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This paper explores promptable NeRF generation (e.g., text prompt or singleimage prompt) for direct conditioning and fast generation of NeRF parametersfor the underlying 3D scenes, thus undoing complex intermediate steps whileproviding full 3D generation with conditional control. Unlike previousdiffusion-CLIP-based pipelines that involve tedious per-prompt optimizations,Prompt2NeRF-PIL is capable of generating a variety of 3D objects with a singleforward pass, leveraging a pre-trained implicit latent space of NeRFparameters. Furthermore, in zero-shot tasks, our experiments demonstrate thatthe NeRFs produced by our method serve as semantically informativeinitializations, significantly accelerating the inference process of existingprompt-to-NeRF methods. Specifically, we will show that our approach speeds upthe text-to-NeRF model DreamFusion and the 3D reconstruction speed of theimage-to-NeRF method Zero-1-to-3 by 3 to 5 times.

Prompt2NeRF-PIL: Fast NeRF Generation via Pretrained Implicit Latent