GPT-4V(ision) is a Human-Aligned Evaluator for Text-to-3D Generation

• Published in: 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) pp. 22227 - 22238
• Main Authors: Wu, Tong, Yang, Guandao, Li, Zhibing, Zhang, Kai, Liu, Ziwei, Guibas, Leonidas, Lin, Dahua, Wetzstein, Gordon
• Format: Conference Proceeding
• Language: English
• Published: IEEE 16.06.2024
• Subjects: 3D Generation; Adaptation models; Codes; Computational modeling; Computer vision; Design methodology; Evaluation; Measurement; Three-dimensional displays
• ISSN: 2575-7075
• DOI: 10.1109/CVPR52733.2024.02098

Despite recent advances in text-to-3D generative methods, there is a notable absence of reliable evaluation metrics. Existing metrics usually focus on a single criterion each, such as how well the asset aligned with the input text. These metrics lack the flexibility to generalize to different evaluation criteria and might not align well with human preferences. Conducting user preference studies is an alternative that offers both adaptability and human-aligned results. User studies, however, can be very ex-pensive to scale. This paper presents an automatic, ver-satile, and human-aligned evaluation metric for text-to-3D generative models. To this end, we first develop a prompt generator using GPT-4V to generate evaluating prompts, which serve as input to compare text-to-3D models. We further design a method instructing GPT-4V to compare two 3D assets according to user-defined crite-ria. Finally, we use these pairwise comparison results to assign these models Elo ratings. Experimental results suggest our metric strongly aligns with human preference across different evaluation criteria. Our code is available at https://github.com/3DTopia/GPTEval3D.