A Cyclic Consistency Motion Style Transfer Method Combined with Kinematic Constraints

Abstract

Motion capture technology plays an important role in the production field of film and television, animation, etc. In order to reduce the cost of data acquisition and improve the reuse rate of motion capture data and the effect of movement style migration, the synthesis technology of motion capture data in human movement has become a research hotspot in this field. In this paper, kinematic constraints (KC) and cyclic consistency (CC) network are employed to study the methods of kinematic style migration. Firstly, cycle-consistent adversarial network (CCycleGAN) is constructed, and the motion style migration network based on convolutional self-encoder is used as a generator to establish the cyclic consistent constraint between the generated motion and the content motion, so as to improve the action consistency between the generated motion and the content motion and eliminate the lag phenomenon of the generated motion. Then, kinematic constraints are introduced to normalize the movement generation, so as to solve the problems such as jitter and sliding step in the movement style migration results. Experimental results show that the generated motion of the cyclic consistent style transfer method with kinematic constraints is more similar to the style of style motion, which improves the effect of motion style transfer.

Sensors, Signal, and Artificial Intelligent Processing

“…In similar line, motion capture technology plays an important role in the production field of film and television. In [35], kinematic constraints (KC) and cyclic consistency (CC) network are employed to study the methods of kinematic style migration. Cycle-Consistent Adversarial Network (CCycleGAN) is developed in [35], and the motion style migration network of convolutional self-encoder has been used as a generator to establish the cyclic consistent constraint.…”

“…In [35], kinematic constraints (KC) and cyclic consistency (CC) network are employed to study the methods of kinematic style migration. Cycle-Consistent Adversarial Network (CCycleGAN) is developed in [35], and the motion style migration network of convolutional self-encoder has been used as a generator to establish the cyclic consistent constraint. In order to normalize the movement generation to solve the problems such as jitter and sliding step, the kinematic constraints are concurrently used.…”