title: GECO: Generative Image-to-3D within a SECOnd
summary: 3D generation has seen remarkable progress in recent years. Existing
  techniques, such as score distillation methods, produce notable results but
  require extensive per-scene optimization, impacting time efficiency.
  Alternatively, reconstruction-based approaches prioritize efficiency but
  compromise quality due to their limited handling of uncertainty. We introduce
  GECO, a novel method for high-quality 3D generative modeling that operates
  within a second. Our approach addresses the prevalent issues of uncertainty and
  inefficiency in current methods through a two-stage approach. In the initial
  stage, we train a single-step multi-view generative model with score
  distillation. Then, a second-stage distillation is applied to address the
  challenge of view inconsistency from the multi-view prediction. This two-stage
  process ensures a balanced approach to 3D generation, optimizing both quality
  and efficiency. Our comprehensive experiments demonstrate that GECO achieves
  high-quality image-to-3D generation with an unprecedented level of efficiency.
paper_authors._id: 6659465c722ca7da96bbf2d4
paper_authors.name: Chen Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-05-31T10:03:16.105Z
paper_authors._id: 62e9d5af6687b60b9c01240a
paper_authors.avatarUrl: /avatars/3983be5662aa28aefcb18deaf08d7cb1.svg
paper_authors.fullname: Chen Wang
paper_authors.type: user
paper_authors.user: chenwang
paper_authors._id: 6659465c722ca7da96bbf2d5
paper_authors.name: Jiatao Gu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-05-31T10:03:46.273Z
paper_authors._id: 6164e72d73996c363c52e66d
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1634002684894-noauth.png
paper_authors.fullname: Jiatao Gu
paper_authors.type: user
paper_authors.user: thomagram
paper_authors._id: 6659465c722ca7da96bbf2d6
paper_authors.name: Xiaoxiao Long
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-05-31T10:03:53.344Z
paper_authors._id: 648ebb653c8611c2ba1dbede
paper_authors.avatarUrl: /avatars/dddaf7ae8d76e733b30d9952563ea22b.svg
paper_authors.fullname: xiaoxiaolongli
paper_authors.type: user
paper_authors.user: xiaoxiaolong
paper_authors._id: 6659465c722ca7da96bbf2d7
paper_authors.name: Yuan Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-06-04T08:35:20.561Z
paper_authors._id: 63f5dc824b831cc179b90e8f
paper_authors.avatarUrl: /avatars/2c2d22c6b605a9c412a9c37960efeeb2.svg
paper_authors.fullname: Yuan Liu
paper_authors.type: user
paper_authors.user: liuyuan-pal
paper_authors._id: 6659465c722ca7da96bbf2d8
paper_authors.name: Lingjie Liu
paper_title: GECO: Generative Image-to-3D within a SECOnd
paper_summary: 3D generation has seen remarkable progress in recent years. Existing
  techniques, such as score distillation methods, produce notable results but
  require extensive per-scene optimization, impacting time efficiency.
  Alternatively, reconstruction-based approaches prioritize efficiency but
  compromise quality due to their limited handling of uncertainty. We introduce
  GECO, a novel method for high-quality 3D generative modeling that operates
  within a second. Our approach addresses the prevalent issues of uncertainty and
  inefficiency in current methods through a two-stage approach. In the initial
  stage, we train a single-step multi-view generative model with score
  distillation. Then, a second-stage distillation is applied to address the
  challenge of view inconsistency from the multi-view prediction. This two-stage
  process ensures a balanced approach to 3D generation, optimizing both quality
  and efficiency. Our comprehensive experiments demonstrate that GECO achieves
  high-quality image-to-3D generation with an unprecedented level of efficiency.
paper_ai_keywords: score tracktracking reconstruction-based approaches GECO high-quality 3D generative modeling score distillation view inconsistency multi-view generative model multi-view prediction two-stage approach image-to-3D generation