MagicArticulate: Make Your 3D Models Articulation-Ready

2025-02-17

AI Summary

This work addresses the challenging problem of automatic articulation of static 3D models—a long-standing bottleneck due to reliance on manual annotations and the absence of large-scale benchmarks. Methodologically, we propose the first end-to-end learnable framework comprising: (1) Articulation-XL, the first large-scale (33K), cross-category articulation benchmark; (2) an autoregressive Transformer that generates semantically plausible and topologically consistent skeletal sequences; and (3) a functional diffusion model incorporating voxelized geodesic distance priors for high-fidelity skin weight prediction. Evaluated on the Objaverse-XL cleaned dataset, our method significantly outperforms state-of-the-art approaches in skeletal plausibility, weight smoothness, and animation fidelity, enabling high-quality physics-driven animation. All code, data, and an interactive demo are publicly released.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Automatic conversion of static 3D models
• Creation of articulation-ready 3D assets
• Development of a large-scale 3D benchmark

Innovation

• Automated 3D model articulation
• Transformer-based skeleton generation
• Functional diffusion for skinning weights