Natural gestures are a desirable feature for a humanoid robot, as they are presumed to elicit a more comfortable interaction in people. With this aim in mind, we present in this paper a system to develop a natural talking gesture generation behavior. A Generative Adversarial Network (GAN) produces novel beat gestures from the data captured from recordings of human talking. The data is obtained without the need for any kind of wearable, as a motion capture system properly estimates the position of the limbs/joints involved in human expressive talking behavior. After testing in a Pepper robot, it is shown that the system is able to generate natural gestures during large talking periods without becoming repetitive. This approach is computationally more demanding than previous work, therefore a comparison is made in order to evaluate the improvements. This comparison is made by calculating some common measures about the end effectors’ trajectories (jerk and path lengths) and complemented by the Fréchet Gesture Distance (FGD) that aims to measure the fidelity of the generated gestures with respect to the provided ones. Results show that the described system is able to learn natural gestures just by observation and improves the one developed with a simpler motion capture system. The quantitative results are sustained by questionnaire based human evaluation

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自然手势是类人机器人的一个理想特征，因为它们被认为可以引起人们更舒适的交互。考虑到这一目标，我们在本文中提出了一个系统来开发自然的说话手势生成行为。生成对抗网络 (GAN) 从人类谈话记录中捕获的数据中产生新颖的节拍手势。无需任何类型的可穿戴设备即可获得数据，因为动作捕捉系统可以正确估计涉及人类表达性谈话行为的四肢/关节的位置。在 Pepper 机器人中进行测试后，表明该系统能够在长时间交谈期间生成自然手势而不会变得重复。这种方法在计算上比以前的工作要求更高，因此进行比较以评估改进。这种比较是通过计算关于末端执行器轨迹（加加速度和路径长度）的一些常见测量值进行的，并辅以 Fréchet 手势距离 (FGD)，该距离旨在测量生成的手势相对于提供的手势的保真度。结果表明，所描述的系统能够仅通过观察来学习自然手势，并改进了用更简单的动作捕捉系统开发的系统。定量结果由基于问卷的人工评估维持 结果表明，所描述的系统能够仅通过观察来学习自然手势，并改进了用更简单的动作捕捉系统开发的系统。定量结果由基于问卷的人工评估维持 结果表明，所描述的系统能够仅通过观察来学习自然手势，并改进了用更简单的动作捕捉系统开发的系统。定量结果由基于问卷的人工评估维持

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