The study of complex human interactions and group activities has become a focal point in human-centric computer vision. However, progress in related tasks is often hindered by the challenges of obtaining large-scale labeled datasets from real-world scenarios. To address the limitation, we introduce M3 Act, a synthetic data generator for multi-view multi-group multi-person human atomic actions and group activities. Powered by Unity Engine, M3 Act features mul-tiple semantic groups, highly diverse and photorealistic images, and a comprehensive set of annotations, which facilitates the learning of human-centered tasks across single-person, multi-person, and multi-group conditions. We demonstrate the advantages of M3 Act across three core experiments. The results suggest our synthetic dataset can significantly improve the performance of several downstream methods and replace real-world datasets to reduce cost. Notably, M3 Act improves the state-of-the-art MOTRv2 on DanceTrack dataset, leading to a hop on the leaderboard from 10thto 2ndplace. Moreover, M3 Act opens new research for controllable 3D group activity generation. We define multiple metrics and propose a competitive baseline for the novel task. Our code and data are available at our project page: http://cjerry1243.github.io/M3Act.

Digital storytelling, essential in entertainment, education, and marketing, faces challenges in generation efficiency. The StoryAgent framework, introduced in this paper, utilizes Large Language Models and generative tools to automate and refine digital storytelling. Employing a top-down story drafting and bottom-up asset generation approach, StoryAgent tackles key issues such as manual intervention, interactive scene orchestration, and narrative consistency. This framework enables efficient production of interactive and consistent digital storytellings across multiple modalities, democratizing content creation and enhancing engagement.

Previous studies regarding the perception of emotions for embodied virtual agents have shown the effectiveness of using virtual characters in conveying emotions through interactions with humans. However, creating an autonomous embodied conversational agent with expressive behaviors presents two major challenges. The first challenge is the difficulty of synthesizing the conversational behaviors for each modality that are as expressive as real human behaviors. The second challenge is that the affects are modeled independently, which makes it difficult to generate multimodal responses with consistent emotions across all modalities. In this work, we propose a conceptual framework, ACTOR (Affect-Consistent mulTimodal behaviOR generation), that aims to increase the perception of affects by generating multimodal behaviors conditioned on a consistent driving affect. We have conducted a user study with 199 participants to assess how the average person judges the affects perceived from multimodal behaviors that are consistent and inconsistent with respect to a driving affect. The result shows that among all model conditions, our affect-consistent framework receives the highest Likert scores for the perception of driving affects. Our statistical analysis suggests that making a modality affect-inconsistent significantly decreases the perception of driving affects. We also observe that multimodal behaviors conditioned on consistent affects are more expressive compared to behaviors with inconsistent affects. Therefore, we conclude that multimodal emotion conditioning and affect consistency are vital to enhancing the perception of affects for embodied conversational agents.

This paper describes the IVI Lab entry to the GENEA Challenge 2022. We formulate the gesture generation problem as a sequence-to-sequence conversion task with text, audio, and speaker identity as inputs and the body motion as the output. We use the Tacotron2 architecture as our backbone with the locality-constraint attention mechanism that guides the decoder to learn the dependencies from the neighboring latent features. The collective evaluation released by GENEA Challenge 2022 indicates that our two entries (FSH and USK) for the full body and upper body tracks statistically outperform the audio-driven and text-driven baselines on both two subjective metrics. Remarkably, our full-body entry receives the highest speech appropriateness (60.5% matched) among all submitted entries. We also conduct an objective evaluation to compare our motion acceleration and jerk with two autoregressive baselines. The result indicates that the motion distribution of our generated gestures is much closer to the distribution of natural gestures.

Abstract 3D facial animation synthesis from audio has been a focus in recent years. However, most existing literature works are designed to map audio and visual content, providing limited knowledge regarding the relationship between emotion in audio and expressive facial animation. This work generates audio‐matching facial animations with the specified emotion label. In such a task, we argue that separating the content from audio is indispensable—the proposed model must learn to generate facial content from audio content while expressions from the specified emotion. We achieve it by an adaptive instance normalization module that isolates the content in the audio and combines the emotion embedding from the specified label. The joint content‐emotion embedding is then used to generate 3D facial vertices and texture maps. We compare our method with state‐of‐the‐art baselines, including the facial segmentation‐based and voice conversion‐based disentanglement approaches. We also conduct a user study to evaluate the performance of emotion conditioning. The results indicate that our proposed method outperforms the baselines in animation quality and expression categorization accuracy.

In this study, a real-time wireless embedded EEG-based brain computer interface (BCI) system was developed for drowsiness detection in a realistic driving task. Accidents caused by driver's drowsiness behind the steering wheel have a high fatality rate because of the marked decline in the driver's abilities of perception, recognition, and vehicle control abilities while sleepy. Therefore, real-time drowsiness monitoring is important to avoid traffic accidents. In this study, an embedded EEG-based BCI system which includes a wireless physiological signal acquisition module and an embedded signal processing module was designed, and a real-time drowsiness detection algorithm based on our unsupervised approach was implemented in the embedded signal processing module. EEG signal would be monitored and analyzed by the embedded signal processing module, and the warning tone would be triggered to prevent traffic accidents when the drowsiness condition occurred.