Scalable, cost-efficient high-fidelity video editing data pipeline

Develop a scalable and cost-efficient synthetic data generation pipeline for instruction-based video editing that produces high-fidelity edited videos at scale, addressing the current trade-offs between editing diversity, temporal consistency, visual quality, and scalability.

Background

The paper reviews prior synthetic data generation strategies for instruction-based video editing and notes that existing pipelines face a persistent trade-off: they either scale efficiently but suffer in editing diversity, temporal coherence, and visual quality, or they achieve higher fidelity at prohibitive computational cost.

Within this context, the authors explicitly state that creating a scalable, cost-efficient pipeline capable of generating high-fidelity results remains an open challenge. Their work proposes Ditto as a step toward addressing this challenge, but the sentence explicitly frames the core issue as open at the time of discussion.