Real-world applications often require a large gallery of 3D assets that share a consistent theme. While remarkable advances have been made in general 3D content creation from text or image, synthesizing customized 3D assets following the shared theme of input 3D exemplars remains an open and challenging problem. In this work, we present ThemeStation, a novel approach for theme-aware 3D-to-3D generation. ThemeStation synthesizes customized 3D assets based on given few exemplars with two goals: 1) unity for generating 3D assets that thematically align with the given exemplars and 2) diversity for generating 3D assets with a high degree of variations. To this end, we design a two-stage framework that draws a concept image first, followed by a reference-informed 3D modeling stage. We propose a novel dual score distillation (DSD) loss to jointly leverage priors from both the input exemplars and the synthesized concept image. Extensive experiments and user studies confirm that ThemeStation surpasses prior works in producing diverse theme-aware 3D models with impressive quality. ThemeStation also enables various applications such as controllable 3D-to-3D generation.

ThemeStation通过给定的少量示例，通过首先绘制概念图像，然后进行参考引导的3D建模阶段，合成与示例相同主题的自定义3D资产，以进一步实现生成与给定示例相一致并具有高度变化的多样化主题感知3D模型的目标，并使用新颖的双重分数蒸馏（DSD）损失来整合输入示例和合成概念图像的经验。

ThemeStation: 从少数示例生成主题感知的3D资源