⚡ electrical engineering

Toward Uncertainty Quantification in Modern Art

This paper introduces a novel protocol and the first dedicated corpus for quantifying generative uncertainty in modern art animation, demonstrating that source-blind, multi-dimensional estimators can effectively distinguish between different types of semantic variation and identify faithful renderings, significantly outperforming traditional scalar-based uncertainty metrics.

Original authors: Tirtho Roy, Ushashi Bhattacharjee, Showrav Kumar Saha, Sayantan Chakraborty, Koushik Howlader, Tanusree Bhattacharjee

Published 2026-08-06

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CC BY 4.0

Original authors: Tirtho Roy, Ushashi Bhattacharjee, Showrav Kumar Saha, Sayantan Chakraborty, Koushik Howlader, Tanusree Bhattacharjee

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine you are a director asking a magical, invisible camera crew to film a scene based on a single, vague instruction: "Make the painting move." In the world of artificial intelligence, these "cameras" are text-to-video models, and the "paintings" are modern art. Modern art is special because it is designed to be open to interpretation; a single abstract shape might look like a storm to one person and a dance to another. When you ask the AI to film this painting, it doesn't just make one movie; it makes many, depending on a tiny, random number called a "seed" that it picks at the start. Sometimes the seeds produce movies that look very different from each other.

For a long time, scientists trying to measure how "confused" or "uncertain" the AI is have used a simple ruler: they just measure how far apart the movies are from each other and give a single number, like "5 out of 10." But this is like saying a group of people are "loud" without telling you if they are all shouting the same thing, if one person is screaming while the others whisper, or if they are arguing in two different languages. The big question this paper tackles is: Can we look at a group of AI-generated movies and figure out how they are different, not just how much? This matters because if an AI is making art, we want to know if it's offering us a rich variety of creative ideas or if it's just glitching out and failing to capture the original artwork.

The researchers in this paper decided to stop using the single "loudness" ruler and instead built a sophisticated detective kit to analyze the shape of the AI's confusion. They created a new way to look at groups of videos generated from the same modern art caption. Instead of just saying "these videos are different," their method asks specific questions: Are the videos all huddled together in one tight cluster? Is there one weird video that stands out like a sore thumb (an outlier)? Are there two distinct groups of videos, like two different interpretations fighting for attention? Or are the videos scattered everywhere with no clear pattern?

To test this, they built a massive library of 1,000 videos. They took 250 captions describing modern artworks and asked a powerful AI model, Wan2.1, to generate four different videos for each caption using four different random seeds. Crucially, the AI never saw the original artwork; it only saw the text description. The researchers then ran their new "detective kit" on these videos.

The results were fascinating. First, they found that their new kit could successfully identify the "shape" of the group. It could tell the difference between a tight group of similar videos and a group with one weird outlier with near-perfect accuracy (98% accuracy), whereas the old single-number method failed to see the difference at all. They also discovered that high uncertainty doesn't always mean the AI is failing. Sometimes, the AI produces many different versions that still all capture the spirit of the original art (reference covering). Other times, the AI produces many versions, but none of them actually look like the original art (reference missing). Their new protocol can spot this difference, which the old methods could not.

However, the paper also delivered some important "no-go" signs. Even though the new kit is great at describing the shape of the uncertainty, it didn't turn out to be better at predicting exactly what the AI would disagree on (like whether the AI thought the painting was about sadness or anger). The old, simple "how far apart" number was just as good at predicting that kind of disagreement. Furthermore, the researchers confirmed that looking at the videos without seeing the original artwork (source blind) can tell you how the AI is interpreting the prompt, but it cannot tell you if the AI is faithfully copying the original art.

In short, this paper proves that we can move beyond simple numbers to understand the structure of an AI's creative choices. We can now tell if an AI is offering a diverse range of valid interpretations or if it's just spinning its wheels. While this doesn't magically make the AI predict the future better, it gives us a much sharper tool to understand how these creative machines think, helping us decide when to accept their output and when to ask for a do-over. The researchers have shared their code and their library of 1,000 videos so others can use this new way of looking at AI uncertainty.

