Phidias: A Generative Model for Creating 3D Content from Text, Image,
  and 3D Conditions with Reference-Augmented Diffusion
Authors: name: Zhenwei Wang, name: Tengfei Wang, name: Zexin He, name: Gerhard Hancke, name: Ziwei Liu, name: Rynson W. H. Lau

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.