The AIT lab conducts research at the fore-front of human-centric computer vision.
Our core research interests are algorithms and methods for the spatio-temporal understanding of how humans move within and interact with the physical world.
We develop learning-based algorithms, methods and representations for human- and interaction-centric understanding of our world from videos, images and other sensor data.
Application domains of interest include Augmented and Virtual Reality, Human Robot Interaction and more.
Please refer to our
publications for more information.
The AIT Lab, led by
Prof. Dr. Otmar Hilliges,
is part of the Institute for Intelligent Interactive Systems (IIS),
in the Department of Computer Science
at ETH Zurich.
Markos Diomataris joins the AIT lab. Welcome! 01.07.2022
Mert Albaba joins the AIT lab. Welcome! 09.08.2022 We have 1 paper accepted at
TMLR. Stay tuned for more details. 09.08.2022 We have 2 papers accepted at
3DV 2022. Stay tuned for more details. 01.07.2022 We have 1 paper accepted at
ECCV 2022. Stay tuned for more details. 09.05.2022 We have 1 paper accepted at
SIGGRAPH 2022. Stay tuned for more details. 01.05.2022
Hsuan-I Ho and Artur Grigorev join the AIT lab. Welcome! 17.03.2022 Prof. Dr. Otmar Hilliges receives the
ERC Consolidator Grant. Congratulations! Check
here for previous news.
Reconstructing Action-Conditioned Human-Object Interactions Using Commonsense Knowledge Priors TempCLR: Reconstructing Hands via Time-Coherent Contrastive Learning SFP: State-free Priors for Exploration in Off-Policy Reinforcement Learning EyeNeRF: A Hybrid Representation for Photorealistic Synthesis, Animation and Relighting of Human Eyes D-Grasp: Physically Plausible Dynamic Grasp Synthesis for Hand-Object Interactions gDNA: Towards Generative Detailed Neural Avatars I M Avatar: Implicit Morphable Head Avatars from Videos PINA: Learning a Personalized Implicit Neural Avatar from a Single RGB-D Video Sequence Computational Design of Kinesthetic Garments Human Performance Capture from Monocular Video in the Wild A Skeleton-Driven Neural Occupancy Representation for Articulated Hands Learning to Disambiguate Strongly Interacting Hands via Probabilistic Per-pixel Part Segmentation A Spatio-temporal Transformer for 3D Human Motion Prediction VariTex: Variational Neural Face Textures EM-POSE: 3D Human Pose Estimation from Sparse Electromagnetic Trackers SNARF: Differentiable Forward Skinning for Animating Non-Rigid Neural Implicit Shapes Shape-aware Multi-Person Pose Estimation from Multi-view Images PeCLR: Self-Supervised 3D Hand Pose Estimation from monocular RGB via Equivariant Contrastive Learning SPEC: Seeing People in the wild with an Estimated Camera PARE: Part Attention Regressor for 3D Human Body Estimation Hedgehog: Handheld Spherical Pin Array based on a CentralElectromagnetic Actuator Learning Functionally Decomposed Hierarchies for Continuous Control Tasks With Path Planning Improved Learning of Robot Manipulation Tasks via Tactile Intrinsic Motivation Optimization-based User Support for Cinematographic Quadrotor Camera Target Framing Hierarchical Reinforcement Learning Explains Task Interleaving Behavior CoSE: Compositional Stroke Embeddings Self-Learning Transformations for Improving Gaze and Head Redirection Spatial Attention Improves Iterative 6D Object Pose Estimation Convolutional Autoencoders for Human Motion Infilling Omni: Volumetric Sensing and Actuation of Passive Magnetic Tools for Dynamic Haptic Feedback Optimal Control for Electromagnetic Haptic Guidance Systems Learning-based Region Selection for End-to-End Gaze Estimation Human Body Model Fitting by Learned Gradient Descent Category Level Object Pose Estimation via Neural Analysis-by-Synthesis Weakly Supervised 3D Hand Pose Estimation via Biomechanical Constraints ETH-XGaze: A Large Scale Dataset for Gaze Estimation under Extreme Head Pose and Gaze Variation Towards End-to-end Video-based Eye-Tracking Contact-free Nonplanar Haptics with a Spherical Electromagnet Accurate Real-time 3D Gaze Tracking Using a Lightweight Eyeball Calibration Learning to Assemble: Estimating 6D Poses for Robotic Object-Object Manipulation