Creating Distortion Frames in 2-D Animation via Motion Tracking and Trajectory Analysis

Traditional animation necessitates the use of squash and stretch principles, where animators identify motion extremes and apply deformations to create believable character movement. However, the process is labor-intensive, requiring expertise and time investment. Current computer vision techniques allow for the creation of in-between frames in animation, but there is a gap in automated systems that can intelligently apply creative distortions such as squash and stretch based on motion analysis. This study presents an artificial-intelligence system that detects moving objects in animation sequences and applies contextually appropriate squash and stretch deformation. The methodology combines background subtraction using MOG2 for object detection, trajectory analysis for motion extreme identification, and physics-based deformation calculation. The system analyzes motion patterns to identify three key animation movements; peaks, impacts, and direction changes. Deformation parameters were calculated based on motion intensity, with squash factors ranging from 0.3 to 2.0. The processed animations showed enhanced visual appeal, properly applying squash and stretch effects, emphasizing dynamic movement while maintaining object volume. The system provides an enhancement to existing animator workflows, reducing the effort needed in planning squash or stretch while retaining high-quality animation.