Building Pretraining Data for World Models: An Unreal Engine-Based Pipeline for Action-Conditioned Video Generation
Classification
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Category review. The contribution is an Unreal Engine pipeline that executes and records controls, then renders action-conditioned multiview pretraining videos; it supplies data infrastructure without a learned WAM controller. The cataloged contribution is a dataset/data-generation method or evaluation/simulation resource, not the architecture of an evaluated or external policy. Reading evidence
Contribution
This paper documents the Unreal Engine synthetic-data component used in EchoWM: simulate character motion once, record controls and states, then replay the trajectory for high-quality multi-view rendering. Its contribution is a production system combining scene curation, cache locality and failure recovery. Reported video volume establishes production scale; downstream learning utility remains untested here. [e-scope, e-workflow, e-scale, e-limits]
Abstract
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Affiliations
Joy Future Academy, JD; Tsinghua University; The Hong Kong University of Science and Technology