Source-linked AI summary
Explicit Layer Modeling for Video Object Insertion and Layer Decomposition
Kyujin Han, Seungjoo Shin, Sunghyun Cho
TL;DR
Video editing still lacks explicit layered representations needed for realistic compositing and consistent manipulation. TriLayer supplies aligned triplets for supervised layer learning, while DBL-Diffusion improves insertion and decomposition across the two tasks.
Problem
Video object insertion and layer decomposition lack explicit, physically consistent foreground–background–composite supervision for learning layered video representations directly.
Method
TriLayer provides aligned composite, background, and foreground videos, and DBL-Diffusion jointly models RGB composites and RGBA foreground layers through dual-branch diffusion.
Results
DBL-Insert achieves balanced performance across insertion metrics, while DBL-Decompose achieves substantial improvements in video layer decomposition.
Takeaways & Limitations
Explicit layer supervision supports practical, flexible layer-based video editing and provides a resource for future layer-aware video research.
Takeaways & Limitations
The models struggle with complex physical interactions and highly dynamic motions, while the dual-branch architecture adds computational and memory overhead.
Abstract
from arXiv · showhide
Most video editing systems still lack explicit layered video representations, limiting their ability to perform realistic compositing, object reuse, and consistent manipulation. This limitation is especially pronounced in video object insertion and video layer decomposition, where existing methods rely on implicit inference or per-scene optimization due to the absence of explicit foreground-layer supervision. We introduce TriLayer, a large-scale triplet video dataset containing aligned composite, background, and foreground videos, where the foreground layers include both object appearance and associated visual effects. This explicit supervision enables models to learn layered video representations directly rather than inferring them implicitly. Building on this dataset, we propose DBL-Diffusion, a dual-branch diffusion framework that jointly models RGB composites and RGBA foreground layers through shared denoising and cross-branch interaction. We instantiate the framework in two tasks: DBL-Insert for layered object insertion, which generates explicit RGBA layers for realistic compositing and flexible post-editing, and DBL-Decompose for video layer decomposition, which recovers foreground and background layers using triplet supervision. Experiments demonstrate that explicit layer modeling substantially improves both insertion fidelity and decomposition quality.
1 Introduction
The introduction identifies explicit foreground–background–composite supervision as the missing foundation for realistic video insertion and generalizable layer decomposition. It presents TriLayer and DBL-Diffusion as a dataset-and-framework solution for learning and applying explicit layered video representations.
- Motivation: Most video editing approaches lack explicit foreground, background, and interaction representations, limiting realistic compositing and consistent editing.The limitation is especially important for video object insertion and video layer decomposition.
- Motivation: Video object insertion methods that implicitly generate foregrounds can distort nearby backgrounds and reduce post-editing flexibility.Without separable foreground and background layers, compositing quality and object reusability also degrade.
- Motivation: Video layer decomposition lacks explicit foreground supervision, causing reliance on per-scene optimization or implicit inference and limiting generalization.The task separates composite videos into foreground and background layers for object removal, duplication, and background replacement.
- TriLayer: TriLayer provides aligned composite, background, and foreground videos with foreground layers containing object appearance and associated visual effects.These correspondences enable supervised learning of layered video representations and support scalable construction of high-quality layered video datasets.
- DBL-Diffusion: DBL-Diffusion jointly models RGB composites and RGBA foreground layers through shared denoising and cross-branch interaction.The framework is instantiated as DBL-Insert for layered object insertion and DBL-Decompose for video layer decomposition.
2 Related Work
Prior video object insertion methods use training-free feature injection, inpainting, or motion guidance, while video layer decomposition separates composites into foreground and background layers for downstream editing applications. Omnimatte-based methods explicitly model foreground appearance and associated visual effects, with later extensions targeting improved consistency and 3D representation.
- Video object insertion: Video object insertion methods use training-free diffusion feature injection, masked video completion, or object trajectories to improve insertion, temporal coherence, and controllability.Training-free methods avoid additional training; inpainting treats insertion as masked video completion, while motion-guided approaches use object trajectories.
- Video layer decomposition: Video layer decomposition separates composite videos into foreground and background layers for object removal, duplication, and background replacement.The task supports multiple video editing applications involving foreground and background manipulation.
- Video layer decomposition: Omnimatte extracts foreground layers containing both object appearance and associated visual effects, while OmnimatteRF and Omnimatte3D extend the framework toward consistency and 3D modeling.OmnimatteRF incorporates a neural radiance field for improved consistency, and Omnimatte3D extends the approach further.
