Cyc3D: Evaluating Cyclic Structural Stability and Asset Usability in Image-to-3D Generation

• URL: http://arxiv.org/abs/2608.28080v1
• Date: Fri, 28 Aug 2026 08:50:33 GMT
• Title: Cyc3D: Evaluating Cyclic Structural Stability and Asset Usability in Image-to-3D Generation
• Abstract summary: We introduce Cyc3D, a multidimensional benchmark that evaluates image-to-3D generation.<n>At the asset level, Cyc3D measures whether object identity remains semantically coherent across rendered viewpoints.<n>At the model level, we propose View-Cycle Structural Consistency, a closed-loop render-regenerate-align protocol.
• Score: 8.124067257650157
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image-conditioned 3D generation has advanced rapidly, yet existing evaluation protocols largely judge rendered-view plausibility and semantic alignment, overlooking whether a generator forms a stable 3D interpretation and produces assets usable in graphics pipelines. We introduce Cyc3D, a multidimensional benchmark that evaluates image-to-3D generation along two complementary axes: Cross-View Object Consistency and Representation Quality. At the asset level, Cyc3D measures whether object identity remains semantically coherent across rendered viewpoints. At the model level, we propose View-Cycle Structural Consistency, a closed-loop render-regenerate-align protocol that repeatedly re-observes a generated asset from novel views and quantifies geometric, perceptual, and semantic drift across generations. To assess native asset usability beyond rendered appearance, Cyc3D further evaluates geometric structure, reference-image fidelity, mesh discretization and efficiency, and UV parameterization quality. Together, these diagnostics expose failures obscured by a single perceptual score and provide interpretable evidence of both model instability and representation defects. Experiments on five representative image-to-3D systems show that closed-source feed-forward models consistently outperform open-source optimization-based baselines in geometric fidelity, mesh quality, and cycle stability. Nevertheless, even the strongest methods achieve cycle-stability scores below 48, revealing a persistent gap between visually plausible generation and robust 3D object understanding.

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