3DGen-Bench: Comprehensive Benchmark Suite for 3D Generative Models
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909707747196928 |
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| author | Zhang, Yuhan Zhang, Mengchen Wu, Tong Wang, Tengfei Wetzstein, Gordon Lin, Dahua Liu, Ziwei |
| author_facet | Zhang, Yuhan Zhang, Mengchen Wu, Tong Wang, Tengfei Wetzstein, Gordon Lin, Dahua Liu, Ziwei |
| contents | 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/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_21745 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | 3DGen-Bench: Comprehensive Benchmark Suite for 3D Generative Models Zhang, Yuhan Zhang, Mengchen Wu, Tong Wang, Tengfei Wetzstein, Gordon Lin, Dahua Liu, Ziwei Computer Vision and Pattern Recognition 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/. |
| title | 3DGen-Bench: Comprehensive Benchmark Suite for 3D Generative Models |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.21745 |