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.

我们引入了一种新颖的 3D 个性化方法，名为 Make-Your-3D，可以从单个主题图像和文本描述中提取个性化的高保真、一致的 3D 内容，在 5 分钟内完成。我们设计了一个共同进化的框架，通过身份感知优化和主题先验优化，使多视图扩散模型和身份特定的 2D 生成模型的分布与期望的 3D 主题的分布相一致。广泛的实验证明我们的方法可以产生高质量、一致性和主题特定的 3D 内容，并进行文本驱动的修改。