PrimitiveAnything: Human-Crafted 3D Primitive Assembly Generation with
  Auto-Regressive Transformer
Shape primitive abstraction, which decomposes complex 3D shapes into simple
geometric elements, plays a crucial role in human visual cognition and has
broad applications in computer vision and graphics. While recent advances in 3D
content generation have shown remarkable progress, existing primitive
abstraction methods either rely on geometric optimization with limited semantic
understanding or learn from small-scale, category-specific datasets, struggling
to generalize across diverse shape categories. We present PrimitiveAnything, a
novel framework that reformulates shape primitive abstraction as a primitive
assembly generation task. PrimitiveAnything includes a shape-conditioned
primitive transformer for auto-regressive generation and an ambiguity-free
parameterization scheme to represent multiple types of primitives in a unified
manner. The proposed framework directly learns the process of primitive
assembly from large-scale human-crafted abstractions, enabling it to capture
how humans decompose complex shapes into primitive elements. Through extensive
experiments, we demonstrate that PrimitiveAnything can generate high-quality
primitive assemblies that better align with human perception while maintaining
geometric fidelity across diverse shape categories. It benefits various 3D
applications and shows potential for enabling primitive-based user-generated
content (UGC) in games. Project page: https://primitiveanything.github.io
681c03418ff29a163ef5f370
Jingwen Ye
681c03418ff29a163ef5f371
Yuze He
claimed_verified
2025-05-08T08:59:10.350Z
64c903957b4d0d947ce86bc6
/avatars/61d70a3ba00c83a5950f5c909a1a06f8.svg
Yuze He
user
hyz317
681c03418ff29a163ef5f372
Yanning Zhou
681c03418ff29a163ef5f373
Yiqin Zhu
681c03418ff29a163ef5f374
Kaiwen Xiao
claimed_verified
2025-05-08T08:59:12.445Z
6441491c5d600fb0951cd872
/avatars/d98892f3b52d87c2328201efa9897110.svg
Kaiwen Xiao
user
loktarxiao
681c03418ff29a163ef5f375
Yong-Jin Liu
681c03418ff29a163ef5f376
Wei Yang
681c03418ff29a163ef5f377
Xiao Han
PrimitiveAnything: Human-Crafted 3D Primitive Assembly Generation with
  Auto-Regressive Transformer
Shape primitive abstraction, which decomposes complex 3D shapes into simple
geometric elements, plays a crucial role in human visual cognition and has
broad applications in computer vision and graphics. While recent advances in 3D
content generation have shown remarkable progress, existing primitive
abstraction methods either rely on geometric optimization with limited semantic
understanding or learn from small-scale, category-specific datasets, struggling
to generalize across diverse shape categories. We present PrimitiveAnything, a
novel framework that reformulates shape primitive abstraction as a primitive
assembly generation task. PrimitiveAnything includes a shape-conditioned
primitive transformer for auto-regressive generation and an ambiguity-free
parameterization scheme to represent multiple types of primitives in a unified
manner. The proposed framework directly learns the process of primitive
assembly from large-scale human-crafted abstractions, enabling it to capture
how humans decompose complex shapes into primitive elements. Through extensive
experiments, we demonstrate that PrimitiveAnything can generate high-quality
primitive assemblies that better align with human perception while maintaining
geometric fidelity across diverse shape categories. It benefits various 3D
applications and shows potential for enabling primitive-based user-generated
content (UGC) in games. Project page: https://primitiveanything.github.io
PrimitiveAnything is a novel framework for shape primitive abstraction that reformulates the task as primitive assembly generation using a shape-conditioned primitive transformer and achieves high-quality results across diverse shape categories.
primitive abstraction geometric optimization semantic understanding category-specific datasets shape-conditioned primitive transformer auto-regressive generation ambiguity-free parameterization large-scale human-crafted abstractions geometric fidelity primitive-based user-generated content