title: FlexiDreamer: Single Image-to-3D Generation with FlexiCubes
summary: 3D content generation from text prompts or single images has made remarkable
  progress in quality and speed recently. One of its dominant paradigms involves
  generating consistent multi-view images followed by a sparse-view
  reconstruction. However, due to the challenge of directly deforming the mesh
  representation to approach the target topology, most methodologies learn an
  implicit representation (such as NeRF) during the sparse-view reconstruction
  and acquire the target mesh by a post-processing extraction. Although the
  implicit representation can effectively model rich 3D information, its training
  typically entails a long convergence time. In addition, the post-extraction
  operation from the implicit field also leads to undesirable visual artifacts.
  In this paper, we propose FlexiDreamer, a novel single image-to-3d generation
  framework that reconstructs the target mesh in an end-to-end manner. By
  leveraging a flexible gradient-based extraction known as FlexiCubes, our method
  circumvents the defects brought by the post-processing and facilitates a direct
  acquisition of the target mesh. Furthermore, we incorporate a multi-resolution
  hash grid encoding scheme that progressively activates the encoding levels into
  the implicit field in FlexiCubes to help capture geometric details for per-step
  optimization. Notably, FlexiDreamer recovers a dense 3D structure from a
  single-view image in approximately 1 minute on a single NVIDIA A100 GPU,
  outperforming previous methodologies by a large margin.
paper_authors._id: 660b96033fbd33a1d0f703dc
paper_authors.name: Ruowen Zhao
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-04-02T11:02:58.599Z
paper_authors._id: 6522e4fbd89bc7773ddc4b58
paper_authors.avatarUrl: /avatars/3e9b158af52c5f738a3eae72dcbb3824.svg
paper_authors.fullname: Ruowen Zhao
paper_authors.type: user
paper_authors.user: zzzrw
paper_authors._id: 660b96033fbd33a1d0f703dd
paper_authors.name: Zhengyi Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-04-03T09:27:14.382Z
paper_authors._id: 634e15aec1ce28f1de91c470
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/634e15aec1ce28f1de91c470/MdtJKgE_fbHydhqESr-DH.png
paper_authors.fullname: Zhengyi Wang
paper_authors.type: user
paper_authors.user: Zhengyi
paper_authors._id: 660b96033fbd33a1d0f703de
paper_authors.name: Yikai Wang
paper_authors._id: 660b96033fbd33a1d0f703df
paper_authors.name: Zihan Zhou
paper_authors._id: 660b96033fbd33a1d0f703e0
paper_authors.name: Jun Zhu
paper_title: FlexiDreamer: Single Image-to-3D Generation with FlexiCubes
paper_summary: 3D content generation from text prompts or single images has made remarkable
  progress in quality and speed recently. One of its dominant paradigms involves
  generating consistent multi-view images followed by a sparse-view
  reconstruction. However, due to the challenge of directly deforming the mesh
  representation to approach the target topology, most methodologies learn an
  implicit representation (such as NeRF) during the sparse-view reconstruction
  and acquire the target mesh by a post-processing extraction. Although the
  implicit representation can effectively model rich 3D information, its training
  typically entails a long convergence time. In addition, the post-extraction
  operation from the implicit field also leads to undesirable visual artifacts.
  In this paper, we propose FlexiDreamer, a novel single image-to-3d generation
  framework that reconstructs the target mesh in an end-to-end manner. By
  leveraging a flexible gradient-based extraction known as FlexiCubes, our method
  circumvents the defects brought by the post-processing and facilitates a direct
  acquisition of the target mesh. Furthermore, we incorporate a multi-resolution
  hash grid encoding scheme that progressively activates the encoding levels into
  the implicit field in FlexiCubes to help capture geometric details for per-step
  optimization. Notably, FlexiDreamer recovers a dense 3D structure from a
  single-view image in approximately 1 minute on a single NVIDIA A100 GPU,
  outperforming previous methodologies by a large margin.
paper_ai_summary: FlexiDreamer is a novel end-to-end framework for single-image 3D generation using FlexiCubes and a multi-resolution hash grid encoding scheme to improve efficiency and reduce visual artifacts.
paper_ai_keywords: NeRF implicit representation sparse-view reconstruction gradient-based extraction FlexiCubes multi-resolution hash grid encoding end-to-end generation geometric details per-step optimization dense 3D structure 3D content generation single-image-to-3D generation