Looking at words and points with attention: a benchmark for text-to-shape coherence

• URL: http://arxiv.org/abs/2309.07917v1
• Date: Thu, 14 Sep 2023 17:59:48 GMT
• Title: Looking at words and points with attention: a benchmark for text-to-shape coherence
• Authors: Andrea Amaduzzi, Giuseppe Lisanti, Samuele Salti, Luigi Di Stefano
• Abstract summary: The evaluation of coherence between generated 3D shapes and input textual descriptions lacks a clear benchmark. We employ large language models to automatically refine descriptions associated with shapes. To validate our approach, we conduct a user study and compare quantitatively our metric with existing ones. The refined dataset, the new metric and a set of text-shape pairs validated by the user study comprise a novel, fine-grained benchmark.
• Score: 17.340484439401894
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While text-conditional 3D object generation and manipulation have seen rapid progress, the evaluation of coherence between generated 3D shapes and input textual descriptions lacks a clear benchmark. The reason is twofold: a) the low quality of the textual descriptions in the only publicly available dataset of text-shape pairs; b) the limited effectiveness of the metrics used to quantitatively assess such coherence. In this paper, we propose a comprehensive solution that addresses both weaknesses. Firstly, we employ large language models to automatically refine textual descriptions associated with shapes. Secondly, we propose a quantitative metric to assess text-to-shape coherence, through cross-attention mechanisms. To validate our approach, we conduct a user study and compare quantitatively our metric with existing ones. The refined dataset, the new metric and a set of text-shape pairs validated by the user study comprise a novel, fine-grained benchmark that we publicly release to foster research on text-to-shape coherence of text-conditioned 3D generative models. Benchmark available at https://cvlab-unibo.github.io/CrossCoherence-Web/.

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