Insert Anything: Seamless Scene Integration

Updated 12 November 2025

Insert Anything is a paradigm that integrates arbitrary objects into scenes using vision, language, and robotics for realistic, context-aware synthesis.
It leverages advanced techniques like diffusion models, transformer-based in-context editing, and delta-pose regression to ensure precise geometric and appearance harmonization.
The approach drives innovations in digital content creation, video editing, and robotic assembly by improving temporal consistency and robust scene manipulation.

The concept of “Insert Anything” involves learning-based systems and algorithmic frameworks capable of seamlessly and realistically inserting arbitrary objects, entities, or concepts into target scenes, images, or videos. This task is foundational in vision-language understanding, synthetic content generation, scene manipulation, and robotic manipulation, and spans a spectrum from reference-guided image editing to precision-controlled robotic assembly. Across modalities, it entails geometric, linguistic, and appearance harmonization, while supporting robustness and generalization to novel categories, poses, and environments.

1. Formulations and Problem Settings

The “Insert Anything” paradigm encompasses a family of problem settings:

Visual Insertion (Image/Video): Given a reference object (image, mask, or concept embedding) and a target context (scene image, video clip, or semantic map), synthesize a new composite in which the reference is plausibly, harmoniously, and controllably inserted. Signals for “where” and “what” to insert include masks, bounding boxes, textual prompts, geometric constraints, and user-guided trajectories.
Object Recommendation/Scene Retrieval: “What and Where” systems formalize dual tasks: (i) Given a scene, recommend suitable objects and locations for insertion; (ii) Given a category, retrieve scenes where it is likely to appear and propose plausible placements (Zhang et al., 2018).
Robotic Insertion: Given a robot-EEF and sensorial feedback, predict the precise pose delta or corrective action necessary to insert a manipulated part into a socket or assembly in arbitrary, cluttered settings, generalizing to new geometries with minimal data (Li et al., 22 May 2025, Spector et al., 2021, Spector et al., 2022).
Image-to-Video Insertion: Insert an object from a static image into a dynamic video such that it follows a user- or data-driven trajectory and harmonizes spatiotemporally with the background (Zhao et al., 13 Mar 2025, Tu et al., 2 Jan 2025, Bai et al., 30 Jan 2024).
Semantic Insertion: Insert instance masks or specify semantic label modifications into label maps, with an eye toward downstream RGB scene synthesis or semantic editing (Lee et al., 2018).

Unifying themes are flexibility (scalable to any concept or geometry), robustness to limited or weak supervision, and harmonization (appearance, physics, semantics).

2. Core Methodological Approaches

Generative Modeling

Diffusion Models and Transformers: Recent diffusion architectures (Stable Diffusion, DiT) and diffusion-transformer hybrids underlie state-of-the-art insertion methods for both images and videos. These systems support conditioning on multimodal context—reference images, masks, text, motion trajectories—through cross-attention and in-context concatenation (Song et al., 21 Apr 2025, Tu et al., 2 Jan 2025, Zhao et al., 13 Mar 2025, Zhang et al., 25 Sep 2025).

Flow Matching and Loss Functions: Training losses combine continuous-time (flow-matching) or standard diffusion denoising objectives with explicit identity- and pixel-reconstruction terms (e.g., CLIP or DINO similarity, LPIPS) to preserve visual fidelity and harmonization (Song et al., 21 Apr 2025).

In-Context and Multimodal Conditioning: "In-Context Editing" strategies concatenate scenes and references as panel “polyptychs” for transformer-based attention, supporting mask-guided (exact placement) or text-guided (semantic) insertion (Song et al., 21 Apr 2025).

Two-Stage Pipelines for Video: Anchor-frame inpainting combined with anchor-based attention propagation ensures temporal consistency in video insertion (Saini et al., 15 Jul 2024). Extended self-attention across frames allows sharing of structural priors between the anchor and subsequent frames, effectively reducing flicker and object drift.

Data-Synthesis for Insertion: “EraseDraw” inverts object removal pipelines to synthesize massive datasets: objects are erased by inpainting and paired with the original scene to ground learning of insertion models under spatial, physical, and lighting consistency (Canberk et al., 31 Aug 2024).

