Who Generated This 3D Asset? Learning Source Attribution for Generative 3D Models

TL;DR

This paper introduces a benchmark and a multi-view Transformer approach for source attribution of generative 3D models, demonstrating high accuracy even with limited training data.

Contribution

It constructs the first comprehensive benchmark for 3D source attribution and proposes a novel hierarchical Transformer model to effectively capture dispersed generative fingerprints.

Findings

Achieves 97.22% accuracy with full supervision.

Reaches 77.17% accuracy with less than five samples per generator.

Identifies stable fingerprints like cross-view inconsistency and structural artifacts.

Abstract

Generative 3D models are deployed in gaming, robotics, and immersive creation, making source attribution critical: given a 3D asset, can we identify whether and which generative model created it? This problem faces two core challenges: dispersed attribution signals, where 3D fingerprints are distributed across multi-view, geometric, and frequency-domain cues; and realistic deployment constraints, where scarce labels, degraded prompts, and mixed real/synthetic assets undermine attribution reliability. To systematically study this problem, we construct, to the best of our knowledge, the first passive source attribution benchmark for modern generated assets, covering 22 representative 3D generators under standard, few-shot, and realistic deployment protocols. Based on this benchmark, we find that generative 3D models leave two types of stable fingerprints: cross-view inconsistency and structural artifacts reflected in geometric statistics and frequency-domain cues. To capture these dispersed signals, we propose a hierarchical multi-view multi-modal Transformer that fuses appearance, geometric, and frequency-domain features within each view and models global relationships across views. Extensive experiments demonstrate strong performance, achieving 97.22% accuracy under full supervision and 77.17% accuracy with only 1% training data, corresponding to fewer than five samples per generator. These results show that modern 3D generators leave stable and attributable fingerprints, establishing a new benchmark and methodological foundation for trustworthy 3D content provenance.