Technical Summary: Toward Uncertainty Quantification in Modern Art

Problem Statement
Current text-to-video models, when tasked with animating ambiguous modern art under different random seeds, produce visibly divergent films. In the context of modern art, where ambiguity is intentional, this divergence represents distinct interpretations (signal) rather than random noise. However, prevailing Uncertainty Quantification (UQ) methods reduce a set of generations to a single scalar value (e.g., directional concentration, pairwise dispersion, or semantic entropy). While a scalar can quantify how much seeds disagree, it fails to describe how they disagree. It cannot distinguish between a compact cluster of interpretations, a dominant reading with a single outlier, two competing coherent modes, or diffuse instability. Consequently, existing metrics cannot guide systems on whether to accept a generation, discard a specific seed, present alternatives, or abstain.

Methodology
The authors propose the first systematic study of the structure of generative uncertainty for modern art animation, introducing a reusable, source-blind protocol.

Corpus Construction: The study utilizes a newly constructed corpus of 250 modern artwork captions (spanning Post-Impressionism, Pop Art, Surrealism, and a generic set). These were rendered by the Wan2.1 14B model under four distinct seeds, generating 1,000 videos. Crucially, the original artwork images are withheld from the generation process and used only for evaluation. The videos are embedded using four different encoders (OpenCLIP ViT B/32, ViT L/14, SigLIP, and DINOv2).
The Protocol: The framework consists of four main components:
1. Estimator Suite: Seven source-blind estimators (reading only the generated videos) and six reference-aware estimators (consulting the hidden artwork for evaluation only).
2. Distributional Profile: A decomposition of the empirical interpretation distribution into named structural components: global spread, robust spread, outlier influence, explicit topology (classifying shapes like 4, 3+1, 2+2, etc.), multimodality, anisotropy, leave-one-seed influence, and reference coverage.
3. Distribution Model Ablation: Fitting six parametric models (vMF, Kent, ACG, Student t, kernel, mixture) to seed sets to gauge how much structure four seeds can support.
4. Statistical Protocol: A rigorous evaluation framework involving 104 bootstrap replicates, permutation p-values, and strict calibration hygiene to ensure reliability across encoders and seed counts.

Key Results
The protocol was evaluated against eight research questions (RQ1–RQ8), yielding both positive diagnostic capabilities and specific negative limits:

Redundancy of Scalars (RQ1): Source-blind scalar estimators are highly redundant. Ten of 21 pairs showed correlations $|r| > 0.8$ , with a maximum of 1.00. A single dispersion axis accounts for most of the information provided by these scalars.
Structural Recovery (RQ2): The distributional profile successfully recovers structure that scalars miss. It classifies controlled topologies (compact, 3+1, 2+2, diffuse) with a balanced accuracy of 0.98 (chance 0.25) and identifies outlier configurations (3+1) with an AUROC of 1.00, whereas the standard Directional Concentration Uncertainty (DCU) scalar achieves only 0.35.
Semantic Disagreement Prediction (RQ6): Contrary to the hope that structure adds predictive power, the full distributional profile does not outperform a simple pairwise dispersion scalar in predicting cross-seed semantic disagreement (Cross-Validated $R^2$ of 0.240 vs. 0.243 for the scalar).
Reference Coverage vs. Uncertainty (RQ4, RQ5): The protocol successfully separates high uncertainty into two distinct categories: "reference covering" diversity (where seeds vary but remain faithful to the art, $n=97$ ) and "reference missing" diversity (where seeds fail to capture the art, $n=56$ ). Source-blind uncertainty tracks interpretation variance, not reference fidelity.
Reliability (RQ7, RQ8): Simple estimators (pairwise, DCU) are reliable even with subsets of seeds (3 of 4 or 2 of 4) and remain stable across different encoders. However, richer structural components (like mode ratio) are fragile and require more seeds for stability.

Significance and Claims
The paper claims to be the first systematic study of the structure of generative uncertainty in the context of modern art animation. Its primary significance lies in shifting the focus from "how uncertain is the generator?" to "what is the structure of that uncertainty?"

The authors assert that for modern art generation, structure identification is where source-blind uncertainty carries actionable information, not the scalar magnitude. The protocol provides a diagnostic tool to:

Classify the topology of a seed set (e.g., distinguishing a single outlier from a bimodal split).
Isolate outlier configurations.
Distinguish between meaningful variation (reference covering) and failure to cover the target.

The paper is modest about its limitations, explicitly stating that adding structural complexity does not improve the prediction of semantic disagreement over simple dispersion and that source-blind uncertainty cannot substitute for reference fidelity checks. The authors release the protocol, the 1,000-video corpus, and the evaluation harness to facilitate further research.

Technical Summary: Toward Uncertainty Quantification in Modern Art

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