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English(EN) Rethinking 3D Shape Generation: Diffusion over Superquadrics

新的扩散方法使用紧凑型超二次曲面生成3D形状

研究人员开发了一种新的3D形状生成方法,通过在超二次曲面参数上进行扩散,而不是使用密集几何表示。这种方法显著降低了扩散状态的维度,每个形状仅需要7KB的参数。超二次曲面上的扩散方法能够实现更快的生成速度、提高可扩展性,并支持部件级编辑和基于约束的设计等高级功能,同时在标准基准测试中取得了具有竞争力的性能。 AI

影响 实现了更高效、可控的3D形状生成,可能影响需要快速资产创建的领域。

排序理由 该集群包含一篇详细介绍新颖3D形状生成方法的学术论文。

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的扩散方法使用紧凑型超二次曲面生成3D形状

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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Zhiyang Liu, Wanze Li, Yuwei Wu, Chengran Yuan, Jiawei Sun, Rui Zheng, Marcelo H Ang Jr ·

    重新思考三维形状生成:超二次曲面上的扩散模型

    arXiv:2606.08957v1 Announce Type: new Abstract: Diffusion models have advanced 3D shape generation, yet most methods still denoise in high-cardinality spaces (e.g., voxel/SDF grids, meshes, or point clouds), which is computationally and memory intensive and makes it difficult to …