  We present a novel alignment-before-generation approach to tackle the
challenging task of generating general 3D shapes based on 2D images or texts.
Directly learning a conditional generative model from images or texts to 3D
shapes is prone to producing inconsistent results with the conditions because
3D shapes have an additional dimension whose distribution significantly differs
from that of 2D images and texts. To bridge the domain gap among the three
modalities and facilitate multi-modal-conditioned 3D shape generation, we
explore representing 3D shapes in a shape-image-text-aligned space. Our
framework comprises two models: a Shape-Image-Text-Aligned Variational
Auto-Encoder (SITA-VAE) and a conditional Aligned Shape Latent Diffusion Model
(ASLDM). The former model encodes the 3D shapes into the shape latent space
aligned to the image and text and reconstructs the fine-grained 3D neural
fields corresponding to given shape embeddings via the transformer-based
decoder. The latter model learns a probabilistic mapping function from the
image or text space to the latent shape space. Our extensive experiments
demonstrate that our proposed approach can generate higher-quality and more
diverse 3D shapes that better semantically conform to the visual or textural
conditional inputs, validating the effectiveness of the
shape-image-text-aligned space for cross-modality 3D shape generation.
