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.

中文翻译：

---

一种结合运动学约束的循环一致性运动风格转换方法

动作捕捉技术在影视、动画等制作领域发挥着重要作用。 为了降低数据采集成本，提高动作捕捉数据的复用率和运动风格迁移的效果，综合技术人体运动中的动作捕捉数据已成为该领域的研究热点。在本文中，运动学约束（KC）和循环一致性（CC）网络被用来研究运动学风格迁移的方法。首先构建循环一致性对抗网络（CCycleGAN），以基于卷积自编码器的运动风格迁移网络为生成器，在生成的运动和内容运动之间建立循环一致性约束，从而提高生成动作与内容动作的动作一致性，消除生成动作的滞后现象。然后，引入运动学约束对运动生成进行归一化，以解决运动风格迁移结果中的抖动和滑动步长等问题。实验结果表明，具有运动学约束的循环一致风格迁移方法生成的运动更接近于风格运动的风格，提高了运动风格迁移的效果。