Artificial Intelligence in Process Animation for 3D Modeling Character: A Comparative Study with the Riggify Technique to Animate Character

Abstract

AI-assisted tools are increasingly used to accelerate motion capture, rigging support, and 3D character animation workflows. However, comparing “AI” as a general concept with Blender Rigify can create ambiguity because AI tools are external platforms, while Rigify is a native Blender rigging add-on. This study therefore compares two clearly defined workflows, namely between an AI-assisted motion-capture workflow represented by Rokoko Vision and the Rokoko Blender Plugin for generating and retargeting motion data, with a Rigify-based workflow in Blender in which rig generation, posing, and refinement remain under animator control. The study uses a comparative workflow-evaluation design supported by procedural observation, literature review, and an evaluation matrix covering animation creation speed, movement quality, retargeting accuracy, detailed control, technical dependency, and production suitability. The findings indicate that the AI-assisted workflow improves speed and accessibility, especially for prototyping and simple body-motion animation, but it remains dependent on video quality, camera angle, lighting, occlusion, and post-processing cleanup. Rigify requires more time and technical skill, yet it offers stronger bone-level control, more reliable refinement, and better suitability for production requiring precise character performance. The main contribution of this study is a clarified comparison framework that separates AI-assisted motion acquisition from Blender-based manual rigging and recommends a hybrid workflow for future 3D animation production.