|
| 1.
| [1]
understand the motivation and principles for character animation, as well as the skills for implementing such techniques |
| 2.
| [2]
understand the motivation and principles of physical simulation |
| 3.
| [3]
understand the motivation and techniques for learning character animation and physical simulation, implement solutions to learn from character motion data or physical animation data |
Mapping from Course Learning Outcomes to Programme Learning Outcomes
| | PLO a | PLO b | PLO c | PLO d | PLO e | PLO f | PLO g | PLO h | PLO i | PLO j |
| CLO 1 | T | T | | | | | | | | T |
| CLO 2 | T | | T | T | | | | | | |
| CLO 3 | T | | | T | | | | | T | |
T - Teach, P - Practice
For BEng(CompSc) Programme Learning Outcomes, please refer to
here.
|
|
Calendar Entry:
Basics of character animation, keyframe animation, motion capture, inverse kinematics, physically based character animation, Basics of physically-based animation, rigid body dynamics, point-based dynamics, hair animation, cloth simulation, facial animation, crowd simulation, mesh-shape editing, performance capture, skinning, data-driven character control, data-driven cloth animation, data-driven facial animation, data-driven skinning.
|
|
Detailed Description:
| Introduction |
Mapped to CLOs
|
| Course Introduction:
Overview of character animation,
overview of physical simulation,
overview of shape deformation,
Introduction to Python/NumPy.
| 1, 2, 3 |
| Character Animation |
Mapped to CLOs
|
| Character animation: Keyframe animation, motion capture, skinning, forward kinematics, inverse kinematics | 1 |
| Facial animation: Blendshapes, musculoskeletal models, action units, face rigs, keyframe animation, video based capture
| 1 |
| hand animation: video-based hand motion capture, hand rigging, collision detection, response
| 1 |
| physically-based animation by pd control: Pd-control
| 1 |
| crowd animation: Boids, reciprocal velocity obstacles, flow-based approaches
| 1 |
| Data driven character animation |
Mapped to CLOs
|
| Learning full-body character motion: Learning by LSTM, PFNN, local motion phase, RL for high level control
| 1, 3 |
| Learning by track reference motion: reinforcement learning, deep reinforcement learning, learning physic-based animation
| 1, 3 |
| learning facial animation: learning full face motion synthesis from sparse key point motion
| 1, 3 |
| Learning crowd motion: Crowd animation synthesis by RL
| 1, 3 |
| Physics based animation |
Mapped to CLOs
|
| Point based dynamics: Cloth modeling, fluid modeling
| 2 |
| Finite element model: Deformable models
| 2 |
| Rigid body dynamics: Collision detection and response
| 2 |
| Eulerian physics-based animation:
MPM-based simulation,
Eulerian fluid simulation
| 2 |
| Learning Physics-based animation |
Mapped to CLOs
|
| Learning physics by point-based methods | 2, 3 |
| Learning shape deformation | 2, 3 |
| Learning cloth deformation,
Learning hair animation
| 2, 3 |
|
