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This paper introduces a novel framework for video object insertion that leverages multi-view object priors to ensure consistent appearance, spatial alignment, and temporal coherence. The approach lifts 2D reference images into multi-view representations and uses a dual-path view-consistent conditioning mechanism for stable identity guidance. An integration-aware consistency module further enhances spatial realism by resolving occlusion and boundary artifacts, leading to improved video object insertion quality.
Achieve realistic video object insertion by lifting 2D images into multi-view representations, ensuring consistent appearance and handling occlusions in dynamic scenes.
Video object insertion is a critical task for dynamically inserting new objects into existing environments. Previous video generation methods focus primarily on synthesizing entire scenes while struggling with ensuring consistent object appearance, spatial alignment, and temporal coherence when inserting objects into existing videos. In this paper, we propose a novel solution for Video Object Insertion, which integrates multi-view object priors to address the common challenges of appearance inconsistency and occlusion handling in dynamic environments. By lifting 2D reference images into multi-view representations and leveraging a dual-path view-consistent conditioning mechanism, our framework ensures stable identity guidance and robust integration across diverse viewpoints. A quality-aware weighting mechanism is also employed to adaptively handle noisy or imperfect inputs. Additionally, we introduce an Integration-Aware Consistency Module that guarantees spatial realism, effectively resolving occlusion and boundary artifacts while maintaining temporal continuity across frames. Experimental results show that our solution significantly improves the quality of video object insertion, providing stable and realistic integration.
