HumanRig: Learning Automatic Rigging for Humanoid Character in a Large Scale Dataset (2412.02317v1)

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.

• The paper introduces the first large-scale HumanRig dataset, featuring 11,434 AI-generated T-pose meshes aligned with a standard Mixamo skeleton.
• The paper presents an innovative automatic rigging framework combining PGSE and MSMAN to enhance skeleton estimation and skinning optimization.
• The paper demonstrates significant improvements over traditional methods through robust numerical evaluations and ablation studies.

An Expert Review of the Paper "HumanRig: Learning Automatic Rigging for Humanoid Characters in a Large-Scale Dataset"

The paper "HumanRig: Learning Automatic Rigging for Humanoid Characters in a Large-Scale Dataset," authored by Zedong Chu and collaborators, introduces a significant advancement in the domain of automatic rigging for 3D humanoid characters. Within this paper, the authors address two predominant issues that have hindered progress in this field: the lack of comprehensive datasets and the inadequacies of existing methods in dealing with complex, AI-generated meshes.

Summary of Contributions

1. HumanRig Dataset: The introduction of HumanRig, the first large-scale dataset specifically designed for 3D humanoid character rigging, is a pivotal contribution. This dataset consists of 11,434 AI-generated T-pose meshes aligned with a standard Mixamo skeleton. The dataset’s inclusivity, spanning various head-to-body ratios, from real human forms to cartoon styles and humanoid animals, marks a significant advance from earlier datasets such as SMPL and RigNetv1, which offered limited diversity and scalability.
2. Automatic Rigging Framework: The authors present a novel framework combining several advanced components, including the Prior-Guided Skeleton Estimator (PGSE) and the Mesh-Skeleton Mutual Attention Network (MSMAN). This combination facilitates a comprehensive rigging approach that integrates both skeleton construction and skinning tasks, outperforming existing Graph Neural Network (GNN)-based rigging methods that struggle with AI-generated meshes' chaotic topologies.
3. Technical Innovations: The PGSE module serves as an initial skeleton estimator using 2D skeleton joints projected into 3D, which simplifies the rigging task by reducing complexity. In parallel, a U-shaped Point Transformer is utilized to encode mesh data effectively, which, when combined with MSMAN, allows for the refinement of skeleton positions and skinning weights through joint optimization.

Strong Numerical Results and Experimentation

The paper provides robust empirical evidence demonstrating the superiority of their proposed methods. Evaluations include cross-dataset comparisons and ablation studies that underscore the HumanRig dataset's effectiveness and the improved performance of their new framework.

• When benchmarked against the RigNetv1-human dataset, models trained with the HumanRig dataset showed superior performance across various skeleton construction and skinning metrics.
• Ablation studies reveal PGSE and MSMAN's individual contributions, with MSMAN proving crucial for joint optimization tasks.
• Compared to traditional mesh encoders like GraphSAGE and GraphTransformer, the Point Transformer-based mesh encoder exhibits noteworthy gains in generalization and robustness.

Implications and Future Directions

The implications of this research are twofold: practical and theoretical. Practically, the HumanRig dataset and the accompanying framework promise to revolutionize the animation industry by enabling more efficient and automated character rigging processes, thus reducing reliance on manual rigging labor. This advance represents a crucial step towards integrating AI into creative workflows, paving the way for new forms of digital content creation.

Theoretically, the work suggests future avenues for research, particularly in extending the current methods to more complex skeleton models that include detailed limb articulations, such as fingers, and adapting the framework for non-humanoid characters and quadrupeds. By leveraging AI model-generation techniques to develop enriched datasets, further advancements could be made in creating diverse and scalable character models.

In summary, the authors present a well-rounded contribution to automatic rigging research, providing both a substantial dataset and a robust methodology capable of tackling current challenges in AI-generated 3D modeling. It lays the groundwork for subsequent advancements in creating more efficient and sophisticated animation pipelines.