InstructVEdit: A Holistic Approach for Instructional Video Editing

2025-03-22

AI Summary

This paper addresses the training and generalization bottlenecks in instruction-driven video editing caused by the scarcity of high-quality paired data. Methodologically, it introduces the first end-to-end, full-cycle editing framework comprising: (1) a controllable and scalable pipeline for generating instruction-video editing pairs; (2) a dual-module architecture jointly optimizing local edit fidelity and temporal consistency; and (3) an iterative refinement strategy grounded in real-video feedback, integrating instruction-conditioned control, temporal modeling, data distillation, and self-optimizing training. Evaluated on multiple benchmarks, the framework achieves state-of-the-art performance, demonstrating significantly improved robustness to complex instructions and dynamic scenes, as well as enhanced generalization under low-data regimes. It establishes a novel paradigm for data-efficient video editing.

Technology Category

• Computer Vision: Video Understanding & Activity Analysis
• Search and Optimization: Learning to Search
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Video editing according to instructions is a highly challenging task due to the difficulty in collecting large-scale, high-quality edited video pair data. This scarcity not only limits the availability of training data but also hinders the systematic exploration of model architectures and training strategies. While prior work has improved specific aspects of video editing (e.g., synthesizing a video dataset using image editing techniques or decomposed video editing training), a holistic framework addressing the above challenges remains underexplored. In this study, we introduce InstructVEdit, a full-cycle instructional video editing approach that: (1) establishes a reliable dataset curation workflow to initialize training, (2) incorporates two model architectural improvements to enhance edit quality while preserving temporal consistency, and (3) proposes an iterative refinement strategy leveraging real-world data to enhance generalization and minimize train-test discrepancies. Extensive experiments show that InstructVEdit achieves state-of-the-art performance in instruction-based video editing, demonstrating robust adaptability to diverse real-world scenarios. Project page: https://o937-blip.github.io/InstructVEdit.

Problem

• Addresses scarcity of high-quality instructional video datasets
• Improves video editing quality and temporal consistency
• Enhances generalization with iterative real-world data refinement

Innovation

• Reliable dataset curation workflow initialization
• Model architectural improvements for consistency
• Iterative refinement with real-world data