InsertAnywhere: Bridging 4D Scene Geometry and Diffusion Models for Realistic Video Object Insertion
Recent advances in diffusion-based video generation have opened new possibilities for controllable video editing, yet realistic video object insertion (VOI) remains challenging due to limited 4D scene understanding and inadequate handling of occlusion and lighting effects. We present InsertAnywhere, a new VOI framework that achieves geometrically consistent object placement and appearance-faithful video synthesis. Our method begins with a 4D aware mask generation module that reconstructs the scene geometry and propagates user specified object placement across frames while maintaining temporal coherence and occlusion consistency. Building upon this spatial foundation, we extend a diffusion based video generation model to jointly synthesize the inserted object and its surrounding local variations such as illumination and shading. To enable supervised training, we introduce ROSE++, an illumination aware synthetic dataset constructed by transforming the ROSE object removal dataset into triplets of object removed video, object present video, and a VLM generated reference image. Through extensive experiments, we demonstrate that our framework produces geometrically plausible and visually coherent object insertions across diverse real world scenarios, significantly outperforming existing research and commercial models.
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Hoiyeong Jin
claimed_verified
2025-12-25T20:44:16.181Z
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Hoiyeong Jin
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user
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694c0ede746a34b55dd53fa3
Hyojin Jang
claimed_verified
2025-12-25T20:44:20.849Z
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Jang hyojin
Whit3Snow
user
Whit3Snow
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Jeongho Kim
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Junha Hyung
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Kinam Kim
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Dongjin Kim
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Huijin Choi
694c0ede746a34b55dd53fa9
Hyeonji Kim
694c0ede746a34b55dd53faa
Jaegul Choo
InsertAnywhere: Bridging 4D Scene Geometry and Diffusion Models for Realistic Video Object Insertion
Recent advances in diffusion-based video generation have opened new possibilities for controllable video editing, yet realistic video object insertion (VOI) remains challenging due to limited 4D scene understanding and inadequate handling of occlusion and lighting effects. We present InsertAnywhere, a new VOI framework that achieves geometrically consistent object placement and appearance-faithful video synthesis. Our method begins with a 4D aware mask generation module that reconstructs the scene geometry and propagates user specified object placement across frames while maintaining temporal coherence and occlusion consistency. Building upon this spatial foundation, we extend a diffusion based video generation model to jointly synthesize the inserted object and its surrounding local variations such as illumination and shading. To enable supervised training, we introduce ROSE++, an illumination aware synthetic dataset constructed by transforming the ROSE object removal dataset into triplets of object removed video, object present video, and a VLM generated reference image. Through extensive experiments, we demonstrate that our framework produces geometrically plausible and visually coherent object insertions across diverse real world scenarios, significantly outperforming existing research and commercial models.
InsertAnywhere framework enhances video object insertion by generating geometrically consistent and visually coherent scenarios through 4D aware mask generation and diffusion-based synthesis.
diffusion-based video generation realistic video object insertion 4D scene understanding occlusion effects geometrically consistent object placement appearance-faithful video synthesis 4D aware mask generation diffusion based video generation model ROSE++ illumination aware synthetic dataset object removal dataset VLM generated reference image geometrically plausible visually coherent object insertions