Logistics
Learning Resources
Readings
Computer Vision
- Foundations of Computer Vision. Antonio Torralba, Phillip Isola and William T. Freeman
- Hartley and Zisserman, Multiple View Geometry in Computer Vision, 2nd edition
Physics Modeling
Online Courses
3D Vision
- Shubham Tulsiani ‘s class: Learning for 3D Vision
- Vincent Sitzmann’s class: Machine Learning for Inverse Graphics
Physical Modeling
- David I.W. Levin’s class: Physics-based Animation
- Stelian Coros’s class: Simulation Methods for Animation and Digital Fabrication
- Minchen Li’s class: Physics-based Animation of Solids and Fluids
Physics Informed Machine Learning
Communication
Discord is intended for all announcements, general questions about the course, clarifications about assignments, student questions to each other, discussions about material, and so on.
Grading
This class requirements include 3 homework assignments (30% of the grade), a final project presentation and participation (70% of the grade) . Each assignment takes 10% of the grade. Every student should work on the assignments individually. The requirements of the final project include a project proposal presentation (10% of the grade), two in-person discussion with the instructor (2 * 10% of the grade), and a poster presentation (40% of the grade). In-person participation of these presentations / discussions are required.
Late Policy of Homework Assignments
Throughout the whole semester, students should submit their assignments before the deadline. The score of the assignment will be multiplied by 0.9 for each additional day of delay. The submission deadline is based on Taiwan’s time zone
Grading policy of final project presentations
The proposal presentations will be scored by the instructor and TAs:
$\mathrm{score} = \frac{1}{N+1} \mathrm{score_{tw}} + \frac{1}{N+1} \sum_{i}^{N} \mathrm{score_{TA, i}}$
The poster presentations will be scored by the instructor, TAs and the students:
$\mathrm{score} = \frac{1}{N+2} \mathrm{score_{tw}} + \frac{1}{N+2} \sum_{i}^{N} \mathrm{score_{TA, i}} + \frac{1}{N+2} \frac{1}{M} \sum_{j}^{M} \mathrm{score_{student, j}}$
Homework Assignments
You are encouraged to discuss with others, but do not share your codes with them! If you wrote the same code as others, you may waive the penalty by refactoring your code in-person within limited time, or otherwise, you’ll get 15% total grade penalty (for each assignment).
Please list your collaborator in the appendix of each assignment
You are allowed to use AIs at your own risk. You are responsible for refactoring the code snippets generated by AIs. You’ll get the penalty as long as your submitted codes are the same as others.
Final Project Presentations
You can form a team of 3-4 members. If you really want to work alone, come and chat with us.
You will submit a proposal, describing the topic, experimental setup, todos and expected contribution of each member for the final project.
All members need to code. You are encouraged to create a github repo, keeping track the contributions of each member. If you found piggybackers on your team, come to discuss with us along with your github repo.