3 TriLayer Dataset
TriLayer provides aligned composite, clean background, and RGBA foreground videos that explicitly capture object appearance and associated visual effects. A multi-stage automated and human-verified pipeline constructs 3,964 high-quality triplets from roughly 18,000 candidates.
- Dataset design: TriLayer aligns composite, background, and foreground videos, with foreground layers and alpha mattes capturing opaque objects plus semi-transparent shadows and reflections.The composite is formed by alpha-compositing the foreground onto the background, and each sample includes object names and VLM-generated captions.
- Construction pipeline: The dataset construction pipeline combines automated processing with human verification to derive foreground–background–composite triplets from in-the-wild videos.Videos are collected from Pexels, resized and temporally trimmed, and multi-shot videos retain only the first clean shot.
- Layer extraction: Foreground masks guide object removal and inpainting to reconstruct clean background videos, while hybrid cues produce RGBA foreground layers containing object appearance and visual effects.Gen-Omnimatte supplies effect extraction, whereas MatAnyone and Grounded-SAM2 refine opacity and boundaries; alpha uses the per-pixel maximum across sources.
- Quality control: 3,964 high-quality triplets remain after filtering roughly 18,000 candidates and applying multiple human-verification rounds for mask accuracy, background stability, and foreground fidelity.A final post-captioning pass removes remaining failure cases, producing training data for layered video representations.
4 DBL-Diffusion for Explicit Layer Modeling
DBL-Diffusion explicitly models videos with coupled RGB and RGBA branches that exchange information through joint cross-attention. The unified backbone supports DBL-Insert for object insertion and DBL-Decompose for recovering background and foreground layers, with branch-specific adaptation and timestep sampling for training.
- DBL-Diffusion architecture: DBL-Diffusion uses dual RGB and RGBA diffusion branches for scene appearance and foreground-layer prediction, respectively, with bidirectional joint cross-attention between them.Cross-branch interaction promotes consistent layered synthesis.
- Task instantiations: The shared backbone is instantiated as DBL-Insert for layered object insertion and DBL-Decompose for layered video decomposition, with task-specific inputs, outputs, and training objectives.Both models retain the same architectural principles while serving different layered-video tasks.
- DBL-Insert: DBL-Insert generates a temporally coherent RGB composite and an explicit RGBA foreground layer, then alpha-blends the foreground with the target background for the final result.The RGBA output is the definitive inserted-object representation, while the RGB composite also provides an auxiliary denoising signal.
- DBL-Decompose: DBL-Decompose takes a composite video and recovers a clean background plus an RGBA foreground layer containing appearance, boundaries, transparency, shadows, and reflections.The RGB branch removes the object and effects, while the RGBA branch predicts the explicit foreground representation.
- Training strategy: Both tasks use LoRA for the in-domain RGB branch and DoRA for the out-of-domain RGBA branch, while disentangled timestep sampling stabilizes joint rectified-flow optimization.Independent timesteps t_x,t_y ∼ U(0, 1) let the branches evolve at separate noise levels while preserving cross-branch interaction.
5 Experiments
Experiments evaluate DBL-Diffusion on layered object insertion, video layer decomposition, and ablations of its supervision, parameter-efficient training, and conditioning strategies. Results show strong insertion and foreground decomposition, while layer-based and scene-aware editing support consistent object and effect manipulation.
- Layered object insertion: Layered object insertion is evaluated for text–video alignment, video quality, and subject consistency against AnyV2V, ReVideo, and VACE-Inp, with additional VACE-C and VACE-F variants.The metrics include ViCLIP-T, VBench, CLIP-I, and DINO-I; VACE-C and VACE-F isolate background–composite and background–foreground supervision.
- Layered object insertion: DBL-Insert achieves the most balanced performance across insertion metrics, whereas competing methods can alter backgrounds, introduce temporal inconsistencies, or fail to synthesize object-induced effects outside masked regions.VACE-C and VACE-F benefit from TriLayer supervision but still underperform in subject consistency and compositing quality.
- Video layer decomposition: DBL-Decompose achieves the best foreground reconstruction across PSNR, SSIM, and LPIPS, producing high-quality RGBA layers with cleaner separation than Gen-Omnimatte and OmnimatteZero.Gen-Omnimatte can produce semi-transparent or incomplete layers, while OmnimatteZero may yield inaccurate alpha masks for transparent objects or fine-scale boundaries.
