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

Tong Wu, Guandao Yang, Zhibing Li, Kai Zhang, Ziwei Liu, Leonidas Guibas, Dahua Lin, Gordon Wetzstein; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 22227-22238

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 expensive to scale. This paper presents an automatic versatile 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 criteria. Finally we use these pairwise comparison results to assign these models Elo ratings. Experimental results suggest our metric strongly align with human preference across different evaluation criteria.