Animation II
Machine learning has moved into exactly the stages this course teaches, including automated joint placement and skin-weight prediction, learned deformers and simulation approximations, markerless performance capture, and render denoising, so the character artist's work is shifting from producing every result by hand toward directing, testing, and repairing machine-generated output. The course keeps its identity as a Maya character animation studio course and adds the review, verification, and provenance practices that current production work requires.
Current catalog prerequisites — (CSC 210 or 2800).
Current description → proposed description
In this course, students will continue work begun in CSC 2800 with a deeper exploration of 3D computer animation and introduction of a commercial 3D animation software product, Autodesk Maya. The class is viewed as a logical continuation of CSC 2800. This course explores the core technical and artistic aspects of 3D computer animation. Students will learn character modeling, character rigging, skinning, animation, and lighting using Autodesk Maya.
Prerequisites: (CSC 210 or 2800).
This course continues the work begun in CSC 2800 with a deeper exploration of 3D computer animation and the introduction of a commercial production package, Autodesk Maya. Students work through the stages of character production in turn: modeling with edge flow and topology built for deformation, joint placement and hierarchy, skin weighting and corrective deformation, keyframed performance judged against the established principles of animation, and character lighting and look development for a finished shot, with each stage reviewed in dailies-style critique. Because studio practice now leans on machine assistance at several of these stages, students test automated joint placement and predicted skin weights against hand-authored solutions and repair markerless performance capture, weighing learned deformers and denoisers against the detail a shot requires. The tooling is treated as material to be directed, and the animator remains accountable for the finished result. Students also examine performer likeness and consent, the licensing and provenance of asset libraries and training data, and the worth of human creative authorship from a Lutheran Christian understanding of vocation.
What changes
- Machine-assisted auto-rigging and skin-weight prediction evaluated and repaired, not accepted
- Markerless and video-derived performance capture retargeted and cleaned on a student-built rig
- Learned deformers and render denoisers judged against a render-time allocation shared across the class
- Machine-generated versus hand-authored elements disclosed in rig documentation and dailies critique
- Performer likeness, consent, and asset provenance treated as questions of vocation and authorship
6 proposed outcomes, mapped to 8 program outcomes
Each outcome below is written to be observable and assessable, and each is mapped to the program learning outcomes for which it produces evidence.
Students will be able to construct a deformation-ready character in Autodesk Maya, establishing edge flow and topology, a joint hierarchy, corrective deformers, and an animator-facing control interface that holds across the character's full range of motion and is specified in writing, including the hand-keyed and machine-generated motion sources it accepts, for the animator who will drive it.
PLO 3.2: the CLO requires a control interface designed for a human driver and specified in writing for that person, applying communication craft to a rig that must accept machine-generated motion as well as hand-keyed input, which is the human-centered application work the PLO names. PLO 5.1: the written interface specification is a technical document addressed to a named downstream reader, and stating plainly which motion sources the rig accepts is the transparent written communication the PLO requires.
Students will be able to evaluate the output of machine-assisted rigging tools, including automated joint placement, predicted skin weights, and learned deformers, by testing deformation at the extremes of the pose range, repairing or rejecting results that fail that test, and stating in each case why the predicted solution broke down.
PLO 6.1: the CLO requires the student to state why a predicted skin weight or learned deformer broke down at a given pose, which is interpreting and explaining a machine-produced result rather than accepting it.
Students will be able to analyze markerless and video-derived performance capture retargeted onto the student's own rig, measuring it against joint-curve and contact data to diagnose foot slide, interpenetration, and lost weight, timing, and arcs, and correcting those faults across successive critique passes so that the performance reads as intended.
PLO 6.2: the CLO requires quantitative reading of joint-curve and contact data alongside the qualitative judgment of whether the performance reads as intended, which is exactly the combined analysis the PLO names.
Students will be able to design character lighting and look development for a shot, selecting sampling, simulation, and learned-denoiser settings that reach the intended look within a render-time allocation shared across the class, justifying the student's claim on that queue against the needs of other shots in production.
PLO 2.3: the CLO makes the student justify his or her claim on a render queue shared with classmates against the needs of other shots, which is the equitable access to a limited resource and the ethical resource management the PLO calls for. PLO 6.1: recognizing where a learned denoiser has erased genuine detail instead of noise requires the student to read and explain a machine-produced image result.
Students will be able to articulate the technical and artistic decisions behind a rigged and animated character in dailies-style critique and in written rig documentation for the animators who will inherit it, stating plainly which elements were machine-generated and which were authored by hand.
PLO 5.1: stating plainly what was machine-generated and what was hand-authored, in both spoken critique and written documentation, is the transparency and integrity the PLO requires of professional communication.
Students will be able to critique the use of performance capture and generative tooling in character work, addressing performer likeness and consent, the licensing and provenance of asset libraries and training data, and the animator's responsibility for work released under his or her name, in light of a Lutheran Christian view of vocation and creative authorship.
PLO 6.3: weighing whose likeness and whose prior work a generative or capture pipeline consumes is an assessment of fairness and moral impact in a specific production setting. PLO 1.2: performer consent, disclosure of synthetic elements, and accountability for a delivered shot are the privacy, transparency, and accountability questions the PLO places at the center of practice. PLO 1.3: treating creative authorship as something the animator answers for, rather than a step to be outsourced, frames the work as a calling rather than a technique.
Program outcomes this course reaches
Filled cells are program learning outcomes with at least one supporting course learning outcome in this course. Sparse coverage is expected — no single course carries all eighteen.