Case Study / 02
A workflow study focused on where AI can accelerate beginner animation production, where human intervention remains necessary, and how a Live2D pipeline can stay reliable after automation enters the process.
Overview
This project compares manual and AI-assisted production paths in a Live2D animation workflow. The goal was not simply to prove that AI is faster, but to understand how speed, control, cleanup work, and production trust shift when automation is introduced into a creative pipeline.
The resulting case study frames AI as an operational layer rather than a novelty effect, showing how layer separation, workflow tooling, human review, and production testing need to be staged together.
Project Type
Key Focus
02
A quick read on the key frames, tools, and research logic behind the workflow.
Workflow Comparison
The project makes the contrast between manual and AI-assisted production visible as two parallel stories, revealing where time is saved and where correction work reappears later in the pipeline.
Operational Tooling
The AI layer was built as a usable workflow through ComfyUI and a Discord bot wrapper, making the experiment closer to a production test than a one-off image-generation trial.
Research Framing
The case study is grounded in an A/B experiment with animation students, focusing on efficiency, effort redistribution, and output quality rather than abstract claims about AI.