We introduce a new task -- language-driven video inpainting, which uses natural language instructions to guide the inpainting process. This approach overcomes the limitations of traditional video inpainting methods that depend on manually labeled binary masks, a process often tedious and labor-intensive. We present the Remove Objects from Videos by Instructions (ROVI) dataset, containing 5,650 videos and 9,091 inpainting results, to support training and evaluation for this task. We also propose a novel diffusion-based language-driven video inpainting framework, the first end-to-end baseline for this task, integrating Multimodal Large Language Models to understand and execute complex language-based inpainting requests effectively. Our comprehensive results showcase the dataset's versatility and the model's effectiveness in various language-instructed inpainting scenarios. We will make datasets, code, and models publicly available.
@article{arxiv.2401.10226,
title = {Towards Language-Driven Video Inpainting via Multimodal Large Language Models},
author = {Jianzong Wu and Xiangtai Li and Chenyang Si and Shangchen Zhou and Jingkang Yang and Jiangning Zhang and Yining Li and Kai Chen and Yunhai Tong and Ziwei Liu and Chen Change Loy},
journal= {arXiv preprint arXiv:2401.10226},
year = {2024}
}