arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2510.15742cs.CV

通过高质量合成数据集扩展基于指令的视频编辑

Scaling Instruction-Based Video Editing with a High-Quality Synthetic Dataset

HKUST(香港科技大学);Ant Group(蚂蚁集团);Zhejiang University(浙江大学);Northeastern University(东北大学)

Qingyan Bai, Qiuyu Wang, Hao Ouyang, Yue Yu, Hanlin Wang, Wen Wang, Ka Leong Cheng, Shuailei Ma, Yanhong Zeng, Zichen Liu, Yinghao Xu, Yujun Shen, Qifeng Chen

AI总结:

Ditto通过高效数据生成管道和智能代理,构建大规模高质量数据集,提升基于指令的视频编辑性能。

摘要:

基于指令的视频编辑有望普及内容创作,但其进展严重受制于大规模、高质量训练数据的匮乏。我们介绍了Ditto,一个旨在解决这一根本挑战的综合框架。其核心是一个新的数据生成管道,融合了领先图像编辑器的创意多样性与上下文视频生成器,克服了现有模型的局限性。为使这一过程可行,我们的框架通过采用高效、压缩的模型架构并辅以时间增强器,解决了成本与质量之间的权衡问题,同时减少计算开销并提高时间一致性。最后,为实现全面扩展,整个流程由智能代理驱动,该代理能生成多样化的指令并严格筛选输出,确保大规模的质量控制。使用此框架,我们投入超过12000个GPU天,构建了Ditto-1M,一个包含一百万高质量视频编辑示例的新数据集。我们使用Ditto-1M和课程学习策略训练了我们的模型Editto。结果表明,该模型在遵循指令方面表现出色,并在基于指令的视频编辑领域建立了新的最先进的水平。

英文摘要:

Instruction-based video editing promises to democratize content creation, yet its progress is severely hampered by the scarcity of large-scale, high-quality training data. We introduce Ditto, a holistic framework designed to tackle this fundamental challenge. At its heart, Ditto features a novel data generation pipeline that fuses the creative diversity of a leading image editor with an in-context video generator, overcoming the limited scope of existing models. To make this process viable, our framework resolves the prohibitive cost-quality trade-off by employing an efficient, distilled model architecture augmented by a temporal enhancer, which simultaneously reduces computational overhead and improves temporal coherence. Finally, to achieve full scalability, this entire pipeline is driven by an intelligent agent that crafts diverse instructions and rigorously filters the output, ensuring quality control at scale. Using this framework, we invested over 12,000 GPU-days to build Ditto-1M, a new dataset of one million high-fidelity video editing examples. We trained our model, Editto, on Ditto-1M with a curriculum learning strategy. The results demonstrate superior instruction-following ability and establish a new state-of-the-art in instruction-based video editing.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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