Phidias: A Generative Model for Creating 3D Content from Text, Image, and 3D Conditions with Reference-Augmented Diffusion

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Abstract

In 3D modeling, designers often use an existing 3D model as a reference to create new ones. This practice has inspired the development of Phidias, a novel generative model that uses diffusion for reference-augmented 3D generation. Given an image, our method leverages a retrieved or user-provided 3D reference model to guide the generation process, thereby enhancing the generation quality, generalization ability, and controllability. Our model integrates three key components: 1) meta-ControlNet that dynamically modulates the conditioning strength, 2) dynamic reference routing that mitigates misalignment between the input image and 3D reference, and 3) self-reference augmentations that enable self-supervised training with a progressive curriculum. Collectively, these designs result in a clear improvement over existing methods. Phidias establishes a unified framework for 3D generation using text, image, and 3D conditions with versatile applications.

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Citation

If you find this work helpful for your research, please cite:

@article{wang2024phidias,
        title={Phidias: A Generative Model for Creating 3D  Content from Text, Image, and 3D Conditions with Reference-Augmented  Diffusion}, 
        author={Zhenwei Wang and Tengfei Wang and Zexin He and Gerhard Hancke and Ziwei Liu and Rynson W.H. Lau},
        eprint={2409.11406},
        archivePrefix={arXiv},
        primaryClass={cs.CV},
        year={2024},
        url={https://arxiv.org/abs/2409.11406},
  }