Mr. Chunzhi Gu Profile

Chunzhi Gu

at Univ of Fukui

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Proceedings Article | 1 May 2022 Paper

Measuring speaking time from privacy-preserving videos

Shun Maeda, Chunzhi Gu, Chao Zhang

Proceedings Volume 12177, 121773H (2022) https://doi.org/10.1117/12.2625956

KEYWORDS: Source localization, Visual process modeling, Feature extraction

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The ongoing pandemic caused by the COVID-19 virus is challenging many aspects of daily life such as restricting the conversation time. A vision-based face analyzing system is considerable for measuring and managing the person-wise speaking time, however, pointing a camera to people directly would be offensive and intrusive. In addition, privacy contents such as the identifiable face of the speakers should not be recorded during measuring. In this paper, we adopt a deep multimodal clustering method, called DMC, to perform unsupervised audiovisual learning for matching preprocessed audio with corresponding locations at videos. We set the camera above the speakers, and by feeding a pair of captured audio and visual data to a pre-trained DMC, a series of heatmaps that identify the location of the speaking people can be generated. Eventually, the speaking time measurement of each speaker can be achieved by accumulating the lasting speaking time of the corresponding heatmap.

Proceedings Article | 1 May 2022 Paper

Diversity-promoting human motion interpolation via conditional variational auto-encoder

Chunzhi Gu, Shuofeng Zhao, Chao Zhang

Proceedings Volume 12177, 121773F (2022) https://doi.org/10.1117/12.2625851

KEYWORDS: Motion models, Neural networks

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In this paper, we present a deep generative model based method to generate diverse human motion interpolation results. We resort to the conditional variational auto-encoder (CVAE) to learn human motion conditioned on a pair of given start and end motions, by leveraging the recurrent neural network (RNN) structure for both the encoder and the decoder. Additionally, we introduce a regularization loss to further promote sample diversity. Once trained, our method is able to generate multiple plausible coherent motions by repetitively sampling from the learned latent space. Experiments on the publicly available dataset demonstrate the effectiveness of our method, in terms of sample plausibility and diversity.

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