ThemeStation: Generating Theme-Aware 3D Assets from Few Exemplars
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.
6600fb883d247974ba253e90
Zhenwei Wang
admin_assigned
2024-03-25T16:21:54.715Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/6305e9471e731505a76b9099/w9csjXIEUzrd0sa1FxIXm.jpeg
Zhenwei WANG (Zavier)
user
ZhenweiWang
6600fb883d247974ba253e91
Tengfei Wang
admin_assigned
2024-03-25T10:55:00.012Z
62e7c26236a8e8a827ff0891
/avatars/15648407233bf5757262dbc04464d02e.svg
Tengfei Wang
user
tfwang
6600fb883d247974ba253e92
Gerhard Hancke
6600fb883d247974ba253e93
Ziwei Liu
admin_assigned
2024-03-25T10:55:21.146Z
62ab1ac1d48b4d8b048a3473
https://cdn-avatars.huggingface.co/v1/production/uploads/1656826685333-62ab1ac1d48b4d8b048a3473.png
Ziwei Liu
user
liuziwei7
6600fb883d247974ba253e94
Rynson W. H. Lau
ThemeStation: Generating Theme-Aware 3D Assets from Few Exemplars
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 generates high-quality, theme-aware 3D assets from few exemplars using a dual score distillation loss in a two-stage framework.
theme-aware 3D-to-3D generation dual score distillation concept image reference-informed 3D modeling theme-aware 3D models controllable 3D-to-3D generation