods require tedious manual intervention: users must carefully adjust masks when product dimensions differ, and painstakingly restore occluded elements post-generation. We present CatalogStitch, a set of model-agnostic techniques that automate these corrections, enabling user-friendly content creation. Our dimension-aware mask computation algorithm automatically adapts the target region to accommodate products with different dimensions; users simply provide a product image and background, without manual mask adjustments. Our occlusion-aware hybrid restoration method guarantees pixel-perfect preservation of occluding elements, eliminating post-editing workflows. We additionally introduce CatalogStitch-Eval, a 58-example benchmark covering aspect-ratio mismatch and occlusion-heavy catalog scenarios, together with supplementary PDF and HTML viewers. We evaluate our techniques with three state-of-the-art compositing models (ObjectStitch, OmniPaint, and InsertAnything), demonstrating consistent improvements across diverse catalog scenarios. By reducing manual intervention and automating tedious corrections, our approach transforms generative compositing into a practical, human-friendly tool for production catalog workflows.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces CatalogStitch, a set of model-agnostic techniques for automating object compositing in catalog image generation. It describes a dimension-aware mask computation algorithm that automatically adapts the target region to products of varying dimensions, eliminating manual mask adjustments, and an occlusion-aware hybrid restoration method that guarantees pixel-perfect preservation of occluding elements. The authors also present CatalogStitch-Eval, a new 58-example benchmark focused on aspect-ratio mismatch and occlusion-heavy scenarios, along with supplementary viewers, and report consistent improvements when the techniques are applied to three existing compositing models (ObjectStitch, OmniPaint, and InsertAnything).

Significance. If the central claims hold, the work has clear practical significance for e-commerce catalog workflows by reducing manual post-processing in generative compositing pipelines. The model-agnostic design and release of a dedicated benchmark with viewers are strengths that could support reproducibility and future comparisons. These elements address a genuine usability gap between research prototypes and production use cases.

major comments (2)
  1. [occlusion-aware hybrid restoration method] In the description of the occlusion-aware hybrid restoration method: the claim that the method 'guarantees pixel-perfect preservation of occluding elements' is load-bearing for the central contribution. Because the underlying compositing models (ObjectStitch, OmniPaint, InsertAnything) can modify local illumination, shadows, and edge anti-aliasing, the restoration procedure must be shown to handle these changes without seams or inconsistencies. Please supply the precise algorithm (including any detection and copy steps), quantitative isolation of the restoration component via ablation, and analysis of failure cases such as partial transparency or strong shadow mismatch.
  2. [evaluation and CatalogStitch-Eval benchmark] In the evaluation section and CatalogStitch-Eval description: the benchmark contains only 58 examples and the text reports 'consistent improvements' without specifying the quantitative metrics employed (e.g., PSNR, SSIM, LPIPS, or user-study scores), statistical significance testing, or per-model numerical results. This information is required to substantiate the cross-model claim and to allow readers to judge the practical magnitude of the gains.
minor comments (2)
  1. The abstract references 'supplementary PDF and HTML viewers'; please add a brief description of their contents and how they can be accessed in the main text or a dedicated data section.
  2. [related work or experimental setup] Ensure that the original papers for ObjectStitch, OmniPaint, and InsertAnything are cited with full bibliographic details in the related-work or experimental-setup section.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment point-by-point below, providing clarifications and committing to revisions that strengthen the manuscript without altering its core claims.

read point-by-point responses

  1. Referee: [occlusion-aware hybrid restoration method] In the description of the occlusion-aware hybrid restoration method: the claim that the method 'guarantees pixel-perfect preservation of occluding elements' is load-bearing for the central contribution. Because the underlying compositing models (ObjectStitch, OmniPaint, InsertAnything) can modify local illumination, shadows, and edge anti-aliasing, the restoration procedure must be shown to handle these changes without seams or inconsistencies. Please supply the precise algorithm (including any detection and copy steps), quantitative isolation of the restoration component via ablation, and analysis of failure cases such as partial transparency or strong shadow mismatch.

    Authors: We agree that the pixel-perfect preservation claim requires explicit algorithmic detail and supporting evidence to be fully substantiated. The method first computes an occlusion mask by intersecting the dimension-aware insertion region with depth-aware or segmentation-based detection of foreground elements from the original background; it then performs a direct pixel copy from the source background into the generated composite for those masked regions, with optional boundary feathering to reduce edge artifacts. We will add the complete algorithm (including pseudocode for detection and copy steps) to the methods section. While the base models can alter illumination and shadows within the inserted object, the restoration targets only non-overlapping occluders, limiting seam issues to boundary transitions. In the revision we will include a dedicated ablation isolating the restoration component (with and without it) using LPIPS and user-study scores, plus a new failure-case analysis subsection covering partial transparency (handled via alpha-aware blending) and strong shadow mismatches (with visual examples and noted limitations). revision: yes

  2. Referee: [evaluation and CatalogStitch-Eval benchmark] In the evaluation section and CatalogStitch-Eval description: the benchmark contains only 58 examples and the text reports 'consistent improvements' without specifying the quantitative metrics employed (e.g., PSNR, SSIM, LPIPS, or user-study scores), statistical significance testing, or per-model numerical results. This information is required to substantiate the cross-model claim and to allow readers to judge the practical magnitude of the gains.

    Authors: We acknowledge that the current evaluation reporting is insufficiently detailed. CatalogStitch-Eval was intentionally constructed as a compact, targeted set of 58 examples emphasizing the precise failure modes (aspect-ratio mismatch and heavy occlusion) that arise in catalog workflows, rather than a general-purpose large-scale benchmark. In the revised manuscript we will explicitly state the metrics used: PSNR, SSIM, and LPIPS for objective assessment together with mean opinion scores from a 20-participant user study. We will insert a results table reporting per-model numerical values for ObjectStitch, OmniPaint, and InsertAnything (with and without CatalogStitch), and we will add statistical significance via paired t-tests. These additions will quantify the magnitude of the observed improvements and allow readers to assess practical impact. revision: yes

Circularity Check

0 steps flagged

No circularity: algorithmic procedures are self-contained and externally evaluated

full rationale

The paper describes two model-agnostic algorithmic procedures (dimension-aware mask computation and occlusion-aware hybrid restoration) plus a new 58-example benchmark. These are presented as practical automation steps for existing compositing models (ObjectStitch, OmniPaint, InsertAnything), with qualitative and quantitative evaluation on catalog scenarios. No equations, first-principles derivations, fitted parameters, or predictions are introduced that reduce to the method's own inputs by construction. The abstract and description contain no self-citations used as load-bearing justification, no uniqueness theorems, and no renaming of known results. The central claims concern engineering improvements that can be (and are) tested against external baselines, making the work self-contained against the circularity criteria.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper introduces algorithmic techniques without explicit free parameters, new physical entities, or non-standard axioms beyond routine assumptions of image processing and generative model compatibility.

axioms (1)
  • domain assumption Existing generative compositing models can accept modified input masks and post-processed outputs without retraining or loss of quality.
    Required for the model-agnostic claim to hold across ObjectStitch, OmniPaint, and InsertAnything.

pith-pipeline@v0.9.0 · 5513 in / 1202 out tokens · 47269 ms · 2026-05-10T16:41:22.183834+00:00 · methodology

discussion (0)

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Reference graph

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