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arxiv: 2605.18132 · v1 · pith:WTDDUA6Anew · submitted 2026-05-18 · 💻 cs.CV · cs.AI

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

Sihan Ma , Siyuan Liang , Dacheng Tao This is my paper

Pith reviewed 2026-05-20 11:50 UTC · model grok-4.3

classification 💻 cs.CV cs.AI
keywords 3D generationsource attributiongenerative modelsfingerprintingmulti-view fusionbenchmarkcontent provenancegeometric artifacts
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The pith

Generative 3D models leave stable fingerprints that reveal which model created a given asset.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper shows that generative 3D models leave two stable types of fingerprints consisting of cross-view inconsistencies and structural artifacts visible in geometric statistics and frequency-domain cues. It builds the first benchmark covering 22 representative generators and tests attribution under full supervision, few-shot learning with 1 percent of labels, and realistic mixed real-synthetic conditions. A hierarchical multi-view multi-modal Transformer is introduced to fuse appearance, geometric, and frequency features inside each view while relating signals across views. Experiments reach 97.22 percent accuracy with full data and 77.17 percent with fewer than five samples per generator. This matters because it supplies a concrete way to trace the origin of 3D content used in games, robotics, and immersive environments.

Core claim

Modern generative 3D models leave two types of stable fingerprints: cross-view inconsistency and structural artifacts reflected in geometric statistics and frequency-domain cues. By constructing a benchmark with 22 generators under standard, few-shot, and realistic deployment protocols and training a hierarchical multi-view multi-modal Transformer that fuses appearance, geometric, and frequency-domain features within each view while modeling global relationships across views, the work demonstrates that these dispersed signals support source attribution at 97.22 percent accuracy under full supervision and 77.17 percent accuracy with only 1 percent training data.

What carries the argument

Hierarchical multi-view multi-modal Transformer that fuses appearance, geometric, and frequency-domain features within each view and models global relationships across views to capture dispersed attribution signals.

Load-bearing premise

The fingerprints remain stable and detectable under realistic deployment constraints including scarce labels, degraded prompts, and mixed real or synthetic assets.

What would settle it

A substantial drop in attribution accuracy when evaluating on assets produced with heavily varied prompts, post-processed geometry, or combinations of generators absent from the original benchmark.

Figures

Figures reproduced from arXiv: 2605.18132 by Dacheng Tao, Sihan Ma, Siyuan Liang.

Figure 1
Figure 1. Figure 1: Overview of our attribution framework. Given multi-view renderings and structural priors of a 3D asset, our model learns discriminative fingerprints through intra-view fusion and cross-view reasoning, enabling accurate attribution across 22 generative 3D models. Abstract Generative 3D models are deployed in gaming, robotics, and immersive creation, making source attribution critical: given a 3D asset, can … view at source ↗
Figure 2
Figure 2. Figure 2: Representative benchmark samples [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Dispersed attribution fingerprints in generated 3D assets. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Overview of our attribution model. Given multi-view renderings and structural priors from a 3D asset, our model learns dispersed attribution signals across viewpoints and modalities through multi-modal fusion within each single view and cross-view reasoning, achieving strong attribution accuracy and interpretable clustering across 22 generative 3D models. Observable cue tokenization. For each viewpoint i, … view at source ↗
Figure 5
Figure 5. Figure 5: Visualization of attribution fingerprints captured by our framework. [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Overview of our attribution benchmark [PITH_FULL_IMAGE:figures/full_fig_p014_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: View number ablation. View number ablation study. We further study the effect of the number of rendered views in [PITH_FULL_IMAGE:figures/full_fig_p017_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Row-normalized confusion matrix heatmap. Each value denotes the proportion of samples [PITH_FULL_IMAGE:figures/full_fig_p018_8.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper introduces the first passive source attribution benchmark for 3D generative models, spanning 22 generators under standard, few-shot, and realistic deployment protocols. It identifies two types of stable fingerprints—cross-view inconsistency and structural artifacts in geometric statistics and frequency-domain cues—and proposes a hierarchical multi-view multi-modal Transformer to fuse appearance, geometric, and frequency features across views. Experiments report 97.22% accuracy under full supervision and 77.17% accuracy with only 1% training data (fewer than five samples per generator), supporting the claim that modern 3D generators leave attributable fingerprints and establishing a foundation for 3D content provenance.

