Instant3D: Instant Text-to-3D Generation
Text-to-3D generation, which aims to synthesize vivid 3D objects from text
prompts, has attracted much attention from the computer vision community. While
several existing works have achieved impressive results for this task, they
mainly rely on a time-consuming optimization paradigm. Specifically, these
methods optimize a neural field from scratch for each text prompt, taking
approximately one hour or more to generate one object. This heavy and
repetitive training cost impedes their practical deployment. In this paper, we
propose a novel framework for fast text-to-3D generation, dubbed Instant3D.
Once trained, Instant3D is able to create a 3D object for an unseen text prompt
in less than one second with a single run of a feedforward network. We achieve
this remarkable speed by devising a new network that directly constructs a 3D
triplane from a text prompt. The core innovation of our Instant3D lies in our
exploration of strategies to effectively inject text conditions into the
network. Furthermore, we propose a simple yet effective activation function,
the scaled-sigmoid, to replace the original sigmoid function, which speeds up
the training convergence by more than ten times. Finally, to address the Janus
(multi-head) problem in 3D generation, we propose an adaptive Perp-Neg
algorithm that can dynamically adjust its concept negation scales according to
the severity of the Janus problem during training, effectively reducing the
multi-head effect. Extensive experiments on a wide variety of benchmark
datasets demonstrate that the proposed algorithm performs favorably against the
state-of-the-art methods both qualitatively and quantitatively, while achieving
significantly better efficiency. The project page is at
https://ming1993li.github.io/Instant3DProj.
6554445c39bce75e6cc81fba
Ming Li
claimed_verified
2023-11-15T17:40:59.279Z
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ming li
user
Frankboy
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Pan Zhou
admin_assigned
2023-11-15T09:45:32.094Z
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zhou pan
user
zhoupans
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Jia-Wei Liu
admin_assigned
2023-11-15T09:46:01.082Z
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Jiawei Liu
user
JiaweiLIU
6554445c39bce75e6cc81fbd
Jussi Keppo
6554445c39bce75e6cc81fbe
Min Lin
6554445c39bce75e6cc81fbf
Shuicheng Yan
6554445c39bce75e6cc81fc0
Xiangyu Xu
admin_assigned
2023-11-15T09:47:13.384Z
634e60454677a5891c0902f4
/avatars/4dc143719afe7686e05b7f2c2c5c1871.svg
Xiangyu Xu
user
xjcvcvxj
Instant3D: Instant Text-to-3D Generation
Text-to-3D generation, which aims to synthesize vivid 3D objects from text
prompts, has attracted much attention from the computer vision community. While
several existing works have achieved impressive results for this task, they
mainly rely on a time-consuming optimization paradigm. Specifically, these
methods optimize a neural field from scratch for each text prompt, taking
approximately one hour or more to generate one object. This heavy and
repetitive training cost impedes their practical deployment. In this paper, we
propose a novel framework for fast text-to-3D generation, dubbed Instant3D.
Once trained, Instant3D is able to create a 3D object for an unseen text prompt
in less than one second with a single run of a feedforward network. We achieve
this remarkable speed by devising a new network that directly constructs a 3D
triplane from a text prompt. The core innovation of our Instant3D lies in our
exploration of strategies to effectively inject text conditions into the
network. Furthermore, we propose a simple yet effective activation function,
the scaled-sigmoid, to replace the original sigmoid function, which speeds up
the training convergence by more than ten times. Finally, to address the Janus
(multi-head) problem in 3D generation, we propose an adaptive Perp-Neg
algorithm that can dynamically adjust its concept negation scales according to
the severity of the Janus problem during training, effectively reducing the
multi-head effect. Extensive experiments on a wide variety of benchmark
datasets demonstrate that the proposed algorithm performs favorably against the
state-of-the-art methods both qualitatively and quantitatively, while achieving
significantly better efficiency. The project page is at
https://ming1993li.github.io/Instant3DProj.
A framework named Instant3D generates 3D objects from text prompts in under one second using a novel network and adaptive algorithms to enhance efficiency and quality.
text-to-3D generation neural field Instant3D feedforward network triplane activation function scaled-sigmoid adaptive Perp-Neg algorithm benchmark datasets