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About this paper
Haoyu Wang, Songchun Zhang, Haoran Li, Haoyang Huang, Zeyue Xue, Nan Duan
An Unreal Engine-based pipeline was developed for generating large-scale, action-conditioned, multi-view synthetic video data with precise temporal alignment between visuals and control signals, designed for pretraining interactive world models. This system produced over 8,700 hours of 1080p and 720p video across 429 diverse environments and 40 characters, achieving an effective throughput of approximately 33 minutes per node-hour.
Key insights
- Separating physics simulation from high-quality rendering is crucial for generating physically plausible and visually superior synthetic data from commercial game engines.
- Cache-aware task scheduling significantly improves rendering efficiency in large-scale Unreal Engine data production by leveraging persistent local caches.
- Rigorous automated curation and robust operational strategies are essential for sustaining continuous, reliable data output when managing diverse third-party assets in a distributed synthetic data pipeline.
Summary by alphaXiv.
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