  With the rising industrial attention to 3D virtual modeling technology,
generating novel 3D content based on specified conditions (e.g. text) has
become a hot issue. In this paper, we propose a new generative 3D modeling
framework called Diffusion-SDF for the challenging task of text-to-shape
synthesis. Previous approaches lack flexibility in both 3D data representation
and shape generation, thereby failing to generate highly diversified 3D shapes
conforming to the given text descriptions. To address this, we propose a SDF
autoencoder together with the Voxelized Diffusion model to learn and generate
representations for voxelized signed distance fields (SDFs) of 3D shapes.
Specifically, we design a novel UinU-Net architecture that implants a
local-focused inner network inside the standard U-Net architecture, which
enables better reconstruction of patch-independent SDF representations. We
extend our approach to further text-to-shape tasks including text-conditioned
shape completion and manipulation. Experimental results show that Diffusion-SDF
generates both higher quality and more diversified 3D shapes that conform well
to given text descriptions when compared to previous approaches. Code is
available at: https://github.com/ttlmh/Diffusion-SDF
