Summary GigaWorld-0 is a unified world model framework serving as a data engine for Vision-Language-Action (VLA) learning in embodied AI. It addresses the need for scalable, data-efficient embodied AI by generating diverse, realistic, and controllable embodied sequences, empowering researchers with a robust data generation pipeline.

How It Works The framework integrates two synergistic components: GigaWorld-0-Video and GigaWorld-0-3D. GigaWorld-0-Video uses an IT2V foundation model (GigaWorld-0-Video-Dreamer) for diverse, texture-rich embodied sequences with fine-grained control over appearance, camera, and action semantics. GigaWorld-0-3D ensures geometric consistency and physical realism via 3D generative modeling, Gaussian Splatting, differentiable system identification, and motion planning, creating a comprehensive data engine.

Quick Start & Requirements Installation requires Python 3.11.10 in a conda environment. Install giga-train, giga-datasets, natten via pip, then clone and install giga-models and the giga-world-0 repository. Users must download pre-trained models from Huggingface and prepare raw video data with text prompts. Inference scripts support single-GPU, multi-GPU, and LoRA-tuned models.

• Project Page: https://giga-world-0.github.io/
• ArXiv Paper: https://arxiv.org/abs/2511.19861
• Demo Video: https://github.com/giga-world-0/giga-world-0.github.io/releases/download/demo/Gigaworld-0.mp4
• Models: https://huggingface.co/open-gigaai

Highlighted Details

• GigaWorld-0-Video generates embodied sequences with fine-grained control over appearance, camera, and action semantics, supporting Image-to-Video (IT2V) synthesis.
• GigaWorld-0-3D ensures geometric consistency and physical realism through 3D Gaussian Splatting and differentiable system identification.
• Supports LoRA training for efficient fine-tuning.
• Inference pipelines are optimized for single-GPU, multi-GPU, and LoRA-based generation.

Maintenance & Community The provided README lacks specific details on maintainers, community channels, sponsorships, or a public roadmap.

Licensing & Compatibility Licensed under Apache 2.0, which is permissive for commercial use and integration into closed-source projects.

Limitations & Caveats The README does not explicitly detail any limitations, known bugs, alpha status, or deprecation warnings.