Controllable Video Object Insertion via Multiview Priors

2026-04-15

AI Summary

This work addresses the challenges of dynamically inserting novel objects into existing videos—such as appearance inconsistency, spatial misalignment, and temporal flickering—by proposing a controllable video object insertion method guided by multi-view priors. The approach first lifts a 2D reference image into a multi-view 3D representation and then employs a dual-path view-consistent conditional generation mechanism coupled with a quality-aware weighting strategy to effectively handle occlusions and boundary artifacts. Furthermore, an integrated perceptual consistency module is introduced to ensure cross-frame photorealism and temporal stability. The proposed method significantly enhances the realism of inserted objects, improves spatial alignment accuracy, and achieves strong temporal coherence, resulting in high-quality, view-consistent dynamic compositing.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multi-instance/Multi-view Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• Video Object Insertion
• Appearance Consistency
• Temporal Coherence
• Occlusion Handling
• Spatial Alignment

Innovation

• multiview priors
• video object insertion
• view-consistent conditioning
• occlusion handling
• temporal coherence