Large-scale 3D generative models require substantial computational resources yet often fall short in capturing fine details and complex geometries at high resolutions. We attribute this limitation to the inefficiency of current representations, which lack the compactness required to model the generative models effectively. To address this, we introduce a novel approach called Wavelet Latent Diffusion, or WaLa, that encodes 3D shapes into wavelet-based, compact latent encodings. Specifically, we compress a $256^3$ signed distance field into a $12^3 \times 4$ latent grid, achieving an impressive 2427x compression ratio with minimal loss of detail. This high level of compression allows our method to efficiently train large-scale generative networks without increasing the inference time. Our models, both conditional and unconditional, contain approximately one billion parameters and successfully generate high-quality 3D shapes at $256^3$ resolution. Moreover, WaLa offers rapid inference, producing shapes within two to four seconds depending on the condition, despite the model's scale. We demonstrate state-of-the-art performance across multiple datasets, with significant improvements in generation quality, diversity, and computational efficiency. We open-source our code and, to the best of our knowledge, release the largest pretrained 3D generative models across different modalities.

本研究解决了当前大规模三维生成模型在细节和复杂几何形状表现不足的问题。通过提出一种名为小波潜在扩散（WaLa）的方法，将三维形状编码为基于小波的紧凑潜在编码，实现了高达2427倍的压缩比，极大地提高了训练效率，并且生成高质量的三维形状。该方法在多个数据集上展示了最先进的性能，并开源了相关代码，提供了业内最大的预训练三维生成模型。

小波潜在扩散（Wala）：具有紧凑小波编码的十亿参数三维生成模型