ARMO：多类别物体的自回归绑骨
ARMO: Autoregressive Rigging for Multi-Category Objects
AI总结：
针对现有3D生成忽视动态物体绑骨的问题，提出ARMO框架，利用自回归模型统一预测关节位置与连接，基于新数据集OmniRig实现最先进的骨骼预测性能。
摘要：
大规模生成模型的最新进展显著提升了3D形状生成的质量和多样性。然而，现有大多数方法主要集中于生成静态3D模型，忽视了某些形状（如人形、动物和昆虫）可能具有的动态特性。为解决这一空白，我们聚焦于绑骨（rigging）这一动画中的基础任务，该任务为3D模型建立骨骼结构和蒙皮。在本文中，我们引入了OmniRig，这是首个大规模绑骨数据集，包含79,499个带有详细骨骼和蒙皮信息的网格。与依赖预定义标准姿势（如A-pose、T-pose）的传统基准不同，我们的数据集涵盖了多样的形状类别、风格和姿势。利用这一丰富的数据集，我们提出了ARMO，一种新颖的绑骨框架，该框架利用自回归模型以统一方式预测关节位置和连接关系。通过将骨骼结构视为完整图并离散化为令牌，我们使用自编码器对关节进行编码以获得潜在嵌入，并使用自回归模型预测令牌。采用网格条件潜在扩散模型来预测潜在嵌入，以进行条件骨骼生成。我们的方法解决了基于回归的方法的局限性，这些方法通常遭受误差累积和次优连接估计的问题。通过在OmniRig数据集上的大量实验，我们的方法在骨骼预测中达到了最先进的性能，展示了跨多样物体类别的改进泛化能力。代码和数据集将在录用后公开供学术使用。
英文摘要：
Recent advancements in large-scale generative models have significantly improved the quality and diversity of 3D shape generation. However, most existing methods focus primarily on generating static 3D models, overlooking the potentially dynamic nature of certain shapes, such as humanoids, animals, and insects. To address this gap, we focus on rigging, a fundamental task in animation that establishes skeletal structures and skinning for 3D models. In this paper, we introduce OmniRig, the first large-scale rigging dataset, comprising 79,499 meshes with detailed skeleton and skinning information. Unlike traditional benchmarks that rely on predefined standard poses (e.g., A-pose, T-pose), our dataset embraces diverse shape categories, styles, and poses. Leveraging this rich dataset, we propose ARMO, a novel rigging framework that utilizes an autoregressive model to predict both joint positions and connectivity relationships in a unified manner. By treating the skeletal structure as a complete graph and discretizing it into tokens, we encode the joints using an auto-encoder to obtain a latent embedding and an autoregressive model to predict the tokens. A mesh-conditioned latent diffusion model is used to predict the latent embedding for conditional skeleton generation. Our method addresses the limitations of regression-based approaches, which often suffer from error accumulation and suboptimal connectivity estimation. Through extensive experiments on the OmniRig dataset, our approach achieves state-of-the-art performance in skeleton prediction, demonstrating improved generalization across diverse object categories. The code and dataset will be made public for academic use upon acceptance.
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。
