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.

翻译：三维内容创建在质量和速度方面均已取得显著进展。尽管当前的前馈模型可在数秒内生成三维物体，但其分辨率受限于训练过程中所需的高强度计算。本文提出大型多视图高斯模型（LGM），这是一种从文本提示或单视图图像生成高分辨率三维模型的新型框架。我们的核心见解包含两点：1）三维表示：提出多视图高斯特征作为高效且强大的表示方法，可融合后用于可微渲染。2）三维骨干网络：提出非对称U-Net作为高吞吐量骨干网络，作用于多视图图像，可通过利用多视图扩散模型从文本或单视图图像输入生成。大量实验证明了该方法的高保真度与高效性。值得注意的是，我们在保持5秒内快速生成三维物体的同时，将训练分辨率提升至512，从而实现了高分辨率三维内容生成。