CG3D：基于高斯泼溅的文本到3D组合式生成
CG3D: Compositional Generation for Text-to-3D via Gaussian Splatting
AI总结：
CG3D利用可组合的高斯辐射场表示，解决了文本到3D生成中多物体场景、文本控制组合和物理真实性问题，在物体组合与物理精度上超越扩散模型。
摘要：
随着基于扩散的生成模型的出现及其生成文本条件图像的能力，内容生成得到了极大的推动。最近，这些模型已被证明能为3D图形资产的生成提供有用的指导。然而，现有的文本条件3D生成工作面临根本性约束：（i）无法生成详细的多物体场景，（ii）无法对多物体配置进行文本控制，以及（iii）物理上真实的场景组合。在这项工作中，我们提出了CG3D，一种组合式生成可扩展3D资产的方法，解决了这些约束。我们发现，显式高斯辐射场经过参数化以允许物体组合，具备实现语义和物理一致场景的能力。通过利用围绕这种显式表示构建的指导框架，我们展示了最先进的结果，在物体组合和物理精度方面甚至能够超越指导扩散模型。
英文摘要：
With the onset of diffusion-based generative models and their ability to generate text-conditioned images, content generation has received a massive invigoration. Recently, these models have been shown to provide useful guidance for the generation of 3D graphics assets. However, existing work in text-conditioned 3D generation faces fundamental constraints: (i) inability to generate detailed, multi-object scenes, (ii) inability to textually control multi-object configurations, and (iii) physically realistic scene composition. In this work, we propose CG3D, a method for compositionally generating scalable 3D assets that resolves these constraints. We find that explicit Gaussian radiance fields, parameterized to allow for compositions of objects, possess the capability to enable semantically and physically consistent scenes. By utilizing a guidance framework built around this explicit representation, we show state of the art results, capable of even exceeding the guiding diffusion model in terms of object combinations and physics accuracy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
