2025/04/10 by Lourdes Agapito, Maiti, Shalini, Agapito, Lourdes +2 · 2 citations
Computer Science · Earth and Planetary Sciences · Engineering · #Image Processing and 3D Reconstruction #3D Surveying and Cultural Heritage #3D Shape Modeling and Analysis
paper · pdf · doi:10.48550/arxiv.2504.08125
Rapid advancements in text-to-3D generation require robust and scalable evaluation metrics that align closely with human judgment, a need unmet by current metrics such as PSNR and CLIP, which require ground-truth data or focus only on prompt fidelity. To address this, we introduce Gen3DEval, a novel evaluation framework that leverages vision large language models (vLLMs) specifically fine-tuned for 3D object quality assessment. Gen3DEval evaluates text fidelity, appearance, and surface quality by analyzing 3D surface normals, without requiring ground-truth comparisons, bridging the gap between automated metrics and user preferences. Compared to state-of-the-art task-agnostic models, Gen3DEval demonstrates superior performance in user-aligned evaluations, placing it as a comprehensive and accessible benchmark for future research on text-to-3D generation. The project page can be found here: \hrefhttps://shalini-maiti.github.io/gen3deval.github.io/https://shalini-maiti.github.io/gen3deval.github.io/.