Animation I
In industry, learned and generative tools now sit inside the animation pipeline itself, in concept art and previsualization, in assisted inbetweening and keyframe retiming, and in denoisers that cut render iteration, which shifts entry-level animation work toward art direction, review, and correction of output the animator did not hand-key. The course therefore keeps its full craft pipeline intact while adding the judgment, verification, and provenance practices studios now expect of junior artists.
Current description → proposed description
This course will introduce students to 3D computer animation including the end-to-end development process from script/story writing, production planning, creating geometric models and surface properties, designing motion, staging and lighting the action, rendered images with 2D and 3D effects, and editing them into a short film. Open Source software will be used for animation exercises. Throughout the course, existing 2D and 3D movies will be used for learning the techniques and methods of professional animators. The course is designed for students with no previous animation skills and will lead students through a series of exercises that build on each other to learn 2D and 3D animation techniques.
This course introduces 3D computer animation through the complete production pipeline: story and script development, production planning, geometric modeling and surface properties, motion design, staging and lighting the action, rendering with 2D and 3D effects, and editing the result into a short film. Open source software is used for the animation exercises, and existing 2D and 3D movies are studied to learn the techniques and methods of professional animators. The course assumes no previous animation skills and leads students through a series of exercises that build on each other to learn 2D and 3D animation techniques. The course also places machine assistance inside that pipeline: generative concept art and previsualization, assisted inbetweening and retiming, and learned denoisers that shorten render iteration. Students direct and correct such output rather than accept it, documenting provenance, licensing, and attribution for what they deliver.
What changes
- Vocation and authorship reflection on generative tooling in creative work
- Three machine-assisted stages named inside that pipeline: generative previsualization, assisted inbetweening and retiming, learned denoisers
- Animator positioned as director, reviewer, and corrector of machine-generated output
- Asset provenance, licensing, and attribution documented for delivered work
- Shared GPU time consumed by learned passes treated as a budgeted resource
6 proposed outcomes, mapped to 10 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 short animated film that carries a single story idea through the full production pipeline, from script and storyboard to modeling, surfacing, motion, staging, lighting, rendering, and final edit, directing and correcting the machine-assisted stages of that pipeline and clearing licensing, rights, and attribution for every delivered asset.
Clearing licensing, rights, and attribution for every delivered asset applies law to an assisted production pipeline, and carrying one story idea to a film an audience can follow applies communication, which together are the disciplinary skills PLO 3.2 asks students to bring to a human-centered application built with machine assistance under the student's direction.
Students will be able to design motion that reads clearly for weight, timing, and intent by applying established animation principles to hand-keyed sequences, judging spacing charts, timing charts, and animation curve behavior against the intended read, identifying and correcting the recurring failure patterns that assisted inbetween and retiming passes introduce, and revising the work across successive critique cycles.
Identifying the recurring failure patterns that assisted inbetween and retiming passes introduce is analysis of machine-produced patterns, and judging spacing charts, timing charts, and curve behavior against the intended read supplies the paired quantitative and qualitative reasoning PLO 6.2 names; revising the work across successive critique cycles is the continuous self-assessment and improvement of one's own approach described in PLO 4.3.
Students will be able to evaluate machine-generated production assets, including generative concept art and previsualization, assisted inbetween and retiming passes, and learned render denoisers, against the technical and artistic requirements of a shot, revising or rejecting output that fails review and documenting the basis for each accept-or-reject decision.
Deciding whether a generated plate, assisted inbetween, or denoised render meets shot requirements is the critical analysis of model-driven outcomes called for in PLO 4.1; documenting the basis for each accept-or-reject decision is the explicit interpretation and explanation of AI output, inside an art pipeline, that PLO 6.1 requires.
Students will be able to analyze lighting, staging, and render sampling choices against a target look and a fixed budget of GPU time shared across the class, including the denoiser strength and generative previsualization passes that draw on that budget, selecting settings that reach the look without consuming compute other students depend on.
Budgeting the GPU time that learned denoising and generative previsualization passes consume, and choosing settings that reach the look without exhausting compute the rest of the class depends on, is exactly the responsible use of AI resources and equitable access to them that PLO 2.3 calls for.
Students will be able to articulate the creative and technical decisions behind a sequence in a spoken pitch and dailies-style critique and in written shot notes addressed to a non-specialist audience, disclosing plainly which elements were machine-generated and which were authored by hand.
Presenting the same sequence in a spoken pitch and in written shot notes while stating openly what was machine-generated is the written and spoken communication carried out transparently and with integrity that PLO 5.1 requires; addressing those notes to a non-specialist audience and disclosing where machine assistance was used is the translation of technical AI work for non-specialists, promoting informed use, that PLO 5.3 describes.
Students will be able to critique the use of generative tools in animation by documenting asset provenance, licensing, and attribution for every element delivered, and by articulating the animator's responsibility for work released under their name from a Lutheran Christian understanding of vocation and creative work.
Tracing where a generated asset came from and whether its use is licensed and fairly attributed is the fairness and moral-impact assessment of AI developments in PLO 6.3; reasoning about accountability for creative work signed by the artist, framed by a Christian account of calling and creativity, is the stewardship and vocation reflection in PLO 1.3.
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.