119 citations
- Carnegie Mellon UniversityUS4 papers
- Seoul National UniversityKR2 papers
- University of Hong KongHK2 papers
- Aarhus UniversityDK1 paper
- Centre Inria de l'Université Grenoble AlpesFR1 paper
- Dartmouth CollegeUS1 paper
- ETH ZurichCH1 paper
- Georgia Institute of TechnologyUS1 paper
- Guangdong Academy of SciencesCN1 paper
- Guangdong Institute of Intelligent ManufacturingCN1 paper
- Institut national de recherche en sciences et technologies du numériqueFR1 paper
- Korea Advanced Institute of Science and TechnologyKR1 paper
7 papers · 1 filter
Neural Stress Fields for Reduced-order Elastoplasticity and Fracture
Zeshun Zong, Xuan Li, Minchen Li +6
We propose a hybrid neural network and physics framework for reduced-order modeling of elastoplasticity and fracture. State-of-the-art scientific computing models like the Material…
MOCHA: Real-Time Motion Characterization via Context Matching
Deok-Kyeong Jang, Yuting Ye, Jungdam Won +1
Transforming neutral, characterless input motions to embody the distinct style of a notable character in real time is highly compelling for character animation. This paper introduc…
Drivable Avatar Clothing: Faithful Full-Body Telepresence with Dynamic Clothing Driven by Sparse RGB-D Input
Donglai Xiang, Fabian Prada, Zhe Cao +4
Clothing is an important part of human appearance but challenging to model in photorealistic avatars. In this work we present avatars with dynamically moving loose clothing that ca…
Motion In-Betweening with Phase Manifolds
Paul Starke, Sebastian Starke, Taku Komura +1
This paper introduces a novel data-driven motion in-betweening system to reach target poses of characters by making use of phases variables learned by a Periodic Autoencoder. Our a…
QuestEnvSim: Environment-Aware Simulated Motion Tracking from Sparse Sensors
Sunmin Lee, Sebastian Starke, Yuting Ye +2
Replicating a user's pose from only wearable sensors is important for many AR/VR applications. Most existing methods for motion tracking avoid environment interaction apart from fo…
Neural Shadow Mapping
Sayantan Datta, Derek Nowrouzezahrai, Christoph Schied +1
We present a neural extension of basic shadow mapping for fast, high quality hard and soft shadows. We compare favorably to fast pre-filtering shadow mapping, all while producing v…