ViGoR-Bench: How Far Are Visual Generative Models From Zero-Shot Visual Reasoners?
Beneath the stunning visual fidelity of modern AIGC models lies a "logical desert", where systems fail tasks that require physical, causal, or complex spatial reasoning. Current evaluations largely rely on superficial metrics or fragmented benchmarks, creating a ``performance mirage'' that overlooks the generative process. To address this, we introduce ViGoR Vision-G}nerative Reasoning-centric Benchmark), a unified framework designed to dismantle this mirage. ViGoR distinguishes itself through four key innovations: 1) holistic cross-modal coverage bridging Image-to-Image and Video tasks; 2) a dual-track mechanism evaluating both intermediate processes and final results; 3) an evidence-grounded automated judge ensuring high human alignment; and 4) granular diagnostic analysis that decomposes performance into fine-grained cognitive dimensions. Experiments on over 20 leading models reveal that even state-of-the-art systems harbor significant reasoning deficits, establishing ViGoR as a critical ``stress test'' for the next generation of intelligent vision models. The demo have been available at https://vincenthancoder.github.io/ViGoR-Bench/
69ccf52c1f6782218d973df9
Haonan Han
69ccf52c1f6782218d973dfa
Jiancheng Huang
69ccf52c1f6782218d973dfb
Xiaopeng Sun
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2026-04-03T09:08:52.430Z
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Sun
Xiaopro
user
Xiaopro
69ccf52c1f6782218d973dfc
Junyan He
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Rui Yang
69ccf52c1f6782218d973dfe
Jie Hu
69ccf52c1f6782218d973dff
Xiaojiang Peng
69ccf52c1f6782218d973e00
Lin Ma
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Xiaoming Wei
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Xiu Li
ViGoR-Bench: How Far Are Visual Generative Models From Zero-Shot Visual Reasoners?
Beneath the stunning visual fidelity of modern AIGC models lies a "logical desert", where systems fail tasks that require physical, causal, or complex spatial reasoning. Current evaluations largely rely on superficial metrics or fragmented benchmarks, creating a ``performance mirage'' that overlooks the generative process. To address this, we introduce ViGoR Vision-G}nerative Reasoning-centric Benchmark), a unified framework designed to dismantle this mirage. ViGoR distinguishes itself through four key innovations: 1) holistic cross-modal coverage bridging Image-to-Image and Video tasks; 2) a dual-track mechanism evaluating both intermediate processes and final results; 3) an evidence-grounded automated judge ensuring high human alignment; and 4) granular diagnostic analysis that decomposes performance into fine-grained cognitive dimensions. Experiments on over 20 leading models reveal that even state-of-the-art systems harbor significant reasoning deficits, establishing ViGoR as a critical ``stress test'' for the next generation of intelligent vision models. The demo have been available at https://vincenthancoder.github.io/ViGoR-Bench/
ViGoR benchmark addresses limitations in current AIGC evaluation by introducing a comprehensive framework for assessing visual generative reasoning across multiple modalities and cognitive dimensions.
visual generative reasoning AIGC models performance mirage cross-modal coverage dual-track mechanism automated judge diagnostic analysis cognitive dimensions