Recent advances in text-guided 3D avatar generation have made substantial progress by distilling knowledge from diffusion models. Despite the plausible generated appearance, existing methods cannot achieve fine-grained disentanglement or high-fidelity modeling between inner body and outfit. In this paper, we propose Barbie, a novel framework for generating 3D avatars that can be dressed in diverse and high-quality Barbie-like garments and accessories. Instead of relying on a holistic model, Barbie achieves fine-grained disentanglement on avatars by semantic-aligned separated models for human body and outfits. These disentangled 3D representations are then optimized by different expert models to guarantee the domain-specific fidelity. To balance geometry diversity and reasonableness, we propose a series of losses for template-preserving and human-prior evolving. The final avatar is enhanced by unified texture refinement for superior texture consistency. Extensive experiments demonstrate that Barbie outperforms existing methods in both dressed human and outfit generation, supporting flexible apparel combination and animation. The code will be released for research purposes. Our project page is: https://2017211801.github.io/barbie.github.io/.

本研究解决了现有文本引导的3D头像生成方法在内体和服饰之间难以做到细粒度解耦和高保真建模的问题。提出的Barbie框架采用语义对齐的分离模型，实现了头像的细粒度解耦，并通过专家模型优化以确保领域特定的保真度。实验表明，Barbie在生成穿戴人类和服饰方面的性能优于现有方法，具有更高的灵活性和一致性。

芭比：文本到芭比风格的3D头像