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.

通过引入大型多视图高斯模型（LGM），这篇论文提出了一种从文本提示或单视图图像生成高分辨率 3D 模型的新框架，其中包含多视图高斯特征作为有效且强大的表示，并利用多视图扩散模型实现高吞吐量骨干网络的不对称 U-Net。大量实验证明了该方法的高保真度和高效性，同时在 5 秒内生成 3D 对象的速度快且训练分辨率达到 512，从而实现了高分辨率 3D 内容生成。