Point2Insert: Video Object Insertion via Sparse Point Guidance

2026-02-04

AI Summary

Existing video object insertion methods either rely on dense mask annotations or struggle to achieve precise spatial localization, resulting in cumbersome workflows and limited performance. This work proposes the first sparse point-guided framework for video object insertion, enabling fine-grained spatial control through only a few user-provided positive and negative points. The approach employs a two-stage training strategy: first, an image-level insertion model conditioned on either points or masks is trained; then, the model is adapted to video using synthetic video pairs, enhanced by mask-guided knowledge distillation to improve point-based guidance. Experiments demonstrate that the proposed method significantly outperforms strong baselines, achieving substantial gains in both insertion success rate and localization accuracy—surpassing even models with over ten times more parameters.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Multimodal Learning
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

This paper introduces Point2Insert, a sparse-point-based framework for flexible and user-friendly object insertion in videos, motivated by the growing popularity of accurate, low-effort object placement. Existing approaches face two major challenges: mask-based insertion methods require labor-intensive mask annotations, while instruction-based methods struggle to place objects at precise locations. Point2Insert addresses these issues by requiring only a small number of sparse points instead of dense masks, eliminating the need for tedious mask drawing. Specifically, it supports both positive and negative points to indicate regions that are suitable or unsuitable for insertion, enabling fine-grained spatial control over object locations. The training of Point2Insert consists of two stages. In Stage 1, we train an insertion model that generates objects in given regions conditioned on either sparse-point prompts or a binary mask. In Stage 2, we further train the model on paired videos synthesized by an object removal model, adapting it to video insertion. Moreover, motivated by the higher insertion success rate of mask-guided editing, we leverage a mask-guided insertion model as a teacher to distill reliable insertion behavior into the point-guided model. Extensive experiments demonstrate that Point2Insert consistently outperforms strong baselines and even surpasses models with $\times$10 more parameters.

Problem

• video object insertion
• sparse point guidance
• mask annotation
• precise object placement
• user-friendly editing

Innovation

• sparse point guidance
• video object insertion
• mask distillation
• two-stage training
• interactive editing