HumanRig: Learning Automatic Rigging for Humanoid Character in a Large Scale Dataset

• URL: http://arxiv.org/abs/2412.02317v1
• Date: Tue, 03 Dec 2024 09:33:00 GMT
• Title: HumanRig: Learning Automatic Rigging for Humanoid Character in a Large Scale Dataset
• Authors: Zedong Chu, Feng Xiong, Meiduo Liu, Jinzhi Zhang, Mingqi Shao, Zhaoxu Sun, Di Wang, Mu Xu,
• Abstract summary: We present HumanRig, the first large-scale dataset specifically designed for 3D humanoid character rigging.<n>We introduce an innovative, data-driven automatic rigging framework, which overcomes the limitations of GNN-based methods.<n>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.
• Score: 6.978870586488504
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the rapid evolution of 3D generation algorithms, the cost of producing 3D humanoid character models has plummeted, yet the field is impeded by the lack of a comprehensive dataset for automatic rigging, which is a pivotal step in character animation. Addressing this gap, we present HumanRig, the first large-scale dataset specifically designed for 3D humanoid character rigging, encompassing 11,434 meticulously curated T-posed meshes adhered to a uniform skeleton topology. Capitalizing on this dataset, we introduce an innovative, data-driven automatic rigging framework, which overcomes the limitations of GNN-based methods in handling complex AI-generated meshes. Our approach integrates a Prior-Guided Skeleton Estimator (PGSE) module, which uses 2D skeleton joints to provide a preliminary 3D skeleton, and a Mesh-Skeleton Mutual Attention Network (MSMAN) that fuses skeleton features with 3D mesh features extracted by a U-shaped point transformer. This enables a coarse-to-fine 3D skeleton joint regression and a robust skinning estimation, surpassing previous methods in quality and versatility. 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.

Related papers

• SIGMAN:Scaling 3D Human Gaussian Generation with Millions of Assets [72.26350984924129]
  We propose a latent space generation paradigm for 3D human digitization. We transform the ill-posed low-to-high-dimensional mapping problem into a learnable distribution shift. We employ the multi-view optimization approach combined with synthetic data to construct the HGS-1M dataset.
  arXiv  Detail & Related papers  (2025-04-09T15:38:18Z)
• ARMO: Autoregressive Rigging for Multi-Category Objects [8.030479370619458]
  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, our dataset embraces diverse shape categories, styles, and poses. We propose ARMO, a novel rigging framework that utilizes an autoregressive model to predict both joint positions and connectivity relationships in a unified manner.
  arXiv  Detail & Related papers  (2025-03-26T15:56:48Z)
• 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)
• RigAnything: Template-Free Autoregressive Rigging for Diverse 3D Assets [47.81216915952291]
  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.
  arXiv  Detail & Related papers  (2025-02-13T18:59:13Z)
• 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 - both at training and test time - to query any arbitrary point of the human volume. We can naturally exploit differently annotated data sources including mesh, 2D/3D skeleton and dense pose, without having to convert between them.
  arXiv  Detail & Related papers  (2024-07-10T10:44:18Z)
• Graph and Skipped Transformer: Exploiting Spatial and Temporal Modeling Capacities for Efficient 3D Human Pose Estimation [36.93661496405653]
  We take a global approach to exploit Transformer-temporal information with a concise Graph and Skipped Transformer architecture. Specifically, in 3D pose stage, coarse-grained body parts are deployed to construct a fully data-driven adaptive model. Experiments are conducted on Human3.6M, MPI-INF-3DHP and Human-Eva benchmarks.
  arXiv  Detail & Related papers  (2024-07-03T10:42:09Z)
• 3D Human Reconstruction in the Wild with Synthetic Data Using Generative Models [52.96248836582542]
  We propose an effective approach based on recent diffusion models, termed HumanWild, which can effortlessly generate human images and corresponding 3D mesh annotations. By exclusively employing generative models, we generate large-scale in-the-wild human images and high-quality annotations, eliminating the need for real-world data collection.
  arXiv  Detail & Related papers  (2024-03-17T06:31:16Z)
• SpATr: MoCap 3D Human Action Recognition based on Spiral Auto-encoder and Transformer Network [1.4732811715354455]
  We introduce a novel approach for 3D human action recognition, denoted as SpATr (Spiral Auto-encoder and Transformer Network) A lightweight auto-encoder, based on spiral convolutions, is employed to extract spatial geometrical features from each 3D mesh. The proposed method is evaluated on three prominent 3D human action datasets: Babel, MoVi, and BMLrub.
  arXiv  Detail & Related papers  (2023-06-30T11:49:00Z)
• 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)
• A Lightweight Graph Transformer Network for Human Mesh Reconstruction from 2D Human Pose [8.816462200869445]
  We present GTRS, a pose-based method that can reconstruct human mesh from 2D human pose. We demonstrate the efficiency and generalization of GTRS by extensive evaluations on the Human3.6M and 3DPW datasets.
  arXiv  Detail & Related papers  (2021-11-24T18:48:03Z)
• VoteHMR: Occlusion-Aware Voting Network for Robust 3D Human Mesh Recovery from Partial Point Clouds [32.72878775887121]
  We make the first attempt to reconstruct reliable 3D human shapes from single-frame partial point clouds. We propose an end-to-end learnable method, named VoteHMR. The proposed method achieves state-of-the-art performances on two large-scale datasets.
  arXiv  Detail & Related papers  (2021-10-17T05:42:04Z)
• THUNDR: Transformer-based 3D HUmaN Reconstruction with Markers [67.8628917474705]
  THUNDR is a transformer-based deep neural network methodology to reconstruct the 3d pose and shape of people. We show state-of-the-art results on Human3.6M and 3DPW, for both the fully-supervised and the self-supervised models. We observe very solid 3d reconstruction performance for difficult human poses collected in the wild.
  arXiv  Detail & Related papers  (2021-06-17T09:09:24Z)
• Exemplar Fine-Tuning for 3D Human Model Fitting Towards In-the-Wild 3D Human Pose Estimation [107.07047303858664]
  Large-scale human datasets with 3D ground-truth annotations are difficult to obtain in the wild. We address this problem by augmenting existing 2D datasets with high-quality 3D pose fits. The resulting annotations are sufficient to train from scratch 3D pose regressor networks that outperform the current state-of-the-art on in-the-wild benchmarks.
  arXiv  Detail & Related papers  (2020-04-07T20:21:18Z)
• Anatomy-aware 3D Human Pose Estimation with Bone-based Pose Decomposition [92.99291528676021]
  Instead of directly regressing the 3D joint locations, we decompose the task into bone direction prediction and bone length prediction. Our motivation is the fact that the bone lengths of a human skeleton remain consistent across time. Our full model outperforms the previous best results on Human3.6M and MPI-INF-3DHP datasets.
  arXiv  Detail & Related papers  (2020-02-24T15:49:37Z)

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.