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

Autor:	Han, Bo, Fu, Yitong, Shen, Yixuan
Rok vydání:	2023
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Multimedia
Druh dokumentu:	Working Paper
Popis:	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. Comment: work in progress
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2301.13591 Zobrazit plný text záznamu View this record from Arxiv