Goku: A Million-Scale Universal Dataset and Benchmark for Instruction-Based Video Editing

Existing instruction-based video editing datasets commonlyfocus on single-task appearance editing, failing to meet the complex cre-ative demands of real-world scenarios. To bridge this gap, we presentGoku, a large-scale dataset featuring 2 million high-quality, instruction-aligned video editing pairs, which is the first to extend task boundariesfrom basic appearance editing to multi-task and structural manipulations(e.g., precise control of subject movement). To tackle the data synthesischallenges inherent in these complex tasks, we design an efficient datasynthesis pipeline that decomposes complex edits into controllable sub-problems and introduce a progressive filtering system for data reliabil-ity throughout the whole process. Furthermore, we explore the optimalnetwork structures on Goku, and propose Goku-Edit. To deeply com-prehend complex editing instructions, Goku-Edit leverages an MLLMas its text encoder and adopts a decoupled dual-branch design: a dedi-cated mask branch handles structural control, freeing the main branchfor appearance rendering. A comprehensive video editing benchmark,Goku-Bench, is also proposed with 1,000 human-verified test cases and7 novel editing-specific metrics. Evaluated on Goku-Bench, Goku-Editobtains up to +8% improvement on other open-source models in termsof instruction following.