RigAnything: Template-Free Autoregressive Rigging for Diverse 3D Assets

• URL: http://arxiv.org/abs/2502.09615v1
• Date: Thu, 13 Feb 2025 18:59:13 GMT
• Title: RigAnything: Template-Free Autoregressive Rigging for Diverse 3D Assets
• Authors: Isabella Liu, Zhan Xu, Wang Yifan, Hao Tan, Zexiang Xu, Xiaolong Wang, Hao Su, Zifan Shi,
• Abstract summary: We present RigAnything, a novel autoregressive transformer-based model. It makes 3D assets rig-ready by probabilistically generating joints, skeleton topologies, and assigning skinning weights in a template-free manner. RigAnything demonstrates state-of-the-art performance across diverse object types, including humanoids, quadrupeds, marine creatures, insects, and many more.
• Score: 47.81216915952291
• License:
• Abstract: We present RigAnything, a novel autoregressive transformer-based model, which makes 3D assets rig-ready by probabilistically generating joints, skeleton topologies, and assigning skinning weights in a template-free manner. Unlike most existing auto-rigging methods, which rely on predefined skeleton template and are limited to specific categories like humanoid, RigAnything approaches the rigging problem in an autoregressive manner, iteratively predicting the next joint based on the global input shape and the previous prediction. While autoregressive models are typically used to generate sequential data, RigAnything extends their application to effectively learn and represent skeletons, which are inherently tree structures. To achieve this, we organize the joints in a breadth-first search (BFS) order, enabling the skeleton to be defined as a sequence of 3D locations and the parent index. Furthermore, our model improves the accuracy of position prediction by leveraging diffusion modeling, ensuring precise and consistent placement of joints within the hierarchy. This formulation allows the autoregressive model to efficiently capture both spatial and hierarchical relationships within the skeleton. Trained end-to-end on both RigNet and Objaverse datasets, RigAnything demonstrates state-of-the-art performance across diverse object types, including humanoids, quadrupeds, marine creatures, insects, and many more, surpassing prior methods in quality, robustness, generalizability, and efficiency. Please check our website for more details: https://www.liuisabella.com/RigAnything.

Related papers

• MagicArticulate: Make Your 3D Models Articulation-Ready [109.35703811628045]
  We present MagicArticulate, an effective framework that automatically transforms static 3D models into articulation-ready assets. Our key contributions are threefold. First, we introduce Articulation-averse benchmark containing over 33k 3D models with high-quality articulation annotations, carefully curated from XL-XL. Extensive experiments demonstrate that MagicArticulate significantly outperforms existing methods across diverse object categories.
  arXiv  Detail & Related papers  (2025-02-17T18:53:27Z)
• HumanRig: Learning Automatic Rigging for Humanoid Character in a Large Scale Dataset [6.978870586488504]
  We present HumanRig, the first large-scale dataset specifically designed for 3D humanoid character rigging. We introduce an innovative, data-driven automatic rigging framework, which overcomes the limitations of GNN-based methods. This work not only remedies the dataset deficiency in rigging research but also propels the animation industry towards more efficient and automated character rigging pipelines.
  arXiv  Detail & Related papers  (2024-12-03T09:33:00Z)
• Neural Localizer Fields for Continuous 3D Human Pose and Shape Estimation [32.30055363306321]
  We propose a paradigm for seamlessly unifying different human pose and shape-related tasks and datasets. Our formulation is centered on the ability to query any arbitrary point of the human volume, and obtain its estimated location in 3D.
  arXiv  Detail & Related papers  (2024-07-10T10:44:18Z)
• Pushing Auto-regressive Models for 3D Shape Generation at Capacity and Scalability [118.26563926533517]
  Auto-regressive models have achieved impressive results in 2D image generation by modeling joint distributions in grid space. We extend auto-regressive models to 3D domains, and seek a stronger ability of 3D shape generation by improving auto-regressive models at capacity and scalability simultaneously.
  arXiv  Detail & Related papers  (2024-02-19T15:33:09Z)
• Unsupervised 3D Pose Estimation with Non-Rigid Structure-from-Motion Modeling [83.76377808476039]
  We propose a new modeling method for human pose deformations and design an accompanying diffusion-based motion prior. Inspired by the field of non-rigid structure-from-motion, we divide the task of reconstructing 3D human skeletons in motion into the estimation of a 3D reference skeleton. A mixed spatial-temporal NRSfMformer is used to simultaneously estimate the 3D reference skeleton and the skeleton deformation of each frame from 2D observations sequence.
  arXiv  Detail & Related papers  (2023-08-18T16:41:57Z)
• Iterative Graph Filtering Network for 3D Human Pose Estimation [5.177947445379688]
  Graph convolutional networks (GCNs) have proven to be an effective approach for 3D human pose estimation. In this paper, we introduce an iterative graph filtering framework for 3D human pose estimation. Our approach builds upon the idea of iteratively solving graph filtering with Laplacian regularization.
  arXiv  Detail & Related papers  (2023-07-29T20:46:44Z)
• Learning 3D Human Pose Estimation from Dozens of Datasets using a Geometry-Aware Autoencoder to Bridge Between Skeleton Formats [80.12253291709673]
  We propose a novel affine-combining autoencoder (ACAE) method to perform dimensionality reduction on the number of landmarks. Our approach scales to an extreme multi-dataset regime, where we use 28 3D human pose datasets to supervise one model.
  arXiv  Detail & Related papers  (2022-12-29T22:22:49Z)
• Hi-LASSIE: High-Fidelity Articulated Shape and Skeleton Discovery from Sparse Image Ensemble [72.3681707384754]
  Hi-LASSIE performs 3D articulated reconstruction from only 20-30 online images in the wild without any user-defined shape or skeleton templates. First, instead of relying on a manually annotated 3D skeleton, we automatically estimate a class-specific skeleton from the selected reference image. Second, we improve the shape reconstructions with novel instance-specific optimization strategies that allow reconstructions to faithful fit on each instance.
  arXiv  Detail & Related papers  (2022-12-21T14:31:33Z)
• Dynamical Deep Generative Latent Modeling of 3D Skeletal Motion [15.359134407309726]
  Our model decomposes highly correlated skeleton data into a set of few spatial basis of switching temporal processes. This results in a dynamical deep generative latent model that parses the meaningful intrinsic states in the dynamics of 3D pose data.
  arXiv  Detail & Related papers  (2021-06-18T23:58:49Z)
• Dynamic multi-object Gaussian process models: A framework for data-driven functional modelling of human joints [0.0]
  A principled and robust way to combine shape and pose features has been illusive due to three main issues. We propose a new framework for dynamic multi-object statistical modelling framework for the analysis of human joints. The framework affords an efficient generative dynamic multi-object modelling platform for biological joints.
  arXiv  Detail & Related papers  (2020-01-22T07:57:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.