Abstract
This course covers the core technologies required to model and simulate motions for digital humans. The curriculum includes human body modeling, human motion capture, data-driven human motion synthesis, and ML-based generative models. Each topic will be extensively illustrated with examples to provide a comprehensive understanding of the subject matter.Objectives
Students will learn how to estimate human pose, shape, and motion from videos and create basic human avatars from various visual inputs. Students will also learn how to represent and algorithmically generate motions for digital characters. The lectures are accompanied by exercise sessions and a capstone project.
Content
- Basic concepts of 3D representations- Human body/hand models
- Human motion capture;
- Neural rendering
- Transformers
- Generative models for digital humans
Lecture Notes
Lecture recordings and slides will be available on moodlePrerequisites
Experience with python and C++ programming, numerical linear algebra, multivariate calculus and probability theory. Solid background in deep learning, computer vision, physics-based modeling, kinematics, and dynamics is preferred.Administration
IMPORTANT: The deadline to cancel/deregister from the course is March 19. Deregistration after the deadline will lead to fail.
Since Digital Humans changes to include a session exam this year, the deregistration deadline for the course has been updated to be the deregistration deadline for session exams. Students now have until 28th July 2024, to deregister from the exam without receiving a failed result.
| Number | 263-5806-00L |
|---|---|
| Location and Time | Lecture: Tue 14:15-17:00 CAB G 61 Tutorial: Thu 16:15-18:00 ETF E1 |
| Moodle | Lecture recordings and slides will be available on moodle |
| ECTS Credits | 8 |
| Examination | 50% session exam (written 90 minutes) and 50% final project presentation and report |
Schedule
Lecture
| Week | Date (Wed 14-17pm) | Topic |
|---|---|---|
| 01 | 20-Feb | Introduction |
| 02 | 27-Feb | Human body models |
| 03 | 5-Mar | Model fitting |
| 04 | 12-Mar | Project presentation |
| 05 | 19-Mar | Neural representation |
| 06 | 26-Mar | Neural representation |
| 07 | Easter break | |
| 08 | 9-Apr | Transformer |
| 09 | 16-Apr | Generative Models |
| 10 | 23-Apr | Project presentation |
| 11 | 30-Apr | Generative Models |
| 12 | 7-May | Guest lecture |
| 13 | 14-May | Guest lecture |
| 14 | 21-May | Guest lecture |
| 15 | 28-May | Project presentation |
Tutorial
| Week | Date (Thu 16-18pm) | Topic |
|---|---|---|
| 01 | 22-Feb | Project introduction |
| 02 | 29-Feb | Euler cluster introduction |
| 03 | 7-Mar | Project discussion |
| 04 | 14-Mar | Exercise (body model) |
| 05 | 21-Mar | Exercise (model fitting) |
| 06 | 28-Mar | |
| 07 | 4-Apr | |
| 08 | 11-Apr | Exercise (Neural field) |
| 09 | 18-Apr | Exercise (Transformer) |
| 10 | 25-Apr | Project discussion |
| 11 | 2-May | Exercise (VAE) |
| 12 | 9-May | |
| 13 | 16-May | Exercise (Diffusion) |
| 14 | 23-May | |
| 15 | 30-May |
