Recently, 3D Gaussian splatting (3D-GS) has achieved great success in reconstructing and rendering real-world scenes. To transfer the high rendering quality to generation tasks, a series of research works attempt to generate 3D-Gaussian assets from text. However, the generated assets have not achieved the same quality as those in reconstruction tasks. We observe that Gaussians tend to grow without control as the generation process may cause indeterminacy. Aiming at highly enhancing the generation quality, we propose a novel framework named GaussianDreamerPro. The main idea is to bind Gaussians to reasonable geometry, which evolves over the whole generation process. Along different stages of our framework, both the geometry and appearance can be enriched progressively. The final output asset is constructed with 3D Gaussians bound to mesh, which shows significantly enhanced details and quality compared with previous methods. Notably, the generated asset can also be seamlessly integrated into downstream manipulation pipelines, e.g. animation, composition, and simulation etc., greatly promoting its potential in wide applications.
最近，3D高斯散射（3D-GS）在重建和渲染真实世界场景方面取得了巨大成功。为了将高质量渲染转移到生成任务中，一系列研究工作尝试从文本生成3D高斯资产。然而，生成的资产并未达到重建任务中的相同质量。我们观察到，在生成过程中，由于可能引起的不确定性，高斯的增长往往无法控制。为了极大地提高生成质量，我们提出了一个名为GaussianDreamerPro的新框架。主要思想是将高斯绑定到整个生成过程中逐步演化的合理几何结构上。在我们框架的不同阶段，几何形状和外观都可以逐步丰富。最终输出的资产是与网格绑定的3D高斯构建的，与以前的方法相比，细节和质量显著提高。值得注意的是，生成的资产还可以无缝地集成到下游操作流程中，例如动画、合成和模拟等，极大地推动了其在广泛应用中的潜力。