title: LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content
    Creation
summary: 3D content creation has achieved significant progress in terms of both
  quality and speed. Although current feed-forward models can produce 3D objects
  in seconds, their resolution is constrained by the intensive computation
  required during training. In this paper, we introduce Large Multi-View Gaussian
  Model (LGM), a novel framework designed to generate high-resolution 3D models
  from text prompts or single-view images. Our key insights are two-fold: 1) 3D
  Representation: We propose multi-view Gaussian features as an efficient yet
  powerful representation, which can then be fused together for differentiable
  rendering. 2) 3D Backbone: We present an asymmetric U-Net as a high-throughput
  backbone operating on multi-view images, which can be produced from text or
  single-view image input by leveraging multi-view diffusion models. Extensive
  experiments demonstrate the high fidelity and efficiency of our approach.
  Notably, we maintain the fast speed to generate 3D objects within 5 seconds
  while boosting the training resolution to 512, thereby achieving
  high-resolution 3D content generation.
paper_authors._id: 65c435eec1320bb258b4dad8
paper_authors.name: Jiaxiang Tang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-08T11:25:24.312Z
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paper_authors.fullname: kiui
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paper_authors.user: ashawkey
paper_authors._id: 65c435eec1320bb258b4dad9
paper_authors.name: Zhaoxi Chen
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paper_authors.avatarUrl: /avatars/6c5dda9e58747054a989f077a078f3dc.svg
paper_authors.fullname: Zhaoxi Chen
paper_authors.type: user
paper_authors.user: FrozenBurning
paper_authors._id: 65c435eec1320bb258b4dada
paper_authors.name: Xiaokang Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-05T15:57:17.694Z
paper_authors._id: 6635e701420baf7bc3f93561
paper_authors.avatarUrl: /avatars/cdbb3085fee73ac520888977e2c575ea.svg
paper_authors.fullname: Xiaokang Chen
paper_authors.type: user
paper_authors.user: CharlesCXK
paper_authors._id: 65c435eec1320bb258b4dadb
paper_authors.name: Tengfei Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-08T11:24:15.213Z
paper_authors._id: 62e7c26236a8e8a827ff0891
paper_authors.avatarUrl: /avatars/15648407233bf5757262dbc04464d02e.svg
paper_authors.fullname: Tengfei Wang
paper_authors.type: user
paper_authors.user: tfwang
paper_authors._id: 65c435eec1320bb258b4dadc
paper_authors.name: Gang Zeng
paper_authors._id: 65c435eec1320bb258b4dadd
paper_authors.name: Ziwei Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-08T11:24:30.452Z
paper_authors._id: 62ab1ac1d48b4d8b048a3473
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1656826685333-62ab1ac1d48b4d8b048a3473.png
paper_authors.fullname: Ziwei Liu
paper_authors.type: user
paper_authors.user: liuziwei7
paper_title: LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content
    Creation
paper_summary: 3D content creation has achieved significant progress in terms of both
  quality and speed. Although current feed-forward models can produce 3D objects
  in seconds, their resolution is constrained by the intensive computation
  required during training. In this paper, we introduce Large Multi-View Gaussian
  Model (LGM), a novel framework designed to generate high-resolution 3D models
  from text prompts or single-view images. Our key insights are two-fold: 1) 3D
  Representation: We propose multi-view Gaussian features as an efficient yet
  powerful representation, which can then be fused together for differentiable
  rendering. 2) 3D Backbone: We present an asymmetric U-Net as a high-throughput
  backbone operating on multi-view images, which can be produced from text or
  single-view image input by leveraging multi-view diffusion models. Extensive
  experiments demonstrate the high fidelity and efficiency of our approach.
  Notably, we maintain the fast speed to generate 3D objects within 5 seconds
  while boosting the training resolution to 512, thereby achieving
  high-resolution 3D content generation.
paper_ai_summary: A novel Large Multi-View Gaussian Model (LGM) generates high-resolution 3D models from text or a single image using multi-view Gaussian features and an asymmetric U-Net backbone, achieved with a multi-view diffusion model.
paper_ai_keywords: Large Multi-View Gaussian Model LGM multi-view Gaussian features differentiable rendering asymmetric U-Net multi-view diffusion models