OSInsert: A Dual-Stage Composite Imaging Framework

• OSInsert is a generative image composition framework that decouples spatial adaptation (authenticity) and detail preservation (fidelity) to synthesize realistic composite images.
• It employs a two-stage pipeline using pre-trained diffusion models and the SAM for pixel-level mask extraction to precisely blend foreground objects into varied backgrounds.
• Comparative analysis demonstrates that OSInsert overcomes the limitations of single-stage methods by effectively balancing pose adaptation and texture fidelity in complex compositing scenarios.

OSInsert is a generative image composition framework designed to achieve high authenticity and high fidelity when synthesizing composite images that insert a reference foreground object into an arbitrary background. The system targets scenarios where the foreground reference and desired insertion context may exhibit substantial pose, viewpoint, or illumination discrepancies. OSInsert explicitly decouples the challenge of spatial adaptation (authenticity) from detail preservation (fidelity), employing a two-stage inference pipeline that leverages pre-trained diffusion models and pixel-level mask extraction. This approach addresses the limitations of prior single-stage methods, which generally fail to reconcile both objectives simultaneously (Wang et al., 23 Feb 2026).

1. Problem Definition and Motivation

OSInsert addresses a composite image generation task formulated as follows: given a background image $I_{bg}$ and a foreground reference image $I_{ref}$, along with a specified axis-aligned bounding box $B$ in $I_{bg}$, the system must generate a composite image $I_{ins}$. In this output, the foreground object should appear spatially plausible relative to the background (pose, viewpoint, and illumination consistent with $I_{bg}$; termed high authenticity) while retaining the fine-grained appearance and texture of the reference (high fidelity).

Existing solutions fall into two primary classes. High-authenticity methods—exemplified by diffusion-based inpainting frameworks such as ObjectStitch and Paint by Example—can adapt the foreground object's pose and overall gestalt but typically incur blurring, color shifts, and loss of intricate reference details due to over-transformation. High-fidelity methods—including InsertAnything, AnyDoor, and ControlCom—use the reference foreground as explicit input for inpainting, thus preserving textures and colors, but lack robust mechanisms for pose adaptation, frequently resulting in visually incongruent “copy-and-paste” artifacts when the reference and background contexts diverge. This reveals an intrinsic trade-off: methods optimizing for authenticity tend to overwrite appearance details, whereas fidelity-optimized architectures insufficiently adapt spatial attributes (Wang et al., 23 Feb 2026).

2. Two-Stage Framework Design

OSInsert resolves the authenticity–fidelity trade-off by serializing the objectives into two distinct but interdependent stages, with each stage employing a pre-trained diffusion model for generation and adopting the Segment Anything Model (SAM) as a pixel-level mask extractor for precise conditioning.

2.1 Stage 1: High-Authenticity Shape Generation

Stage 1 employs the pre-trained ObjectStitch diffusion-inpainting network $\mathcal{F}_{OS}$. The inputs are:

• Masked background image $I_{mbg}$, constructed by erasing pixels within the bounding box $B$ of $I_{bg}$
• Binary bounding box mask $I_{ref}$0
• Foreground reference image $I_{ref}$1

The generative mapping is defined as: $I_{ref}$2 where $I_{ref}$3 is an initial composite with the foreground object spatially harmonized with the background context, but typically lacking in fine detail. The paper does not specify losses, custom architecture details, or training-specific hyperparameters for this stage.

2.2 Mask Extraction and Refinement

The Segment Anything Model $I_{ref}$4 operates on $I_{ref}$5, using the original box $I_{ref}$6 as prompt to derive a high-precision mask $I_{ref}$7 demarcating the generated foreground. This mask enables construction of a new masked background image for the next stage: $I_{ref}$8

2.3 Stage 2: High-Fidelity Detail Refinement

Stage 2 utilizes the pre-trained InsertAnything Diffusion Transformer $I_{ref}$9 together with the output from Stage 1, specifically:

• The Stage 1/SAM-masked background $B$0
• The refined foreground mask $B$1
• The original reference image $B$2

The mapping for detail synthesis is: $B$3 yielding a composite image in which the detailed texture and appearance from the reference are restored within a spatially adapted silhouette. Again, the framework is agnostic to fidelity- or adversarial-specific loss functions and provides no architectural or optimization hyperparameters for this stage.

2.4 Training and Modularity

Both stages operate the constituent models in inference mode, using them off-the-shelf without further pre-, joint-, or end-to-end training. No hyperparameter schedule, optimizer selection, or batch sizing is reported. The pipeline is modular, contingent on the availability and performance of the external models (ObjectStitch, InsertAnything, and SAM) (Wang et al., 23 Feb 2026).

3. Datasets, Input Preparation, and Implementation

The primary benchmark is the MureCOM dataset, comprising samples of the form $B$4 across diverse scene compositions. The dataset includes both indoor and outdoor environments, coverage of complex and rare foreground categories, and intentionally induced viewpoint and pose discrepancies between reference and background context. Each sample is equipped with standardized bounding box annotations to localize target insertion.

Preprocessing consists of generation of binary masks for erasure, explicit blacking out of foreground regions according to $B$5 or extracted masks, and providing bounding box prompts to SAM. Implementation details such as batch size, hardware, or network parameterization are not reported.

4. Experimental Results and Comparative Analysis

Qualitative comparisons enumerate results from five methods: ObjectStitch (high authenticity, poor detail), InsertAnything (high fidelity, poor pose), OSInsert, Banana pro (commercial), and Seedream 5.0 (commercial). In all displayed scenarios, OSInsert demonstrates simultaneous success in pose compatibility and texture fidelity, while commercial baselines are observed to produce bounding box misalignments and background color shifts.

No quantitative metrics (FID, LPIPS, user studies, or tabular results) are provided. The absence of such data precludes rigorous statistical comparison or ablation-based attribution of performance differentials to specific system components (Wang et al., 23 Feb 2026).

5. Ablation, Analysis, and Observed Limitations

There are no dedicated ablation studies in the reported work. Consequently, the potential impact of replacing SAM, omitting either stage, or varying the capacity of the backbone models remains unexamined. The authors do not discuss or quantify the influence of segmentation accuracy, nor do they address robustness to mask errors or generative failure modes.

Stated and inferred limitations include:

• Exclusive reliance on external, pre-trained models without any system-specific learning or adaptation.
• Absence of explicit mechanisms to enforce or refine consistency in lighting and shadow beyond spatial and textural realism.
• Sensitivity to segmentation errors from SAM, which can propagate through the pipeline without correction mechanisms.

6. Prospective Directions and Open Challenges

While explicit future work is not prescribed, natural avenues for extension include:

• Unified, end-to-end architectures that jointly optimize authenticity and fidelity through learned loss weighting or multi-stage adversarial supervision.
• Explicit definition and minimization of authenticity ($B$6) and fidelity ($B$7) losses within a single model.
• Adaptation of the framework for video composition with frame-wise temporal modeling and constraint enforcement.

A plausible implication is that broader impact and application of OSInsert-like systems would benefit from robust, adaptive mask extraction and from mechanisms enhancing color and illumination harmonization across complex, real-world scenes.

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By decoupling spatial adaptation and detail preservation, and bridging with high-precision segmentation, OSInsert advances the practical state of generative image composition, achieving outcomes qualitatively superior to the best-known single-stage and commercial alternatives under tested scenarios, while making no new architectural or supervisory contributions beyond the modular two-stage design (Wang et al., 23 Feb 2026).