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

Want to make AI characters dance? First teach them about their bones - HumanRig shows how with 11K+ examples.

This paper introduces HumanRig, a groundbreaking dataset of 11,434 AI-generated 3D models with standardized skeleton structures for automated character rigging.

https://arxiv.org/abs/2412.02317v1

🤖 Original Problem:

→ Current 3D character rigging lacks comprehensive datasets, making automation difficult

→ Existing methods struggle with complex AI-generated meshes and diverse body proportions

→ Manual rigging is time-consuming and requires skilled artists

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🛠️ Solution in this Paper:

→ Created HumanRig dataset with 11,434 AI-generated T-pose meshes using uniform skeleton topology

→ Developed Prior-Guided Skeleton Estimator (PGSE) for initial skeleton positioning

→ Implemented Point Transformer-based mesh encoder for better feature extraction

→ Designed Mesh-Skeleton Mutual Attention Network for joint optimization

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💡 Key Insights:

→ AI-generated meshes need different handling than artist-created ones

→ 2D skeleton priors significantly improve 3D skeleton estimation

→ Diverse head-to-body ratios crucial for model generalization

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📊 Results:

→ Outperforms existing methods in skeleton construction (CD-J2J drops from 0.0110 to 0.0027)

→ Achieves superior skinning precision (0.9271) compared to previous approaches

→ Shows 60% improvement in deformation quality over traditional methods