Investigation Of Motion Analysis Techniques For Animation Evaluation And Improvement

Abstract

The assessment and enhancement of animation quality heavily relies on motion analysis. This study looks into several motion analysis methods for assessing and improving animation. The goal is to find practical methods that may be used to evaluate the expressiveness, fluidity, and realism of animated characters and then enhance their motion. The study starts off with a thorough literature review that examines a variety of motion analysis methods used in the world of animation. These methods comprise motion capture, position estimation, key frame analysis, physics-based simulation, and machine learning-based methods. Each technique's benefits and drawbacks are analysed, as well as how well it works with various types of animation and settings. As part of the research process, motion data is gathered from a variety of animated sequences, and the identified motion analysis methodologies are then used to evaluate the data. Performance indicators including joint angles, timing, and trajectory are assessed and contrasted with predetermined benchmarks or data on human motion. The visual appeal and plausibility of the animations are also evaluated through perception research involving human viewers. Recommendations are offered for enhancing animation workflows and techniques based on the findings. These suggestions include improving the present motion capture pipelines, introducing machine learning methods for motion prediction and synthesis, and incorporating more precise physics-based models.