Project information

  • Company: ORamaVR
  • Categories: Generative AI, 3D Deep Learning, NLP
  • Main technologies: Python, PyTorch, NVIDIA NeMo, Weights & Biases (WandB), vLLM, HuggingFace

Summary

Mugiwara is an end-to-end training and evaluation pipeline designed to teach Transformer models how to generate 3D meshes from scratch.

Leveraging the OVR-Datasets infrastructure, the pipeline streams raw 3D meshes, converts their geometric data into token sequences, and feeds them into a Transformer model that learns 3D shape reconstruction and generation.

To ensure scalability and fast iteration, the system was built on the NVIDIA NeMo framework. I integrated Weights & Biases for rigorous model evaluation and tracking. Finally, the trained models were published to HuggingFace and optimized with vLLM to ensure highly efficient, low-latency inference.