Note that Point-E [22] abandons this optimization
paradigm by directly training a diffusion model for 3D point
cloud generation. However, these diffusion models require
a large number of iterative diffusion steps, which inevitably
leads to low efficiency. Besides, it is trained on millions of
private 3D assets, which is not feasible for most researchers.
In this paper, we propose a novel framework, named In-
stant3D, for fast text-to-3D generation. It is able to generate
a realistic 3D object that faithfully aligns with a given text
description, all within a single forward pass of a neural net-
work. Remarkably, this generation process takes less than
one second (bottom of Figure 1). The proposed Instant3D
is a feedforward neural network conditioned on the text
prompt and produces a triplane representation [3] encapsu-
lating the desired 3D object. Similar to DreamFusion [23],
we use the Score Distillation Sampling (SDS) loss to train
our network.
Instant3D