Analysis of Motion Retargeting Techniques for Character Animation Transfer

Abstract

Transferring motion from a source character to a target character is known as "motion retargeting," and it is a crucial operation in character animation. It is essential to many applications, including computer-generated movies, virtual reality, and video games. This study examines the benefits, drawbacks, and prospective uses of motion retargeting algorithms for character animation transfer. The examination covers a variety of motion retargeting strategies, including deep learning-based techniques, motion graphs, forward kinematics, and inverse kinematics. Each method is rated according to how well it maintains the desirable aspects of the source motion while modifying it to fit the skeletal structure and dimensions of the target character. The examination also looks at the various strategies computing effectiveness and usability while taking into account the demands of real-time applications and user interaction. It also looks at the difficulties and unanswered concerns surrounding motion retargeting, including how to handle complicated character deformations, manage touch and interaction, and enhance generalisation among characters with various anatomical variations. For academics and industry professionals involved in character animation, the analyses conclusions offer useful insights. Developers can select the most effective solution for their unique requirements by considering the advantages and disadvantages of different motion retargeting strategies. The paper present prospective research avenues that may be pursued in order to overcome current obstacles and develop the state-of-the-art in motion retargeting for character animation transfer. This paper present methodology offers a versatile and effective motion retargeting strategy, which advances character animation. It helps animators and developers save time and effort by enabling them to quickly prototype animations for characters with different morphologies. Our tool's automatic features guarantee a streamlined workflow, allowing users to concentrate on the creative aspects rather than the motion retargeting's technical complexities.