  Text-guided image generation has progressed rapidly in recent years,
inspiring major breakthroughs in text-guided shape generation. Recently, it has
been shown that using score distillation, one can successfully text-guide a
NeRF model to generate a 3D object. We adapt the score distillation to the
publicly available, and computationally efficient, Latent Diffusion Models,
which apply the entire diffusion process in a compact latent space of a
pretrained autoencoder. As NeRFs operate in image space, a naive solution for
guiding them with latent score distillation would require encoding to the
latent space at each guidance step. Instead, we propose to bring the NeRF to
the latent space, resulting in a Latent-NeRF. Analyzing our Latent-NeRF, we
show that while Text-to-3D models can generate impressive results, they are
inherently unconstrained and may lack the ability to guide or enforce a
specific 3D structure. To assist and direct the 3D generation, we propose to
guide our Latent-NeRF using a Sketch-Shape: an abstract geometry that defines
the coarse structure of the desired object. Then, we present means to integrate
such a constraint directly into a Latent-NeRF. This unique combination of text
and shape guidance allows for increased control over the generation process. We
also show that latent score distillation can be successfully applied directly
on 3D meshes. This allows for generating high-quality textures on a given
geometry. Our experiments validate the power of our different forms of guidance
and the efficiency of using latent rendering. Implementation is available at
https://github.com/eladrich/latent-nerf
