PrimitiveAnything: Human-Crafted 3D Primitive Assembly Generation with
  Auto-Regressive Transformer
Authors: name: Jingwen Ye, name: Yuze He, name: Yanning Zhou, name: Yiqin Zhu, name: Kaiwen Xiao, name: Yong-Jin Liu, name: Wei Yang, name: Xiao Han

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