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English(EN) MegaParts: Scaling Part-Aware 3D Object Generation to 300 Parts via Token-Efficient Autoregressive Modeling

MegaParts 使用令牌高效自回归建模将3D对象生成扩展到300个部件

研究人员开发了MegaParts,一个新颖的3D对象生成框架,显著扩展了部件感知建模。通过采用令牌高效的矢量量化部件令牌和具有长上下文训练的结构化自回归序列建模,MegaParts可以处理多达300个部件的对象。与现有的自回归和扩散模型相比,这种方法实现了更高的网格质量,为大规模3D生成任务提供了一个引人注目的替代方案。 AI

影响 能够创建更详细、更可控的3D资产,可能影响游戏、VR和产品设计等领域。

排序理由 该集群包含一篇详细介绍新的3D对象生成方法的论文。

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

MegaParts 使用令牌高效自回归建模将3D对象生成扩展到300个部件

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该集群包含一篇详细介绍新的3D对象生成方法的论文。
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报道来源 [2]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    MegaParts:通过高效的自回归建模将部件感知3D对象生成扩展到300个部件

    MegaParts scales part-aware 3D generation via token-efficient vector-quantized part tokens and structured autoregressive sequence modeling with long-context training.

  2. arXiv cs.CV TIER_1 English(EN) · Manwen Liao, Xinyu Lian, Jian Mao, Kaixu Chen, Li Luo, Jinghao Yan, Wanshui Gan, Qiao Yu, Weitian Zhang, Chunhua Shen, Guang Chen, Bo Dai, Xudong Xu, Zhaoyang Lyu ·

    MegaParts:通过高效率的自回归建模将部件感知 3D 对象生成扩展到 300 个部件

    arXiv:2608.14783v1 Announce Type: new Abstract: Part-aware 3D object generation is essential for graphics applications such as controllable modeling, editing, and articulation, where objects are represented as coherent assemblies of semantic parts. However, existing part-aware ge…