World models are emerging as a foundational paradigm for scalable, data-efficient embodied AI. In
this work, we present GigaWorld-0, a unified world model framework designed explicitly as a data
engine for Vision-Language-Action (VLA) learning. GigaWorld-0 integrates two synergistic components:
GigaWorld-0-Video, which leverages large-scale video generation to produce diverse, texture-rich, and
temporally coherent embodied sequences under fine-grained control of appearance, camera viewpoint,
and action semantics; and GigaWorld-0-3D, which combines 3D generative modeling, 3D Gaussian
Splatting reconstruction, physically differentiable system identification, and executable motion planning
to ensure geometric consistency and physical realism.
GigaWorld-0-Video-Dreamer is our foundation video generation model, capable of achieving IT2V generation.
1@misc{gigaai2025gigaworld0,
2 title={GigaWorld-0: World Models as Data Engine to Empower Embodied AI},
3 author={GigaAI},
4 year={2025},
5 eprint={2511.19861},
6 archivePrefix={arXiv},
7 primaryClass={cs.CV},
8 url={https://arxiv.org/abs/2511.19861},
9}
