Text2NeRF: Text-Driven 3D Scene Generation with Neural Radiance Fields
Text-driven 3D scene generation is widely applicable to video gaming, film
industry, and metaverse applications that have a large demand for 3D scenes.
However, existing text-to-3D generation methods are limited to producing 3D
objects with simple geometries and dreamlike styles that lack realism. In this
work, we present Text2NeRF, which is able to generate a wide range of 3D scenes
with complicated geometric structures and high-fidelity textures purely from a
text prompt. To this end, we adopt NeRF as the 3D representation and leverage a
pre-trained text-to-image diffusion model to constrain the 3D reconstruction of
the NeRF to reflect the scene description. Specifically, we employ the
diffusion model to infer the text-related image as the content prior and use a
monocular depth estimation method to offer the geometric prior. Both content
and geometric priors are utilized to update the NeRF model. To guarantee
textured and geometric consistency between different views, we introduce a
progressive scene inpainting and updating strategy for novel view synthesis of
the scene. Our method requires no additional training data but only a natural
language description of the scene as the input. Extensive experiments
demonstrate that our Text2NeRF outperforms existing methods in producing
photo-realistic, multi-view consistent, and diverse 3D scenes from a variety of
natural language prompts.
646abeff1556443f24b8d097
Jingbo Zhang
claimed_verified
2023-05-22T11:57:40.766Z
631ff2c8124782a19eff5e2f
/avatars/76e9f74ecf6ed56e4abb89a25387b052.svg
Jingbo Zhang
user
Eckert
646abeff1556443f24b8d098
Xiaoyu Li
admin_assigned
2023-05-26T09:03:01.826Z
637360960938c0754236974e
/avatars/4f47c06ca2f6bf5b6f33c0fba4f54892.svg
Xiaoyu Li
user
Axidy
646abeff1556443f24b8d099
Ziyu Wan
646abeff1556443f24b8d09a
Can Wang
646abeff1556443f24b8d09b
Jing Liao
Text2NeRF: Text-Driven 3D Scene Generation with Neural Radiance Fields
Text-driven 3D scene generation is widely applicable to video gaming, film
industry, and metaverse applications that have a large demand for 3D scenes.
However, existing text-to-3D generation methods are limited to producing 3D
objects with simple geometries and dreamlike styles that lack realism. In this
work, we present Text2NeRF, which is able to generate a wide range of 3D scenes
with complicated geometric structures and high-fidelity textures purely from a
text prompt. To this end, we adopt NeRF as the 3D representation and leverage a
pre-trained text-to-image diffusion model to constrain the 3D reconstruction of
the NeRF to reflect the scene description. Specifically, we employ the
diffusion model to infer the text-related image as the content prior and use a
monocular depth estimation method to offer the geometric prior. Both content
and geometric priors are utilized to update the NeRF model. To guarantee
textured and geometric consistency between different views, we introduce a
progressive scene inpainting and updating strategy for novel view synthesis of
the scene. Our method requires no additional training data but only a natural
language description of the scene as the input. Extensive experiments
demonstrate that our Text2NeRF outperforms existing methods in producing
photo-realistic, multi-view consistent, and diverse 3D scenes from a variety of
natural language prompts.
Text2NeRF generates high-fidelity, photo-realistic 3D scenes from text prompts by integrating NeRF with a text-to-image diffusion model and monocular depth estimation.
NeRF text-to-image diffusion model monocular depth estimation progressive scene inpainting novel view synthesis photo-realistic multi-view consistent