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

TL;DR

Phidias is a novel diffusion-based generative model that creates 3D content from text, images, and 3D references, improving quality, control, and generalization in 3D modeling tasks.

Contribution

It introduces a unified framework with innovative components like meta-ControlNet and dynamic reference routing for enhanced 3D generation from multiple conditions.

Findings

Significantly improves 3D generation quality.

Enhances controllability and generalization.

Outperforms existing methods in various tasks.

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.