Most public video-editing datasets focus on simple appearance swaps or single-task edits, which limits progress on instruction-driven, multi-step creative edits. This dataset addresses that gap by offering roughly two million instruction-aligned video editing pairs that explicitly include multi-task and structural manipulations, produced with automated synthesis and multi-stage quality filtering to improve alignment and temporal coherence.
What Sets It Apart
- Large, instruction-aligned scale: ~2M edit pairs designed to support training large video-editing and text-to-video models and enable robust generalization across diverse edits.
- Beyond appearance edits: includes basic appearance changes plus multi-task edits and structural transformations such as camera and subject movement, enabling researchers to tackle spatial and temporal control.
- Scalable synthesis + progressive filtering: complex edits are decomposed into controllable subproblems, then filtered across instruction alignment, frame-to-frame stability, and perceptual realism to reduce noise from automated pipelines.
- Benchmark & model ecosystem: paired with a human-verified Goku-Bench (1,000 test cases, 7 specialized metrics) and used to develop Goku-Edit (MLLM text encoder + dual-branch mask design), showing measurable gains in instruction following.
Who It's For and Trade-offs
Great fit if you need large-scale, instruction-aligned training data for research or prototyping of text-/instruction-to-video editing and video-to-video editing models, especially when structural edits and multi-task workflows are important. It is also useful for building or evaluating benchmarks for instruction-following in video editing.
Look elsewhere if you require purely real-capture, fully human-annotated edits without any synthetic data or if you need permissive commercial licensing—the dataset is released under CC BY-NC-4.0, which restricts commercial use. Automated synthesis and cascade processing can still introduce subtle artifacts despite progressive filtering, so downstream validation is recommended for high-stakes production use.
