GigaWorld-0: World Models as Data Engine to Empower Embodied AI
Paper • 2511.19861 • Published • 30
import torch
from diffusers import DiffusionPipeline
from diffusers.utils import load_image, export_to_video
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("open-gigaai/GigaWorld-0-Video-Pretrain-2b", dtype=torch.bfloat16, device_map="cuda")
pipe.to("cuda")
prompt = "A man with short gray hair plays a red electric guitar."
image = load_image(
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png"
)
output = pipe(image=image, prompt=prompt).frames[0]
export_to_video(output, "output.mp4")GigaWorld-0: World Models as Data Engine to Empower Embodied AI
✨ Introduction
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.
🗺️ Architecture
GigaWorld-0-Video-Dreamer is our foundation video generation model, capable of achieving IT2V generation.
📖 Citation
If you use GigaWorld-0 in your research, please cite:
@misc{gigaai2025gigaworld0,
title={GigaWorld-0: World Models as Data Engine to Empower Embodied AI},
author={GigaAI},
year={2025},
eprint={2511.19861},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2511.19861},
}
- Downloads last month
- -