VIVID-10M: A Dataset and Baseline for Versatile and Interactive Video Local Editing

TL;DR

This paper introduces VIVID-10M, a large-scale dataset, and VIVID, a versatile, interactive video editing model that enables efficient, high-quality local editing with improved user interactivity and state-of-the-art performance.

Contribution

The paper presents the first large-scale hybrid video editing dataset and a new interactive editing model supporting entity addition, modification, and deletion.

Findings

VIVID-10M contains 9.7 million samples covering diverse editing tasks.

VIVID model achieves state-of-the-art results in video local editing.

Interactive keyframe-guided editing reduces latency and improves user control.

Abstract

Diffusion-based image editing models have made remarkable progress in recent years. However, achieving high-quality video editing remains a significant challenge. One major hurdle is the absence of open-source, large-scale video editing datasets based on real-world data, as constructing such datasets is both time-consuming and costly. Moreover, video data requires a significantly larger number of tokens for representation, which substantially increases the training costs for video editing models. Lastly, current video editing models offer limited interactivity, often making it difficult for users to express their editing requirements effectively in a single attempt. To address these challenges, this paper introduces a dataset VIVID-10M and a baseline model VIVID. VIVID-10M is the first large-scale hybrid image-video local editing dataset aimed at reducing data construction and model training costs, which comprises 9.7M samples that encompass a wide range of video editing tasks. VIVID is a Versatile and Interactive VIdeo local eDiting model trained on VIVID-10M, which supports entity addition, modification, and deletion. At its core, a keyframe-guided interactive video editing mechanism is proposed, enabling users to iteratively edit keyframes and propagate it to other frames, thereby reducing latency in achieving desired outcomes. Extensive experimental evaluations show that our approach achieves state-of-the-art performance in video local editing, surpassing baseline methods in both automated metrics and user studies. The VIVID-10M dataset are open-sourced at https://kwaivgi.github.io/VIVID/.