  Recent works learn 3D representation explicitly under text-3D guidance.
However, limited text-3D data restricts the vocabulary scale and text control
of generations. Generators may easily fall into a stereotype concept for
certain text prompts, thus losing open-vocabulary generation ability. To tackle
this issue, we introduce a conditional 3D generative model, namely TextField3D.
Specifically, rather than using the text prompts as input directly, we suggest
to inject dynamic noise into the latent space of given text prompts, i.e.,
Noisy Text Fields (NTFs). In this way, limited 3D data can be mapped to the
appropriate range of textual latent space that is expanded by NTFs. To this
end, an NTFGen module is proposed to model general text latent code in noisy
fields. Meanwhile, an NTFBind module is proposed to align view-invariant image
latent code to noisy fields, further supporting image-conditional 3D
generation. To guide the conditional generation in both geometry and texture,
multi-modal discrimination is constructed with a text-3D discriminator and a
text-2.5D discriminator. Compared to previous methods, TextField3D includes
three merits: 1) large vocabulary, 2) text consistency, and 3) low latency.
Extensive experiments demonstrate that our method achieves a potential
open-vocabulary 3D generation capability.
