ThemeStation: Generating Theme-Aware 3D Assets from Few Exemplars
Zhenwei Wang Tengfei Wang Gerhard Hancke Ziwei Liu Rynson W. H. Lau
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.