Paper
Evaluating Generative Models via One-Dimensional Code Distributions
Authors
Zexi Jia, Pengcheng Luo, Yijia Zhong, Jinchao Zhang, Jie Zhou
Abstract
Most evaluations of generative models rely on feature-distribution metrics such as FID, which operate on continuous recognition features that are explicitly trained to be invariant to appearance variations, and thus discard cues critical for perceptual quality. We instead evaluate models in the space of \emph{discrete} visual tokens, where modern 1D image tokenizers compactly encode both semantic and perceptual information and quality manifests as predictable token statistics. We introduce \emph{Codebook Histogram Distance} (CHD), a training-free distribution metric in token space, and \emph{Code Mixture Model Score} (CMMS), a no-reference quality metric learned from synthetic degradations of token sequences. To stress-test metrics under broad distribution shifts, we further propose \emph{VisForm}, a benchmark of 210K images spanning 62 visual forms and 12 generative models with expert annotations. Across AGIQA, HPDv2/3, and VisForm, our token-based metrics achieve state-of-the-art correlation with human judgments, and we will release all code and datasets to facilitate future research.
Metadata
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.08064v1</id>\n <title>Evaluating Generative Models via One-Dimensional Code Distributions</title>\n <updated>2026-03-09T07:57:56Z</updated>\n <link href='https://arxiv.org/abs/2603.08064v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.08064v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>Most evaluations of generative models rely on feature-distribution metrics such as FID, which operate on continuous recognition features that are explicitly trained to be invariant to appearance variations, and thus discard cues critical for perceptual quality. We instead evaluate models in the space of \\emph{discrete} visual tokens, where modern 1D image tokenizers compactly encode both semantic and perceptual information and quality manifests as predictable token statistics. We introduce \\emph{Codebook Histogram Distance} (CHD), a training-free distribution metric in token space, and \\emph{Code Mixture Model Score} (CMMS), a no-reference quality metric learned from synthetic degradations of token sequences. To stress-test metrics under broad distribution shifts, we further propose \\emph{VisForm}, a benchmark of 210K images spanning 62 visual forms and 12 generative models with expert annotations. Across AGIQA, HPDv2/3, and VisForm, our token-based metrics achieve state-of-the-art correlation with human judgments, and we will release all code and datasets to facilitate future research.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.CV'/>\n <published>2026-03-09T07:57:56Z</published>\n <arxiv:primary_category term='cs.CV'/>\n <author>\n <name>Zexi Jia</name>\n </author>\n <author>\n <name>Pengcheng Luo</name>\n </author>\n <author>\n <name>Yijia Zhong</name>\n </author>\n <author>\n <name>Jinchao Zhang</name>\n </author>\n <author>\n <name>Jie Zhou</name>\n </author>\n </entry>"
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