BrightDreamer: Generic 3D Gaussian Generative Framework for Fast
  Text-to-3D Synthesis
Lutao Jiang Lin Wang
Text-to-3D synthesis has recently seen intriguing advances by combining the
text-to-image models with 3D representation methods, e.g., Gaussian Splatting
(GS), via Score Distillation Sampling (SDS). However, a hurdle of existing
methods is the low efficiency, per-prompt optimization for a single 3D object.
Therefore, it is imperative for a paradigm shift from per-prompt optimization
to one-stage generation for any unseen text prompts, which yet remains
challenging. A hurdle is how to directly generate a set of millions of 3D
Gaussians to represent a 3D object. This paper presents BrightDreamer, an
end-to-end single-stage approach that can achieve generalizable and fast (77
ms) text-to-3D generation. Our key idea is to formulate the generation process
as estimating the 3D deformation from an anchor shape with predefined
positions. For this, we first propose a Text-guided Shape Deformation (TSD)
network to predict the deformed shape and its new positions, used as the
centers (one attribute) of 3D Gaussians. To estimate the other four attributes
(i.e., scaling, rotation, opacity, and SH coefficient), we then design a novel
Text-guided Triplane Generator (TTG) to generate a triplane representation for
a 3D object. The center of each Gaussian enables us to transform the triplane
feature into the four attributes. The generated 3D Gaussians can be finally
rendered at 705 frames per second. Extensive experiments demonstrate the
superiority of our method over existing methods. Also, BrightDreamer possesses
a strong semantic understanding capability even for complex text prompts. The
project code is available at https://vlislab22.github.io/BrightDreamer.