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# 📖 Open Courseware

This page collects publicly accessible courses and full-length instructional tutorials with coherent curricula related to human-centric AI. The collection spans digital humans, human motion, activity recognition, human feedback, embodied intelligence, humanoid systems, and human-robot interaction.

Individual tutorials, keynotes, seminars, and invited lectures are collected separately under [Academic Presentations](academic-presentations.md).

> **Inclusion scope.** A resource is included when it offers a sustained curriculum across multiple classes or a coherent full-length tutorial with meaningful public learning materials, such as videos, slides, assignments, readings, notebooks, or practical exercises.

## Human Modeling, Avatars, and Motion

### [Virtual Humans](https://virtualhumans.mpi-inf.mpg.de/VH23/)

**Offering:** Winter 2024/25<br>
**Instructor:** Gerard Pons-Moll and the Real Virtual Humans team<br>
**Institution:** University of Tübingen and Max Planck Institute for Intelligent Systems<br>
**Access:** [Course website](https://virtualhumans.mpi-inf.mpg.de/VH23/) | [Video playlist](https://www.youtube.com/playlist?list=PLD1ofCm3vxfz4Oe5XqHmyU6GTLsPJMI5s)<br>
**Materials:** Videos, slides, tutorials, and assignments

A comprehensive course on learning virtual humans from scans, images, videos, and wearable sensors. It covers body and clothing models, surface representations, SMPL fitting, neural implicit representations, neural rendering, behavior capture, and motion synthesis.

### [GAMES105: Fundamentals of Character Animation](https://games-105.github.io/)

**Offering:** 2022/23<br>
**Instructor:** Libin Liu<br>
**Institution:** Peking University and GAMES<br>
**Access:** [Course website](https://games-105.github.io/) | [Full video course](https://www.bilibili.com/video/BV1GG4y1p7fF/) | [Lab repository](https://github.com/GAMES-105/GAMES-105)<br>
**Materials:** Thirteen video lectures, slides, and programming assignments

A systematic course connecting character kinematics and data-driven animation with modern learning-based and physics-based methods. Topics include inverse kinematics, skinning, motion synthesis, articulated-body simulation, feedback control, trajectory optimization, reinforcement learning, and multi-skilled characters.

### [Biomechanics of Movement](https://biomech.stanford.edu/)

**Authors:** Thomas K. Uchida and Scott L. Delp<br>
**Platform:** Stanford Biomechanics of Movement Classroom<br>
**Access:** [Learning portal](https://biomech.stanford.edu/) | [Lecture videos](https://www.youtube.com/playlist?list=PL_uk_kfAmFLrtzEfv6njXooOPae3jI1q6)<br>
**Materials:** Lectures, homework problems, slides, data, models, and OpenSim resources

An open curriculum on the mechanical and physiological foundations of human movement. It connects walking and running biomechanics with musculoskeletal modeling, physics-based simulation, rehabilitation, wearable robotics, and human performance analysis.

### [Practical Course: 3D Shape Analysis and Virtual Humans Applications](https://cvg.cit.tum.de/teaching/ws2025/savha)

**Offering:** Winter 2025/26<br>
**Organizers:** Viktoria Ehm, Maolin Gao, Riccardo Marin, and Thomas Dages<br>
**Institution:** Technical University of Munich<br>
**Access:** [Course website](https://cvg.cit.tum.de/teaching/ws2025/savha) | [Preliminary slides](https://docs.google.com/presentation/d/1P5xRJoZkNguUYl0cNcdChStoNu43Xdu0veOO4x1CL5Y/edit?usp=sharing)<br>
**Materials:** Introductory slides, project descriptions, references, and practical project guidance

A project-based course that introduces 3D shape analysis and then applies it to virtual-human problems. Its projects span shape matching, point-cloud registration, geometric deep learning, human reconstruction and animation, 3D human-object interaction, and generative AI for 3D content.

### [NVIDIA Digital Humans Teaching Kit](https://developer.nvidia.com/digital-humans-teaching-kit-syllabus)

**Provider:** NVIDIA Deep Learning Institute<br>
**Access:** [Teaching kit syllabus](https://developer.nvidia.com/digital-humans-teaching-kit-syllabus)<br>
**Materials:** Modular lecture slides, selected lecture videos, quizzes, self-paced course links, documentation, and workflow examples

A modular teaching kit for constructing digital-human pipelines with NVIDIA Omniverse. It covers Omniverse Kit, OpenUSD, scene assembly, digital content creation workflows, Audio2Face, Audio2Emotion, Audio2Gesture, lighting, rendering, and interactive presentation. Some planned slides, videos, labs, and quizzes are not yet available, and the syllabus identifies their release status by module.

### [Human Motion Analysis](https://www.cs.toronto.edu/~urtasun/courses/ETH10/human_motion_analysis.html)

**Offering:** Spring 2010<br>
**Instructor:** Raquel Urtasun<br>
**Access:** Lecture slides, exercises, readings, and project materials

A focused course on human motion representations, pose and motion models, tracking, synthesis, classification, probabilistic inference, kinematic priors, and physics-based human animation.

### [CS7496: Computer Animation](https://sites.cc.gatech.edu/classes/AY2017/cs7496_fall/Home.html)

**Offering:** Fall 2017<br>
**Institution:** Georgia Institute of Technology<br>
**Access:** Syllabus, readings, mini-projects, and final-project materials

A mathematically oriented course on simulation, analysis, and control of dynamic systems, with an emphasis on human motion, physics simulation, character control, and statistical motion modeling.

