title: PrimitiveAnything: Human-Crafted 3D Primitive Assembly Generation with
    Auto-Regressive Transformer
summary: 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
paper_authors._id: 681c03418ff29a163ef5f370
paper_authors.name: Jingwen Ye
paper_authors._id: 681c03418ff29a163ef5f371
paper_authors.name: Yuze He
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-08T08:59:10.350Z
paper_authors._id: 64c903957b4d0d947ce86bc6
paper_authors.avatarUrl: /avatars/61d70a3ba00c83a5950f5c909a1a06f8.svg
paper_authors.fullname: Yuze He
paper_authors.type: user
paper_authors.user: hyz317
paper_authors._id: 681c03418ff29a163ef5f372
paper_authors.name: Yanning Zhou
paper_authors._id: 681c03418ff29a163ef5f373
paper_authors.name: Yiqin Zhu
paper_authors._id: 681c03418ff29a163ef5f374
paper_authors.name: Kaiwen Xiao
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-08T08:59:12.445Z
paper_authors._id: 6441491c5d600fb0951cd872
paper_authors.avatarUrl: /avatars/d98892f3b52d87c2328201efa9897110.svg
paper_authors.fullname: Kaiwen Xiao
paper_authors.type: user
paper_authors.user: loktarxiao
paper_authors._id: 681c03418ff29a163ef5f375
paper_authors.name: Yong-Jin Liu
paper_authors._id: 681c03418ff29a163ef5f376
paper_authors.name: Wei Yang
paper_authors._id: 681c03418ff29a163ef5f377
paper_authors.name: Xiao Han
paper_title: PrimitiveAnything: Human-Crafted 3D Primitive Assembly Generation with
    Auto-Regressive Transformer
paper_summary: 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
paper_ai_summary: 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.
paper_ai_keywords: 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