MagicArticulate: Make Your 3D Models Articulation-Ready

TL;DR

MagicArticulate introduces a comprehensive framework for automatically converting static 3D models into articulation-ready assets, utilizing a large-scale benchmark, a transformer-based skeleton generation, and a diffusion-based skinning weights prediction.

Contribution

It presents the first large-scale articulation benchmark, a novel transformer-based skeleton generation method, and a diffusion process for skinning weights prediction, advancing automation in 3D model articulation.

Findings

Outperforms existing methods in articulation quality

Handles diverse object categories effectively

Enables realistic animation of static models

Abstract

With the explosive growth of 3D content creation, there is an increasing demand for automatically converting static 3D models into articulation-ready versions that support realistic animation. Traditional approaches rely heavily on manual annotation, which is both time-consuming and labor-intensive. Moreover, the lack of large-scale benchmarks has hindered the development of learning-based solutions. In this work, we present MagicArticulate, an effective framework that automatically transforms static 3D models into articulation-ready assets. Our key contributions are threefold. First, we introduce Articulation-XL, a large-scale benchmark containing over 33k 3D models with high-quality articulation annotations, carefully curated from Objaverse-XL. Second, we propose a novel skeleton generation method that formulates the task as a sequence modeling problem, leveraging an auto-regressive transformer to naturally handle varying numbers of bones or joints within skeletons and their inherent dependencies across different 3D models. Third, we predict skinning weights using a functional diffusion process that incorporates volumetric geodesic distance priors between vertices and joints. Extensive experiments demonstrate that MagicArticulate significantly outperforms existing methods across diverse object categories, achieving high-quality articulation that enables realistic animation. Project page: https://chaoyuesong.github.io/MagicArticulate.