Publications

You can also find my articles on my Google Scholar profile.

2026


Divide and Truncate: A Penetration and Inversion Free Framework for Coupled Multi-physics Systems

Anka He Chen, Jerry Hsu, Youssef Ayman, Miles Macklin

ACM SIGGRAPH 2026 Conference Proceedings

A unified framework for penetration-free and inversion-free contact resolution across coupled multi-physics systems including rigid bodies, soft volumetric objects, thin shells, and rods. • PDFArXiv

FreeForm: Reduced-Order Deformable Simulation from Particle-Based Skinning Eigenmodes

Donglai Xiang, Vismay Modi, Rishit Dagli, Ty Trusty, Gilles Daviet, Anka He Chen, Nicholas Sharp, David Levin I.W.

CVPR 2026

A reduced-order deformable simulation method using particle-based skinning eigenmodes. • Project

VoMP: Predicting Volumetric Mechanical Property Fields

Rishit Dagli, Donglai Xiang, Vismay Modi, Charles Loop, Clement Fuji Tsang, Anka He Chen, Anita Hu, Gavriel State, David Levin I.W., Maria Shugrina

ICLR 2026

A method for predicting volumetric mechanical property fields. • PDFArXivProjectCode

Physically Valid Biomolecular Interaction Modeling with Gauss-Seidel Projection

Siyuan Chen, Minghao Guo, Caoliwen Wang, Anka He Chen, Yikun Zhang, Jingjing Chai, Yin Yang, Wojciech Matusik, Peter Yichen Chen

ICLR 2026

A physics-based framework for biomolecular interaction modeling using Gauss-Seidel projection. • PDFArXivCode

2025


Offset Geometric Contact

Anka He Chen, Jerry Hsu, Ziheng Liu, Miles Macklin, Yin Yang, Cem Yuksel

ACM Transactions on Graphics 44(4) [Proceedings of SIGGRAPH Asia]

A novel contact model for guaranteed penetration-free simulation of codimensional objects with minimal computational overhead. • PDFProjectVideo

Isaac Lab: A Unified and Modular Framework for Robot Learning

Mayank Mittal, Pascal Roth, James Tigue, Antoine Richard, Octi Zhang, Peter Du, Antonio Serrano-Muñoz, Xinjie Yao, René Zurbrügg, Nikita Rudin, et al.

arXiv preprint

A GPU-accelerated simulation framework for multi-modal robot learning built on NVIDIA Isaac Sim. • PDFProjectGitHub

2024


Vertex Block Descent

Anka He Chen, Zhiheng Liu, Yin Yang, Cem Yuksel

ACM Transactions on Graphics 43(4) [Proceedings of SIGGRAPH]

A block coordinate descent solution for the variational form of implicit Euler through vertex-level Gauss-Seidel iterations. • PDFProjectVideo

2023


Shortest Path to Boundary for Self-Intersecting Meshes

Anka He Chen, Elie Diaz, Cem Yuksel

ACM Transactions on Graphics 42(4) [Proceedings of SIGGRAPH]

A method for efficiently computing the exact shortest path to the boundary of a mesh from a given internal point in the presence of self-intersections. • ProjectVideo

2021


Capturing Detailed Deformations of Moving Human Bodies

Anka He Chen, Hyojoon Park, Kutay Macit, Ladislav Kavan

ACM Transactions on Graphics 40(4) [Proceedings of SIGGRAPH]

A method to capture over 1,000 unique points on the human body using only standard cameras and passive lights. • PDFProject

2018


Multi-Normal Estimation via Pair Consistency Voting

Jie Zhang*, Junjie Cao* (co-first authors), Xiuping Liu, Anka He Chen, Bo Li, Ligang Liu

IEEE Transactions on Visualization and Computer Graphics (TVCG)

A unified definition for point cloud normal of feature and non-feature points, allowing feature points to possess multiple normals.

2017


Normal Estimation via Shifted Neighborhood for Point Cloud

Junjie Cao, Anka He Chen, Jie Zhang, Yujiao Li, Xiuping Liu, Changqing Zou

Journal of Computational and Applied Mathematics

A fast and quality normal estimator based on neighborhood shift for point clouds.

2016


Online Knapsack Problem Under Concave Functions

Xin Han, Ning Ma, Kazuhisa Makino, He Chen

Frontiers in Algorithmics

An online algorithm for the knapsack problem under concave functions with improved competitive ratios.

Mesh Saliency Detection via Double Absorbing Markov Chain in Feature Space

Xiuping Liu, Pingping Tao, Junjie Cao, He Chen, Changqing Zou

The Visual Computer

A mesh saliency detection approach using absorbing Markov chain that considers both background and foreground cues.