CueInsert: Compiling Relational Intent into Verifiable Plans for Video Object Insertion

Video object insertion is inherently a planning-and-generation problem: a system must infer where and how an object should move from high-level intent, and then faithfully realize that plan in the generated video. Existing methods largely focus on visual synthesis or require manually specified low-level controls, leaving intent-driven insertion planning underexplored. We introduce CueInsert, a training-free framework for intent-driven video object insertion via neuro-symbolic planning and plan-conditioned rendering. Given a background video, a reference object, and an insertion intent, CueInsert analyzes scene structure and anchor motion to generate candidate plans over object scale, placement, orientation, and trajectory. Explicit temporal relation predicates then diagnose whether the proposed motion satisfies the intended interaction, while an iterative repair mechanism refines violated trajectories under proposal-preservation and smoothness constraints. The refined candidates are globally reranked and converted into coarse motion and mask conditions for training-free rendering without additional model training or inversion. We further construct DAVIS-Ins, an intent-conditioned benchmark derived from semantically and viewpoint-compatible DAVIS videos for systematic evaluation of planning and rendering, and introduce Insertion Action Coverage (IAC), which measures the fraction of temporal transitions in which the inserted object is present and exhibits residual motion beyond global translation. Experiments demonstrate improved intent consistency, trajectory control, object fidelity, background preservation, and temporal quality over existing video insertion baselines.