Problem
Research questions and friction points this paper is trying to address.
layered video representation
video object insertion
video layer decomposition
explicit supervision
foreground-layer modeling
Explicit Layer Modeling for Video Object Insertion and Layer Decomposition
🤖 AI Summary
Existing video editing approaches struggle to simultaneously achieve photorealistic synthesis, object reusability, and cross-frame consistency due to the absence of explicit hierarchical representations, particularly in object insertion and layer decomposition tasks where they are constrained by implicit modeling or per-scene optimization. To address this, this work proposes DBL-Diffusion, a dual-branch diffusion framework, and introduces TriLayer, the first large-scale dataset comprising RGB-foreground-background triplets. By leveraging shared denoising and cross-branch interaction mechanisms, the method jointly learns RGB videos and explicit RGBA foreground layers, enabling end-to-end learning of explicit layered video representations for the first time. Experiments demonstrate that the proposed approach significantly enhances realism and editing flexibility in object insertion while substantially improving the quality of foreground and background recovery in layer decomposition.
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
explicit layer modeling
video object insertion
layer decomposition
dual-branch diffusion
triplet video dataset
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