### [SMPL Made Simple](https://smpl-made-simple.is.tuebingen.mpg.de/)

**Offering:** CVPR 2021 full-day tutorial<br>
**Instructors:** Naureen Mahmood, Timo Bolkart, Ahmed A. A. Osman, Joachim Tesch, Dimitrios Tzionas, Michael J. Black, and invited speakers<br>
**Access:** [Tutorial website and recordings](https://smpl-made-simple.is.tuebingen.mpg.de/)<br>
**Materials:** Recorded presentations, slides, frequently asked questions, implementation guidance, and application examples

A comprehensive tutorial on the foundations and practical use of SMPL and related body models. It connects model construction and fitting with datasets, software integration, reconstruction, neural rendering, scene interaction, clothing, health, and visual imitation.

## Human Activity Recognition and Sensing

### [Deep Learning for Human Activity Recognition](https://mariusbock.github.io/dl-for-har/)

**Offering:** ISWC and UbiComp 2021 tutorial<br>
**Instructors:** Marius Bock, Alexander Hölzemann, Michael Möller, and Kristof Van Laerhoven<br>
**Access:** [Tutorial website](https://mariusbock.github.io/dl-for-har/) | [Tutorial paper](https://arxiv.org/abs/2110.06663)<br>
**Materials:** Slides, Jupyter notebooks, Google Colab exercises, data-processing examples, and evaluation exercises

A hands-on introduction to building deep-learning pipelines for sensor-based human activity recognition. It follows the complete workflow from dataset inspection and preprocessing to model design, validation, testing, and reproducible evaluation.

## Human Feedback and Human-AI Interaction

### [CS329H: Machine Learning from Human Preferences](https://web.stanford.edu/class/cs329h/)

**Offering:** Autumn 2025<br>
**Instructors:** Andreas Haupt and Sanmi Koyejo<br>
**Institution:** Stanford University<br>
**Access:** [Course website](https://web.stanford.edu/class/cs329h/) | [Open textbook](https://mlhp.stanford.edu/)<br>
**Materials:** Slides, notebooks, problem sets, coding assignments, and textbook chapters

A graduate course on learning from human preference data across supervised learning, active learning, reinforcement learning, and assistance settings. It covers preference modeling, elicitation, RLHF, aggregation, heterogeneity, interpretation, and privacy.

### [CS329X: Human-Centered LLMs](https://web.stanford.edu/class/cs329x/)

**Offering:** Fall 2025<br>
**Instructor:** Diyi Yang<br>
**Institution:** Stanford University<br>
**Access:** [Course website](https://web.stanford.edu/class/cs329x/)<br>
**Materials:** Lecture slides, readings, assignments, and project guidelines

A course on designing and evaluating language models around human needs. It covers preference alignment, personalization, human-AI interaction, pluralistic values, privacy, anthropomorphism, AI companions, and the future of human work.

## Embodied Intelligence and Humanoid Systems

### [Embodied AI: Perception, Representation, and Action](https://embodied-ai-hku.github.io/DATA8010/)

**Offering:** Spring 2026<br>
**Instructor:** Yanchao Yang<br>
**Institution:** The University of Hong Kong<br>
**Access:** [Course website](https://embodied-ai-hku.github.io/DATA8010/)<br>
**Materials:** Public lecture slides, tutorials, and assignments

A structured course linking classical robotics foundations with learning-based embodied intelligence. It progresses from kinematics, dynamics, control, and planning to multimodal perception, reinforcement learning, imitation learning, generative policies, and vision-language-action models.

### [Introduction to Embodied AI](https://pku-epic.github.io/Intro2EAI_2026/)

**Offering:** Spring 2026<br>
**Instructor:** He Wang<br>
**Institution:** Peking University<br>
**Access:** [Course website](https://pku-epic.github.io/Intro2EAI_2026/) | [Schedule and assignments](https://pku-epic.github.io/Intro2EAI_2026/schedule/)<br>
**Materials:** Public syllabus, assignments, and experiment specifications

A semester-long course covering robotics foundations, pose and grasp estimation, imitation and reinforcement learning, diffusion and language-conditioned policies, humanoid locomotion, vision-language-action models, dexterous manipulation, navigation, and real-world robot experiments.

### [16-264: Humanoids](https://www.cs.cmu.edu/~cga/humanoids-ugrad/)

**Offering:** Spring 2025<br>
**Instructor:** Chris Atkeson<br>
**Institution:** Carnegie Mellon University<br>
**Access:** [Course website](https://www.cs.cmu.edu/~cga/humanoids-ugrad/) | [Instructor video channel](https://www.youtube.com/@cga1959/videos)<br>
**Materials:** Course notes, assignments, project resources, and presentations

A project-centered course spanning humanoid sensing, kinematics, dynamics, control, simulation, learning, recognition, communication, social behavior, safety, and ethics. Its practical format emphasizes building reusable humanoid and agent capabilities.

## Human-Robot Interaction

### [Introduction to Human-Robot Interaction](https://hriclass.cis.cornell.edu/)

**Instructor:** Angelique Taylor<br>
**Institution:** Cornell University<br>
**Access:** [Course website](https://hriclass.cis.cornell.edu/) | [Schedule](https://hriclass.cis.cornell.edu/schedule/) | [Laboratories](https://hriclass.cis.cornell.edu/labs/) | [Readings](https://hriclass.cis.cornell.edu/readings/)<br>
**Materials:** Schedule, readings, ROS laboratories, and project specifications

A full course on computational, engineering, and social approaches to human-robot interaction. It covers perception of people, spatial interaction, emotion and intent, social signals, group interaction, robot design, people tracking, social navigation, and user-study evaluation.

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