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Beyond Talking – Generating Holistic 3D Human Dyadic Motion for Communication

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Abstract

In this paper, we introduce an innovative task focused on human communication, aiming to generate 3D holistic human motions for both speakers and listeners. Central to our approach is the incorporation of factorization to decouple audio features and the combination of textual semantic information, thereby facilitating the creation of more realistic and coordinated movements. We separately train VQ-VAEs with respect to the holistic motions of both speaker and listener. We consider the real-time mutual influence between the speaker and the listener and propose a novel chain-like transformer-based auto-regressive model specifically designed to characterize real-world communication scenarios effectively which can generate the motions of both the speaker and the listener simultaneously. These designs ensure that the results we generate are both coordinated and diverse. Our approach demonstrates state-of-the-art performance on two benchmark datasets. Furthermore, we introduce the HoCo holistic communication dataset, which is a valuable resource for future research. Our HoCo dataset and code will be released for research purposes upon acceptance.

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Data availability

We claim to release the dataset and code upon acceptance. The datasets generated and analyzed during the current study will be available in our open-source repository.

Notes

  1. https://www.youtube.com.

  2. https://pypi.org/project/moviepy/.

  3. https://github.com/openai/whisper.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under contract No. 62171256 and Meituan.

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  1. Mingze Sun, Chao Xu and Xinyu Jiang contributed equally to this work.

Authors and Affiliations

  1. Tsinghua Shenzhen International Graduate School, Shenzhen, China

    Mingze Sun, Xinyu Jiang & Ruqi Huang

  2. Alibaba Group, Hangzhou, China

    Chao Xu, Yang Liu & Baigui Sun

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Appendix

Appendix

1.1 Emotion Editing via Our Audio Decoupling

To verify the effectiveness of our audio decoupling method, we design the editing experiment shown in Fig. 9. First, we select two segments of audio from the MEAD dataset, containing happy and sad emotions respectively. The texts are "The revolution now underway in materials handling makes it much easier" and "No, the man was not drunk, he wondered how he got up tied up with a stranger". The generated motion is then shown in the top-left and bottom-left of Fig. 9. Note the opposite directions of arm movement in the left two panels, reflecting the difference in emotion.

Thanks to our audio decoupling design, our generation framework allows for emotion-based editing. We first compute the style feature \(F^A_{happy}, F^A_{sad}\) of the input audio clips. Then we switch the style feature between the two audio clips. Namely, following Eq. 1, we construct:

$$\begin{aligned} F^m_{happy2sad} = [F^A_{sad}; F^W_{happy}], F^f_{happy2sad} = [F^A_{sad}; F^P_{happy}], \end{aligned}$$
(8)

as the input of our trained model. Similarly, we can edit the sad audio clip to a happy one by reversing the above construction.

We remark that, in the above editing, the audio texts are untouched. Top-right of Fig. 9 shows the editing result of making the happy audio sad, and the bottom-right of Fig. 9 shows the opposite. Again, we check the arm movement directions, which confirms the editing effect.

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Sun, M., Xu, C., Jiang, X. et al. Beyond Talking – Generating Holistic 3D Human Dyadic Motion for Communication. Int J Comput Vis 133, 2910–2926 (2025). https://doi.org/10.1007/s11263-024-02300-7

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