3DGen-Bench: Comprehensive Benchmark Suite for 3D Generative Models

• URL: http://arxiv.org/abs/2503.21745v3
• Date: Sun, 27 Jul 2025 09:53:26 GMT
• Title: 3DGen-Bench: Comprehensive Benchmark Suite for 3D Generative Models
• Authors: Yuhan Zhang, Mengchen Zhang, Tong Wu, Tengfei Wang, Gordon Wetzstein, Dahua Lin, Ziwei Liu,
• Abstract summary: 3D generation is experiencing rapid advancements, while the development of 3D evaluation has not kept pace.<n>We develop 3DGen-Arena, an integrated platform to gather human preferences from both public users and expert annotators.<n>Using this dataset, we further train a CLIP-based scoring model, 3DGen-Score, and a MLLM-based automatic evaluator, 3DGen-Eval.
• Score: 94.48803082248872
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 3D generation is experiencing rapid advancements, while the development of 3D evaluation has not kept pace. How to keep automatic evaluation equitably aligned with human perception has become a well-recognized challenge. Recent advances in the field of language and image generation have explored human preferences and showcased respectable fitting ability. However, the 3D domain still lacks such a comprehensive preference dataset over generative models. To mitigate this absence, we develop 3DGen-Arena, an integrated platform in a battle manner. Then, we carefully design diverse text and image prompts and leverage the arena platform to gather human preferences from both public users and expert annotators, resulting in a large-scale multi-dimension human preference dataset 3DGen-Bench. Using this dataset, we further train a CLIP-based scoring model, 3DGen-Score, and a MLLM-based automatic evaluator, 3DGen-Eval. These two models innovatively unify the quality evaluation of text-to-3D and image-to-3D generation, and jointly form our automated evaluation system with their respective strengths. Extensive experiments demonstrate the efficacy of our scoring model in predicting human preferences, exhibiting a superior correlation with human ranks compared to existing metrics. We believe that our 3DGen-Bench dataset and automated evaluation system will foster a more equitable evaluation in the field of 3D generation, further promoting the development of 3D generative models and their downstream applications. Project page is available at https://zyh482.github.io/3DGen-Bench/.

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