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PrimitiveAnything: Human-Crafted 3D Primitive Assembly Generation with Auto-Regressive Transformer

Jingwen Ye, Yuze He, Yanning Zhou, Yiqin Zhu, Kaiwen Xiao, Yong-Jin Liu, Wei Yang, Xiao Han

2025-05-08

PrimitiveAnything: Human-Crafted 3D Primitive Assembly Generation with
  Auto-Regressive Transformer

Summary

This paper talks about PrimitiveAnything, a new system that can create complex 3D shapes by putting together simple building blocks, called primitives, using a smart AI model.

What's the problem?

The problem is that making detailed 3D models usually takes a lot of time and skill, since artists have to design every little part. It's also hard for computers to break down complicated shapes into basic pieces in a way that looks good and makes sense.

What's the solution?

The researchers built PrimitiveAnything, which uses an AI called a transformer to figure out how to assemble simple shapes into more complex ones, based on what kind of object it's supposed to make. This approach lets the system create high-quality 3D models in many different categories by automatically deciding how to put the pieces together.

Why it matters?

This matters because it makes creating 3D models much easier and faster, which is useful for things like video games, movies, and virtual reality. It also helps computers better understand and work with 3D shapes, opening up new possibilities for design and creativity.

Abstract

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.