Category-Agnostic Neural Object Rigging

2025-05-26

AI Summary

Traditional heuristic rigging methods rely on category-specific expert knowledge and suffer from poor scalability. This paper proposes a purely data-driven neural rigging representation for deformable 4D objects of arbitrary categories—requiring no category priors. It decomposes each object into sparse, spatially localized blobs and instance-aware 3D feature volumes, jointly modeling pose and shape. Pose manipulation is made intuitive via unsupervised low-dimensional manifold learning, while detail fidelity is preserved through end-to-end differentiable deformation. To our knowledge, this is the first approach enabling automatic, cross-category neural rigging—eliminating manual rigging and category-specific modeling. We validate it on diverse dynamic objects across categories, demonstrating high-fidelity pose editing, strong cross-category generalization, and superior controllability and reconstruction accuracy compared to conventional methods.

Technology Category

• Machine Learning: Learning with Manifolds
• Intelligent Robots: Manipulation
• Computer Vision: Representation Learning for Vision

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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

Problem

• Automatic exploration of low-dimensional structures in deformable 4D objects
• Disentangling pose and instance information of 3D shapes
• Intuitive manipulation of 3D objects via sparse blob parameters

Innovation

• Encodes 4D objects into sparse blobs
• Uses instance-aware feature volume
• Manipulates pose via blob parameters