论文标题
人类情感综合中的生成对抗网络：评论
Generative Adversarial Networks in Human Emotion Synthesis:A Review
论文作者
论文摘要
综合现实数据样本对学术和工业社区都具有很高的价值。深层生成模型已成为各个研究领域的新兴主题，例如计算机视觉和信号处理。情感计算是对计算机视觉社会的广泛兴趣的话题，也不例外，并且从生成模型中受益。实际上，情感计算观察到过去二十年来生成模型的快速推导。这种模型的应用包括但不限于情绪识别和分类，单峰情绪综合以及跨模式情绪综合。结果，我们通过研究生成模型的可用数据库，优势和缺点以及考虑两种主要人类交流方式，即音频和视频的相关培训策略，对人类情绪综合的最新进展进行了回顾。在这种情况下，在不同的应用方案下，对面部表达综合，语音情感综合和视听（跨模式）情绪综合进行了广泛的综述。逐渐地，我们讨论了开放的研究问题，以推动该研究领域的界限进行未来的工作。
Synthesizing realistic data samples is of great value for both academic and industrial communities. Deep generative models have become an emerging topic in various research areas like computer vision and signal processing. Affective computing, a topic of a broad interest in computer vision society, has been no exception and has benefited from generative models. In fact, affective computing observed a rapid derivation of generative models during the last two decades. Applications of such models include but are not limited to emotion recognition and classification, unimodal emotion synthesis, and cross-modal emotion synthesis. As a result, we conducted a review of recent advances in human emotion synthesis by studying available databases, advantages, and disadvantages of the generative models along with the related training strategies considering two principal human communication modalities, namely audio and video. In this context, facial expression synthesis, speech emotion synthesis, and the audio-visual (cross-modal) emotion synthesis is reviewed extensively under different application scenarios. Gradually, we discuss open research problems to push the boundaries of this research area for future works.
