Virtual human is widely employed in various industries, including personal assistance, intelligent customer service, and online education, thanks to the rapid development of artificial intelligence. An anthropomorphic digital human can quickly contact people and enhance user experience in human–computer interaction. Hence, we design the human–computer interaction system framework, which includes speech recognition, text-to-speech, dialogue systems, and virtual human generation. Next, we classify the model of talking-head video generation by the virtual human deep generation framework. Meanwhile, we systematically review the past five years’ worth of technological advancements and trends in talking-head video generation, highlight the critical works and summarize the dataset.

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This paper proposes a neural generative architecture, namely NLDT, to generate a natural language short text describing a table which has formal structure and valuable information. Specifically, the architecture maps fields and values of a table to continuous vectors and then generates a natural language description by leveraging the semantics of a table. The NLDT architecture adopts a two-level neural model to make the most of the structure of a table to fully express the relationship between contents. To deal with the problem of out-of-vocabulary, this paper develops a simple and fast word-conversion method that replaces rare words appearing in texts with common field information in tables and directly replicates contents from table to the output sequence according to the field information. Besides, this paper adds the concept of theme to adapt the NLDT architecture to open domain and improves beam search algorithm to strengthen the results in the inference stage. On the WEATHERGOV dataset, the NLDT architecture improves the state-of-the-art BLEU-4 score from 61.01 to 62.89 and the current state-of-the-art F1 score from 73.21 to 78. On the WIKIBIO and WIKITABLE datasets, the NLDT architecture achieves a BLEU-4 score of 45.77 and 38.71 respectively which also outperform the state-of-the-art approaches. Furthermore, this paper introduces a Chinese dataset WIKIBIOCN including 33,244 biographies with corresponding tables. On the WIKIBIOCN dataset, the NLDT architecture achieves a BLEU-4 score of 38.87 and fairly good manual evaluation.

Virtual simulation can solve the challenges of high cost, long cycle time, and inaccessibility in traditional experimental teaching, which is far-reaching for talent training. This study combines bibliometric visualization theory with AHP (Analytic Hierarchy Process). It establishes a hierarchical evaluation model of a virtual simulation experimental teaching platform based on 842 questionnaires and 4787 articles, including 68,306 citation records, and deconstructing the complex evaluation problem into several multidimensional factors by attributes and relationships. Based on this, a virtual simulation experimental teaching platform construction scheme for IP protocol analysis based on a network covert communication perspective is outputted, which is compatible with the research results. The experimental platform takes a task-driven teaching method as the core, mainly including four modules of context creation, task determination, independent learning, and effect evaluation. The experience of building this platform can be extended to other disciplines, leading the teaching reform exploration of practice-based, innovation-focused, and engineering-critical, helping to implement the flipped classroom, and promoting the development of education modernization.