无需专家知识，自动学习3D物体变形的低维结构并实现直观控制。
Category-Agnostic Neural Object Rigging
- 用稀疏空间块和实例感知特征体表示物体，解耦姿态与实例信息。
- 在多个物体类别上实现直观姿态操控，保留丰富个体特征。
- 适合需要自动化、通用化3D动画控制的研究者与开发者。
可变形4D物体的运动存在于低维流形中。为更好捕捉这种低维特性并提升可控性，传统方法依赖基于经验的手动绑定（rigging）技术，但此类方法因需特定类别的专业知识而难以扩展。本文提出一种纯数据驱动的方法，自动探索这类低维结构。设计了一种新表示：将可变形4D物体编码为稀疏的空间块集合与实例感知的特征体积，从而解耦3D形状的姿态与实例信息。通过调整空间块参数，可直观操控物体姿态，同时保持丰富的个体特异性信息。在多种物体类别上进行评估，验证了该框架的有效性。
原文摘要 · Abstract (English)
The motion of deformable 4D objects lies in a low-dimensional manifold. To better capture the low dimensionality and enable better controllability, traditional methods have devised several heuristic-based methods, i.e., rigging, for manipulating dynamic objects in an intuitive fashion. However, such representations are not scalable due to the need for expert knowledge of specific categories. Instead, we study the automatic exploration of such low-dimensional structures in a purely data-driven manner. Specifically, we design a novel representation that encodes deformable 4D objects into a sparse set of spatially grounded blobs and an instance-aware feature volume to disentangle the pose and instance information of the 3D shape. With such a representation, we can manipulate the pose of 3D objects intuitively by modifying the parameters of the blobs, while preserving rich instance-specific information. We evaluate the proposed method on a variety of object categories and demonstrate the effectiveness of the proposed framework. Project page: https://guangzhaohe.com/canor
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