Robotic Manipulation and Regression

Delta-Pose Prediction: Robotic “Insert Anything” frames insertion as a delta-pose regression task in SE(3), leveraging visual cues (dual wrist RGB), minimal human intervention, and robust data augmentation (color jitter, dropout) to achieve high zero-shot success with limited data (Li et al., 22 May 2025).

Coarse-to-Fine Strategies: Multi-phase controllers execute sequentially: (I) coarse alignment, (II) fine adjustment, (III) close-contact exploration with micro-randomization for friction mitigation, informed by force-torque feedback and learned delta-pose predictors (Li et al., 22 May 2025).

Residual Policies and Multimodal Fusion: Regression-based policies, trained over visual features (convolutional towers) and force/moment vectors (MLPs), output direct motion corrections for rapid, contact-minimized insertion (Spector et al., 2021, Spector et al., 2022). Contrastive learning objectives improve embedding robustness and cross-task generalization.

Contextual and Geometric Reasoning

Probabilistic Context Modeling: Joint probability models over object category, context, and placement (e.g., GMMs over relative position/scale conditioned on scene objects and relationships) provide interpretable, data-driven recommendations for “what” and “where” to insert (Zhang et al., 2018).

3D-Aware Insertion: Lifting objects into 3D (using differentiable rendering and diffusion-based SDS) enables geometry-aware insertion and enables both rigid and nonrigid pose control (Zhang et al., 25 Sep 2025). Meshes are edited, rendered, and used as guides for 2D diffusion-based harmonization and blending.

Semantic Map Manipulation: End-to-end context-aware instance insertion decomposes the problem into “where” (location/scale GAN with spatial transformer) and “what” (shape/pose GAN) modules, facilitating plausible, diverse semantic edits (Lee et al., 2018).

3. Datasets and Benchmarking

Large-Scale Data Generation: The AnyInsertion dataset (>120K pairs, high-res, diverse insertion tasks) provides a unified, high-fidelity resource for training reference-based insertion models (Song et al., 21 Apr 2025). GetIn-1M (1M pairs) supports instance-level video insertion evaluation (Zhuang et al., 8 Mar 2025).

Data Annotation Pipelines: Automated labeling systems deploy multimodal models for captioning (NVILA), instance detection (Ground-DINO), mask segmentation (SAM2), and inpainting (ProPainter) to scale up data for learning and benchmarking new paradigms such as Get-In-Video editing (Zhuang et al., 8 Mar 2025).

Metrics: Canonical metrics include:

Fidelity: PSNR, SSIM, and perceptual measures (LPIPS, DINO-Score, CLIP-Score) on inserted regions and background.
Motion/Temporal Consistency: Frame-to-frame CLIP similarity, FVD, LPIPS on inpainted regions.
Task Success: In robotics, zero/one-shot insertion rates (e.g., >90% zero-shot across 15 novel connectors (Li et al., 22 May 2025)), average insertion time, multi-step generalization.
User Studies: Forced-choice and rating protocols for realism, identity preservation, and spatiotemporal coherence (e.g., InVi preferred in 75% of video edit cases (Saini et al., 15 Jul 2024)).

4. Applications and Real-World Impact

Creative Content Generation: “Insert Anything” systems power text- and reference-based visual edits for digital art, personalized advertising, virtual try-on, and cinema post-production, supporting mask- and text-guided tasks in a single model (Song et al., 21 Apr 2025, Zhang et al., 25 Sep 2025).

Virtual and Augmented Reality: Photorealistic, context-aware insertion enables interactive content creation for virtual reality environments, training simulators, and AR overlays (e.g., “Anything in Any Scene” achieves human-preferred realism and boosts rare-object detection mAP by 3.7% in downstream tasks (Bai et al., 30 Jan 2024)).

Video Editing and Post-Production: High-fidelity insertion tools allow seamless object replacement, crowd synthetization, and artifact removal/addition in dynamic video, respecting both physical and semantic contextuality (Bai et al., 30 Jan 2024, Liu et al., 22 Feb 2024).

Robotic Assembly and Manipulation: Data-efficient, generalizable insertion policies enable flexible robotic manipulation of unseen assemblies, with minimal human demonstration and strong robustness to clutter and initial misalignment (Li et al., 22 May 2025, Spector et al., 2022).