- Ablation studies: The hybrid LoRA–DoRA configuration is more stable than LoRA-only or DoRA-only training, whose outputs often contain unstable foreground layers or inconsistent compositing.Cross-attention between the RGB and RGBA branches causes single-method degradations to appear across both outputs.
- Ablation studies: Foreground conditioning improves visual quality and temporal consistency for both DBL-Insert and DBL-Decompose by providing appearance cues for insertion and more accurate RGBA layer recovery.For DBL-Insert, the conditioning acts as an appearance anchor derived from the edited first frame.
- Editing capabilities: The layer-based representation enables independent background and foreground restyling, consistent propagation through video, and scene-aware generation of shadows, reflections, and light interactions.These edits can be recomposed without manual matting or complex post-processing.
6 Conclusion
The work introduces TriLayer and two dual-branch models, DBL-Insert and DBL-Decompose, for supervised layered video representations and practical layer-based editing. Despite strong performance, the approach remains limited by complex physical interactions, dynamic motions, and computational overhead.
- Contributions: TriLayer explicitly models foreground–background–composite relationships, enabling supervision for layered video representations.The dataset supports the proposed layered video modeling approach.
- Contributions: DBL-Insert and DBL-Decompose address video object layered insertion and video layer decomposition, respectively.Both models are built upon the TriLayer dataset.
- Contributions: The approach achieves strong performance across multiple metrics while supporting practical and flexible layer-based video editing.The conclusion summarizes performance and editing applicability without reporting specific numerical values.
- Limitations: The models struggle to capture complex physical interactions and highly dynamic motions between objects and scenes.These limitations motivate future improvements to physical interaction modeling.
- Limitations: The dual-branch architecture introduces additional computational and memory overhead.Future work will focus on improving efficiency and extending the framework to better model physical interactions.
Supplementary Material
The supplementary material is associated with the paper titled “Explicit Layer Modeling for Video Object Insertion and Layer Decomposition.”
- The supplementary material accompanies “Explicit Layer Modeling for Video Object Insertion and Layer Decomposition.”
A Details of Implementation
The implementation fine-tunes the RGB and RGBA branches with separate parameter-efficient methods and evaluates the system on a dedicated 85-video benchmark. It also specifies comparison protocols and notes unavailable baselines without official code.
- Joint cross attention: Joint cross-attention is initialized from pretrained self-attention, with LoRA applied to RGB query, key, and value projections and DoRA applied to RGBA projections.The RGB branch uses W_x projections, while the RGBA branch uses W_y projections.
- VACE-C and VACE-F training: VACE-C uses LoRA and VACE-F uses DoRA, while both models share the same datasets and training hyperparameters for fair comparison.The shared training data comprise TriLayer and internal datasets.
- Compared methods: ReVideo generates up to 14 frames per step, so 81-frame videos are produced autoregressively by feeding each clip’s last frame into the next step.For AnyV2V, the first frame is concatenated with the background video as input.
- Compared methods: Comparisons with OmniInsert, InsertAnywhere, LoVoRA, and VideoAnydoor are infeasible because their official code is not publicly available.The limitation applies specifically to comparisons with these four methods.
- Evaluation: LayeredVid-Benchmark contains 85 five-second videos with 81 frames each, covering diverse object categories, real-world scenarios, and motion patterns.The benchmark is used for quantitative evaluation of video object insertion and layer decomposition, with an emphasis on generalization.
B More Quantitative Results: Inserted Foreground Layer · C More Quantitative Results: Background Reconstruction Quality
DBL-Insert is evaluated on explicit RGBA foreground layers, where its dual-branch design improves modeling of object appearance and visual effects. For background reconstruction, the method prioritizes perceptual plausibility and achieves superior FID despite not always leading pixel-level metrics.
- B More Quantitative Results: Inserted Foreground Layer: The evaluation focuses specifically on the quality of RGBA foreground layers produced for inserted objects.Table 4 reports quantitative results on inserted-object foreground layers.
- B More Quantitative Results: Inserted Foreground Layer: Because AnyV2V, ReVideo, and VACE-Inp do not generate foreground layers explicitly, their RGBA outputs are extracted with an off-the-shelf Omnimatte-based model.This provides comparable layer outputs for quantitative evaluation.