Significance. If the results hold, this work is significant for enabling trustworthy provenance in generative 3D content, an area of growing importance in gaming, robotics, and immersive applications. The construction of a new benchmark covering multiple generators and protocols, combined with strong empirical performance in the low-data regime, provides a concrete methodological foundation and falsifiable baseline for future attribution research.

major comments (2)
  1. [Section 3] Benchmark Construction and Evaluation Protocols (Section 3): The central claim that fingerprints are 'stable' and remain detectable under realistic constraints is load-bearing for both the 77.17% few-shot result and the motivation for multi-modal fusion, yet the paper does not report experiments testing invariance to common post-processing operations such as mesh simplification, format conversion, coordinate normalization, or integration into mixed real/synthetic scenes. The benchmark treats collected assets as fixed representations, leaving open the possibility that reported accuracies partly reflect export-pipeline artifacts rather than intrinsic generator properties.
  2. [Section 4] Hierarchical Fusion Method (Section 4): While the Transformer fuses multi-view and multi-modal cues, there is insufficient ablation or analysis quantifying the individual contribution of cross-view inconsistency versus geometric/frequency artifacts, particularly in the 1%-data regime; this weakens the justification for the hierarchical design over simpler baselines.
minor comments (2)
  1. [Abstract] The abstract and introduction could more explicitly situate the new benchmark against prior 2D attribution or forensic work to strengthen the 'first' claim.
  2. [Section 3] Clarify the precise definition and implementation of 'realistic deployment protocols' to make the generalization claims easier to evaluate.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which help strengthen the robustness and analysis in our work on source attribution for generative 3D models. We address each major comment below and will incorporate revisions to improve the manuscript.

read point-by-point responses

  1. Referee: [Section 3] Benchmark Construction and Evaluation Protocols (Section 3): The central claim that fingerprints are 'stable' and remain detectable under realistic constraints is load-bearing for both the 77.17% few-shot result and the motivation for multi-modal fusion, yet the paper does not report experiments testing invariance to common post-processing operations such as mesh simplification, format conversion, coordinate normalization, or integration into mixed real/synthetic scenes. The benchmark treats collected assets as fixed representations, leaving open the possibility that reported accuracies partly reflect export-pipeline artifacts rather than intrinsic generator properties.

    Authors: We agree that explicit tests for post-processing operations would further validate the stability of the identified fingerprints. Our benchmark already incorporates realistic deployment protocols, including few-shot settings and mixed real/synthetic scenes, but we did not include dedicated experiments on operations such as mesh simplification or format conversion. In the revised manuscript, we will add a new analysis subsection with preliminary results on a subset of generators evaluating attribution performance after mesh simplification and coordinate normalization. We will also explicitly discuss the possibility of export-pipeline artifacts as a limitation. This addresses the concern while preserving the core claim that the fingerprints are attributable to generator properties. revision: yes

  2. Referee: [Section 4] Hierarchical Fusion Method (Section 4): While the Transformer fuses multi-view and multi-modal cues, there is insufficient ablation or analysis quantifying the individual contribution of cross-view inconsistency versus geometric/frequency artifacts, particularly in the 1%-data regime; this weakens the justification for the hierarchical design over simpler baselines.

    Authors: We acknowledge that more detailed ablations would strengthen the justification for the hierarchical multi-view multi-modal design. The architecture is motivated by the dispersed signals observed in the benchmark (cross-view inconsistency and structural artifacts in geometric and frequency domains), but we did not provide component-wise breakdowns specifically for the 1% data regime. In the revision, we will expand Section 4 with additional ablation studies, including performance when removing the cross-view modeling module and when using only geometric or frequency features, reported under both full supervision and 1% training data. These results will better quantify contributions and compare against simpler baselines. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical benchmark construction and model evaluation

full rationale

The paper constructs a new benchmark covering 22 3D generators and reports empirical accuracies (97.22% full supervision, 77.17% with 1% data) for a proposed hierarchical multi-view multi-modal Transformer. No derivation chain, equations, or predictions are presented that reduce by construction to fitted inputs, self-citations, or renamed ansatzes. The fingerprints are observed from the benchmark rather than defined into existence, and the method is a standard fusion architecture trained and evaluated on held-out data. The work is self-contained against its external benchmark with no load-bearing self-referential steps.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

Review based on abstract only; specific model hyperparameters and exact benchmark construction details are not provided.

free parameters (1)
  • Transformer fusion and attention hyperparameters
    The hierarchical multi-view multi-modal Transformer requires choices for layer counts, attention mechanisms, and feature fusion weights that are typically tuned on the training data.
axioms (1)
  • domain assumption Generative 3D models produce assets containing stable, detectable fingerprints in cross-view, geometric, and frequency domains.
    This assumption underpins both the benchmark findings and the design of the attribution model.

pith-pipeline@v0.9.0 · 5786 in / 1379 out tokens · 66851 ms · 2026-05-20T11:50:21.175441+00:00 · methodology

discussion (0)

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