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

• URL: http://arxiv.org/abs/2605.18132v1
• Date: Mon, 18 May 2026 09:36:48 GMT
• Title: Who Generated This 3D Asset? Learning Source Attribution for Generative 3D Models
• Abstract summary: Generative 3D models are deployed in gaming, robotics, and immersive creation.<n>Can we identify whether and which generative model created it?<n>This problem faces two core challenges: dispersed attribution signals and realistic deployment constraints.
• Score: 66.3899679538548
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Generative Texture Diversification of 3D Pedestrians for Robust Autonomous Driving Perception [0.0]
  In this paper, we propose a method for scaling variability in 3D pedestrian assets for synthetic scene generation.<n>We generate multiple distinct pedestrian instances by synthesizing diverse facial textures and identity-level appearance variations.<n>Using the assets, we construct synthetic datasets and study the impact of mixing real and synthetic data for RGB-based object detection.
  arXiv  Detail & Related papers  (2026-05-13T16:35:50Z)
• Step1X-3D: Towards High-Fidelity and Controllable Generation of Textured 3D Assets [90.99212668875971]
  Step1X-3D is an open framework addressing challenges such as data scarcity, algorithmic limitations, and ecosystem fragmentation.<n>We present a two-stage 3D-native architecture combining a hybrid VAE-DiT geometry generator with a diffusion-based texture synthesis module.<n> Benchmark results demonstrate state-of-the-art performance that exceeds existing open-source methods.
  arXiv  Detail & Related papers  (2025-05-12T16:56:30Z)
• GEAL: Generalizable 3D Affordance Learning with Cross-Modal Consistency [50.11520458252128]
  Existing 3D affordance learning methods struggle with generalization and robustness due to limited annotated data.<n>We propose GEAL, a novel framework designed to enhance the generalization and robustness of 3D affordance learning by leveraging large-scale pre-trained 2D models.<n>GEAL consistently outperforms existing methods across seen and novel object categories, as well as corrupted data.
  arXiv  Detail & Related papers  (2024-12-12T17:59:03Z)
• DSplats: 3D Generation by Denoising Splats-Based Multiview Diffusion Models [67.50989119438508]
  We introduce DSplats, a novel method that directly denoises multiview images using Gaussian-based Reconstructors to produce realistic 3D assets.<n>Our experiments demonstrate that DSplats not only produces high-quality, spatially consistent outputs, but also sets a new standard in single-image to 3D reconstruction.
  arXiv  Detail & Related papers  (2024-12-11T07:32:17Z)
• Gaussian Shell Maps for Efficient 3D Human Generation [96.25056237689988]
  3D generative adversarial networks (GANs) have demonstrated state-of-the-art (SOTA) quality and diversity for generated assets. Current 3D GAN architectures, however, rely on volume representations, which are slow to render, thereby hampering the GAN training and requiring multi-view-inconsistent 2D upsamplers.
  arXiv  Detail & Related papers  (2023-11-29T18:04:07Z)
• Deep Generative Models on 3D Representations: A Survey [81.73385191402419]
  Generative models aim to learn the distribution of observed data by generating new instances. Recently, researchers started to shift focus from 2D to 3D space. representing 3D data poses significantly greater challenges.
  arXiv  Detail & Related papers  (2022-10-27T17:59:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.