Scene Understanding and Cognitive Vision: Context-based recommendation and semantic synthesis fuel automated scene editing, data augmentation, and assistive design tools for ML workflows (Zhang et al., 2018, Lee et al., 2018).

5. Challenges, Ablations, and Future Directions

Generalization to Rare/Novel Classes: Data-driven and context-modeling approaches (e.g., relative-pose and context-conditioned GMMs) offer smooth, workspace-invariant mappings, but rare categories and relationships remain data-limited (Zhang et al., 2018, Li et al., 22 May 2025).
Temporal and Spatiotemporal Consistency: Robustness to drift, flicker, and object blending artifacts is addressed by anchor-based or attention-propagation modules in video models; further advances in handling occlusion and dynamic backgrounds are ongoing (Saini et al., 15 Jul 2024, Zhao et al., 13 Mar 2025).
Physical and Lighting Realism: Full-scene illumination capture (camera pose, HDR, shadow and environment estimation) is required for photorealistic harmonization; neural relighting and shadow modules remain crucial future targets (Bai et al., 30 Jan 2024, Zhang et al., 25 Sep 2025).
Scaling and Efficiency: Efficient memory and computational strategies are needed for hierarchical or transformer-based models at high resolution and over long sequences (Zhuang et al., 8 Mar 2025, Tu et al., 2 Jan 2025).
Interactivity and User Control: Future insertion methods are trending toward richer, interactive control modalities—direct keypoint specification, multi-object insertion, and editable motion/appearance trajectories—leveraging scene flow, affordance learning, and multi-stage diffusion adapters (Tu et al., 2 Jan 2025, Zhao et al., 13 Mar 2025, Zhang et al., 25 Sep 2025).

6. Limitations and Open Problems

Common open problems include:

Quality drop in high-frequency detail under strong deformations or occlusions
Poor performance on multi-object, inter-object, or physically interactive insertions beyond simple spatial overlays
Failure in highly dynamic or crowded real-world scenes due to insufficient geometric reasoning or occlusion handling (Bai et al., 30 Jan 2024, Zhuang et al., 8 Mar 2025)
Resolution and inference speed bottlenecks, particularly in video pipelines (Tu et al., 2 Jan 2025)
Challenges in modeling complex physical effects (shadows, reflections, lighting transport), motivating hybrid graphical/learning models (Zhang et al., 25 Sep 2025)

Table: Illustrative Methodological Spectrum

Modality	Core Approach	Representative Work
Image Insertion	DiT, in-context, mask/text	(Song et al., 21 Apr 2025)
Video Insertion	Anchor attention, 3D-VAE, ControlNet	(Saini et al., 15 Jul 2024, Tu et al., 2 Jan 2025)
Robotic Insertion	Delta-pose diff. regression	(Li et al., 22 May 2025)
Context Modeling	Context-aware GMM	(Zhang et al., 2018)
Semantic Insert	Where/What GAN + STN	(Lee et al., 2018)

7. Comparative Results and Benchmarks

Recent works set new performance bars across domains:

Insert Anything (DiT): PSNR 26.40, SSIM 0.8791, LPIPS 0.0820, FID 28.31 on AnyInsertion (Song et al., 21 Apr 2025).
VideoAnydoor: PSNR 37.7, CLIP-Score 81.2, DINO-Score 58.9, Joint-Accuracy 88.0 (Tu et al., 2 Jan 2025).
Robotic EasyInsert: >90% zero-shot insertion on 13/15 unseen object types after 5 h of training (Li et al., 22 May 2025).
GetInVideo (GetIn-1M): FID 15.12, FVD 435.22, CLIP-I 0.8685, DINO-I 0.9267 (outperforming all baselines) (Zhuang et al., 8 Mar 2025)
Ablations: Omission of color augmentation in vision-based insertion reduced rare-color object success by approximately 67% (Li et al., 22 May 2025); removal of in-context editing in DiT increased FID from 35.72 to 59.97 (Song et al., 21 Apr 2025).

The field continues to evolve toward generality, control, and realism, bridging manipulation, content creation, and analytic vision domains with unified insertion methodologies.