title: Instant3D: Instant Text-to-3D Generation
summary: 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.
paper_authors._id: 6554445c39bce75e6cc81fba
paper_authors.name: Ming Li
paper_authors.status: claimed_verified
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paper_authors.fullname: ming li
paper_authors.type: user
paper_authors.user: Frankboy
paper_authors._id: 6554445c39bce75e6cc81fbb
paper_authors.name: Pan Zhou
paper_authors.status: admin_assigned
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paper_authors.fullname: zhou pan
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paper_authors.user: zhoupans
paper_authors._id: 6554445c39bce75e6cc81fbc
paper_authors.name: Jia-Wei Liu
paper_authors.status: admin_assigned
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paper_authors._id: 64230048a73327caad9d8241
paper_authors.avatarUrl: /avatars/ac115f1a21c743db7c925f0e18451145.svg
paper_authors.fullname: Jiawei Liu
paper_authors.type: user
paper_authors.user: JiaweiLIU
paper_authors._id: 6554445c39bce75e6cc81fbd
paper_authors.name: Jussi Keppo
paper_authors._id: 6554445c39bce75e6cc81fbe
paper_authors.name: Min Lin
paper_authors._id: 6554445c39bce75e6cc81fbf
paper_authors.name: Shuicheng Yan
paper_authors._id: 6554445c39bce75e6cc81fc0
paper_authors.name: Xiangyu Xu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-15T09:47:13.384Z
paper_authors._id: 634e60454677a5891c0902f4
paper_authors.avatarUrl: /avatars/4dc143719afe7686e05b7f2c2c5c1871.svg
paper_authors.fullname: Xiangyu Xu
paper_authors.type: user
paper_authors.user: xjcvcvxj
paper_title: Instant3D: Instant Text-to-3D Generation
paper_summary: 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.
paper_ai_keywords: neural field feedforward network 3D triplane text conditions scaled-sigmoid activation function Perp-Neg algorithm concept negation scales Janus problem multi-head problem