Conditional 3D generation is undergoing a significant advancement, enabling the free creation of 3D content from inputs such as text or 2D images. However, previous approaches have suffered from low inference efficiency, limited generation categories, and restricted downstream applications. In this work, we revisit the impact of different 3D representations on generation quality and efficiency. We propose a progressive generation method through Voxel-Point Progressive Representation (VPP). VPP leverages structured voxel representation in the proposed Voxel Semantic Generator and the sparsity of unstructured point representation in the Point Upsampler, enabling efficient generation of multi-category objects. VPP can generate high-quality 8K point clouds within 0.2 seconds. Additionally, the masked generation Transformer allows for various 3D downstream tasks, such as generation, editing, completion, and pre-training. Extensive experiments demonstrate that VPP efficiently generates high-fidelity and diverse 3D shapes across different categories, while also exhibiting excellent representation transfer performance. Codes will be released at \url{https://github.com/qizekun/VPP}.

翻译：条件3D生成正经历显著进展，能够根据文本或2D图像等输入自由创建3D内容。然而，先前方法存在推理效率低、生成类别有限以及下游应用受限的问题。本研究重新审视了不同3D表示对生成质量与效率的影响，提出一种通过体素-点渐进表示（VPP）实现的渐进生成方法。VPP在提出的体素语义生成器中利用结构化体素表示，并在点上采样器中利用非结构化点表示的稀疏性，从而高效生成多类别物体。VPP可在0.2秒内生成高质量的8K点云。此外，掩码生成Transformer支持多种3D下游任务，如生成、编辑、补全和预训练。大量实验表明，VPP能够高效生成跨类别的高保真、多样化3D形状，同时展现出优异的表示迁移性能。代码将在\url{https://github.com/qizekun/VPP} 开源。