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Recent works learn 3D representation explicitly under text-3D guidance.However, limited text-3D data restricts the vocabulary scale and text controlof generations. Generators may easily fall into a stereotype concept forcertain text prompts, thus losing open-vocabulary generation ability. To tacklethis issue, we introduce a conditional 3D generative model, namely TextField3D.Specifically, rather than using the text prompts as input directly, we suggestto 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 theappropriate range of textual latent space that is expanded by NTFs. To thisend, an NTFGen module is proposed to model general text latent code in noisyfields. Meanwhile, an NTFBind module is proposed to align view-invariant imagelatent code to noisy fields, further supporting image-conditional 3Dgeneration. To guide the conditional generation in both geometry and texture,multi-modal discrimination is constructed with a text-3D discriminator and atext-2.5D discriminator. Compared to previous methods, TextField3D includesthree merits: 1) large vocabulary, 2) text consistency, and 3) low latency.Extensive experiments demonstrate that our method achieves a potentialopen-vocabulary 3D generation capability.

TextField3D: Towards Enhancing Open-Vocabulary 3D Generation with Noisy Text Fields