LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content
  Creation
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.
65c435eec1320bb258b4dad8
Jiaxiang Tang
admin_assigned
2024-02-08T11:25:24.312Z
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kiui
user
ashawkey
65c435eec1320bb258b4dad9
Zhaoxi Chen
admin_assigned
2024-02-08T11:22:59.633Z
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Zhaoxi Chen
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FrozenBurning
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Xiaokang Chen
claimed_verified
2025-02-05T15:57:17.694Z
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Xiaokang Chen
user
CharlesCXK
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Tengfei Wang
admin_assigned
2024-02-08T11:24:15.213Z
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Tengfei Wang
user
tfwang
65c435eec1320bb258b4dadc
Gang Zeng
65c435eec1320bb258b4dadd
Ziwei Liu
admin_assigned
2024-02-08T11:24:30.452Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/1656826685333-62ab1ac1d48b4d8b048a3473.png
Ziwei Liu
user
liuziwei7
LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content
  Creation
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.
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.
Large Multi-View Gaussian Model LGM multi-view Gaussian features differentiable rendering asymmetric U-Net multi-view diffusion models