Make-Your-3D: Fast and Consistent Subject-Driven 3D Content Generation
Fangfu Liu Hanyang Wang Weiliang Chen Haowen Sun Yueqi Duan
Recent years have witnessed the strong power of 3D generation models, which
offer a new level of creative flexibility by allowing users to guide the 3D
content generation process through a single image or natural language. However,
it remains challenging for existing 3D generation methods to create
subject-driven 3D content across diverse prompts. In this paper, we introduce a
novel 3D customization method, dubbed Make-Your-3D that can personalize
high-fidelity and consistent 3D content from only a single image of a subject
with text description within 5 minutes. Our key insight is to harmonize the
distributions of a multi-view diffusion model and an identity-specific 2D
generative model, aligning them with the distribution of the desired 3D
subject. Specifically, we design a co-evolution framework to reduce the
variance of distributions, where each model undergoes a process of learning
from the other through identity-aware optimization and subject-prior
optimization, respectively. Extensive experiments demonstrate that our method
can produce high-quality, consistent, and subject-specific 3D content with
text-driven modifications that are unseen in subject image.