3D-WAG：分层小波引导自回归生成用于高保真的3D形状
3D-WAG: Hierarchical Wavelet-Guided Autoregressive Generation for High-Fidelity 3D Shapes
AI总结：
3D-WAG通过分层小波引导自回归生成技术，实现高效且高质量的3D形状生成，优于现有方法。
摘要：
自回归（AR）模型在自然语言和图像生成中取得了显著成功，但其在3D形状建模中的应用仍鲜有探索。与扩散模型不同，AR模型能够更高效、可控地生成内容，且推理时间更短，使其特别适合数据密集型领域。传统的基于AR方法的3D生成模型通常依赖于体素或点级别的“下一个token”预测。虽然在某些应用中有效，但处理大规模3D数据时这些方法会变得限制性且计算成本高。为解决这些问题，我们引入了3D-WAG，一种用于3D隐式距离场的AR模型，能够进行无条件形状生成、类别条件和文本条件的形状生成。我们的核心思想是将形状编码为多尺度小波标记图，并使用Transformer预测“下一个更高分辨率标记图”以自回归方式。通过将3D AR生成任务重新定义为“下一个尺度”预测，我们减少了生成的计算成本，相比传统的“下一个token”预测模型，同时以更结构化和分层的方式保留了3D形状的基本几何细节。我们通过定量和定性比较，将3D-WAG与最先进的方法在广泛使用的基准上进行评估。我们的结果表明，3D-WAG在关键指标如覆盖率和MMD上实现了优越性能，生成的高保真3D形状紧密匹配真实数据分布。
英文摘要：
Autoregressive (AR) models have achieved remarkable success in natural language and image generation, but their application to 3D shape modeling remains largely unexplored. Unlike diffusion models, AR models enable more efficient and controllable generation with faster inference times, making them especially suitable for data-intensive domains. Traditional 3D generative models using AR approaches often rely on ``next-token" predictions at the voxel or point level. While effective for certain applications, these methods can be restrictive and computationally expensive when dealing with large-scale 3D data. To tackle these challenges, we introduce 3D-WAG, an AR model for 3D implicit distance fields that can perform unconditional shape generation, class-conditioned and also text-conditioned shape generation. Our key idea is to encode shapes as multi-scale wavelet token maps and use a Transformer to predict the ``next higher-resolution token map" in an autoregressive manner. By redefining 3D AR generation task as ``next-scale" prediction, we reduce the computational cost of generation compared to traditional ``next-token" prediction models, while preserving essential geometric details of 3D shapes in a more structured and hierarchical manner. We evaluate 3D-WAG to showcase its benefit by quantitative and qualitative comparisons with state-of-the-art methods on widely used benchmarks. Our results show 3D-WAG achieves superior performance in key metrics like Coverage and MMD, generating high-fidelity 3D shapes that closely match the real data distribution.
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。
