In this paper, we propose a generative model which learns the relationship between language and human action in order to generate a human action sequence given a sentence describing human behavior. The proposed generative model is a generative adversarial network (GAN), which is based on the sequence to sequence (SEQ2SEQ) model. Using the proposed generative network, we can synthesize various actions for a robot or a virtual agent using a text encoder recurrent neural network (RNN) and an action decoder RNN. The proposed generative network is trained from 29,770 pairs of actions and sentence annotations extracted from MSR-Video-to-Text (MSR-VTT), a large-scale video dataset. We demonstrate that the network can generate human-like actions which can be transferred to a Baxter robot, such that the robot performs an action based on a provided sentence. Results show that the proposed generative network correctly models the relationship between language and action and can generate a diverse set of actions from the same sentence.

本文提出了一种基于生成对抗网络 (GAN) 的生成模型，通过学习自然语言和人类行为之间的关系，在给定人类行为描述的句子的情况下生成一个人类行为序列；使用这个模型，可以使用文本编码器 RNN 和行为解码器 RNN 为机器人或虚拟代理人合成各种操作， 并通过大规模视频数据集 MSR-Video-to-Text (MSR-VTT) 进行训练，生成能够转移到 Baxter 机器人上的人类操作。结果表明，该生成对抗网络正确地建立了语言和行为之间的关系，可以从同一句子中生成不同的动作。

Text2Action: 从语言到行为的生成对抗合成