Add paper: Physics-Based Motion Tracking of Contact-Rich Interacting Characters

[ImChong]

Summary

Added comprehensive documentation for the paper "Physics-Based Motion Tracking of Contact-Rich Interacting Characters" (arXiv 2604.07984), which presents a method for stable physics-based motion tracking of multi-character interactions using progressive neural networks with automatic expert assignment.

Key Changes

• New paper documentation (papers/13_Physics-Based_Animation/Physics-Based_Motion_Tracking_of_Contact-Rich_Interacting_Characters/Physics-Based_Motion_Tracking_of_Contact-Rich_Interacting_Characters.md):

  • Complete paper summary with bilingual titles (English + Chinese)
  • Problem statement: challenges of tracking contact-rich multi-character interactions in physics simulation
  • Method overview: Progressive Neural Networks (PNN) with multiple experts, automatic sample assignment, and curriculum learning without manual scheduling
  • Key insights on contact coupling as the core difficulty in multi-character physics tracking
  • Experimental results and practical implications for humanoid robotics
  • Interview preparation Q&A section
  • Related reading references

• Updated progress tracking:

  • Added entry to progress.json with paper metadata (index 541, module 13_Physics-Based_Animation, status "done")
  • Updated module rotation tracking (from 12_Hardware_Design → 13_Physics-Based_Animation → 14_Human_Motion)
  • Added paper to _data/papers.json with arXiv reference and bilingual metadata
  • Updated DAILY_SUMMARY_LOG.md and PROGRESS.md to reflect completion

Notable Details

• Paper authored by Xiaotang Zhang, Ziyi Chang, Qianhui Men, and Hubert P. H. Shum from Durham University and University of Bristol
• Published April 9, 2026 (arXiv) / Eurographics 2026 / Computer Graphics Forum
• Addresses the practical problem of force transmission between characters causing instability in contact-dense interactions
• Uses expert decomposition and progressive knowledge reuse to improve both stability and training efficiency
• No public source code currently available (as of documentation date)

https://claude.ai/code/session_01Dbs92qUyQUzoRZvpwRgG9R