- B More Quantitative Results: Inserted Foreground Layer: Existing methods lack explicit supervision for layer representations, limiting their ability to capture visual effects.The comparison highlights the importance of directly modeling foreground layers.
- B More Quantitative Results: Inserted Foreground Layer: DBL-Insert’s dual-branch design disentangles appearance modeling from layer modeling.This enables more effective learning of both object structure and visual effects.
- B More Quantitative Results: Inserted Foreground Layer: DBL-Insert produces higher-quality and more realistic foreground layers, achieving superior quantitative performance.The reported gains apply to the inserted foreground-layer evaluation.
- C More Quantitative Results: Background Reconstruction Quality: Reconstruction-based omnimatte methods favor pixel-wise background metrics such as PSNR and SSIM because they optimize recovery of the original background.The method instead focuses on generating perceptually plausible backgrounds, as evaluated in Table 5 and Fig. 8.
- C More Quantitative Results: Background Reconstruction Quality: The method may not always achieve the best pixel-level reconstruction accuracy, but it consistently attains superior FID and higher visual fidelity.This reflects its emphasis on perceptual plausibility rather than solely pixel-wise background reconstruction.
D More Quantitative Results: User Studies
User studies with 20 participants across 10 real-world videos evaluate video object insertion and layer decomposition. DBL-Insert and DBL-Decompose receive the strongest perceptual evaluations across their respective criteria.
- Study Setup: User studies evaluate insertion and decomposition with 20 participants on 10 manually collected real-world videos.The benchmark includes diverse object categories, visual effects, and scene configurations.
- Video Object Insertion: DBL-Insert is consistently preferred across subject consistency, insertion rationality, prompt alignment, and overall quality.Participants compare generated videos from different methods using these four evaluation criteria.
- Layer Decomposition: DBL-Decompose receives the highest preference for both reconstructed background and foreground layers by a large margin.The result indicates that explicit layer modeling produces more visually plausible decomposed layers than existing methods.
E Details of TriLayer Dataset · F More Qualitative Results
The supplementary sections detail TriLayer’s VLM-guided filtering and multi-method foreground refinement, report a dataset of 3,964 samples across 229 object categories, and provide additional qualitative visualizations of layer editing results.
- E Details of TriLayer Dataset: E Details of TriLayer Dataset: VLM filtering evaluates video and object quality before selecting major objects through object-centric prompting.The quality assessment considers visibility, blurriness, occlusion, and object prominence, classifying samples as ACCEPTABLE or REJECTED.
- E Details of TriLayer Dataset: E Details of TriLayer Dataset: SAM2 and Grounded-DINO generate object masks for ROSE-based removal, but incomplete masks can leave objects or visual effects behind.These failures motivate filtering and correction of background-generation results.
- E Details of TriLayer Dataset: E Details of TriLayer Dataset: Gen-Omnimatte, MatAnyone, and SAM2 are combined to improve foreground opacity and recover fine-grained structures such as hair or fur.Gen-Omnimatte captures visual effects, while SAM2 and MatAnyone address semi-transparent object regions.
- E Details of TriLayer Dataset: E Details of TriLayer Dataset: Foreground outputs receive GOOD, gen-GOOD, mat-GOOD, or BAD labels, with BAD samples discarded.gen-GOOD denotes acceptable Gen-Omnimatte-only results; mat-GOOD denotes better SAM2-and-MatAnyone results; GOOD denotes successful combination of all methods.
- E Details of TriLayer Dataset: E Details of TriLayer Dataset: The refined alpha matte for mat-GOOD is defined as M_mat_GOOD = max(M_sam, M_mat).The masks M_sam and M_mat come from SAM2 and MatAnyone, respectively.
- E Details of TriLayer Dataset: E Details of TriLayer Dataset: TriLayer contains 3,964 samples spanning 229 object categories, with foreground-layer labels assigned through human verification.The reported statistics include object-category and foreground-layer-label distributions.
- F More Qualitative Results: F More Qualitative Results: Supplementary visualizations crop selected regions, emphasize key effects, and compose layer-editing results in Adobe Premiere Pro.The full video editing results are provided in the supplementary video because the main paper shows only a single frame per qualitative example.
- F More Qualitative Results: F More Qualitative Results: Additional figures show VLM prompts, SAM-based masking and ROSE-removal failures, foreground-quality annotations, dataset statistics, and further TriLayer examples.The figures collectively document both the dataset construction process and qualitative outcomes.