Zero3D: Semantic-Driven Multi-Category 3D Shape Generation

• URL: http://arxiv.org/abs/2301.13591v5
• Date: Tue, 14 Nov 2023 03:53:55 GMT
• Title: Zero3D: Semantic-Driven Multi-Category 3D Shape Generation
• Authors: Bo Han, Yitong Fu, Yixuan Shen
• Abstract summary: Previous works face problems with single-category generation, low-frequency 3D details, and requiring a large number of paired datasets for training. To tackle these challenges, we propose a multi-category conditional diffusion model. We employ the hidden-layer diffusion model conditioned on the multi-category shape vector, which greatly reduces the training time and memory consumption.
• Score: 15.665757749429702
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semantic-driven 3D shape generation aims to generate 3D objects conditioned on text. Previous works face problems with single-category generation, low-frequency 3D details, and requiring a large number of paired datasets for training. To tackle these challenges, we propose a multi-category conditional diffusion model. Specifically, 1) to alleviate the problem of lack of large-scale paired data, we bridge the text, 2D image and 3D shape based on the pre-trained CLIP model, and 2) to obtain the multi-category 3D shape feature, we apply the conditional flow model to generate 3D shape vector conditioned on CLIP embedding. 3) to generate multi-category 3D shape, we employ the hidden-layer diffusion model conditioned on the multi-category shape vector, which greatly reduces the training time and memory consumption.

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