Yuk Hang Tsui

Ching Christie Pang, Xuetong Wang, Yuk Hang Tsui et al.

Online knowledge communities (OKC) such as Stack Exchange, Reddit, and Zhihu have long functioned as socio technical infrastructures for collective problem solving. The rapid adoption of Generative AI (GenAI) introduces both complementarity and substitution. Large language models (LLMs) offer faster, more accessible drafts, yet divert traffic and contributions away from OKC that also provided their training data. To understand how communities adapt under this systemic shock, we report a mixed-methods study combining an online survey (N=217) and interviews with 11 current users. Findings show that while users increasingly rely on AI for convenience, they still turn to OKC for complex, ambiguous, or trust sensitive questions. Participants express polarized attitudes toward AI, reflecting divergent hopes and uncertainties about its role. Yet across perspectives, sustaining sociability, empathy, and reciprocity emerges as essential for community resilience. We argue that GenAI's impact constitutes not a terminal decline but a design challenge: to reimagine socio-technical complementarities that balance automation's efficiency with human judgment, trust, and collective stewardship in the evolving knowledge commons. To decline or sustain, it is now or never to take action.

Zhizhuo Yin, Yuk Hang Tsui, Pan Hui

Generating full-body human gestures encompassing face, body, hands, and global movements from audio is a valuable yet challenging task in virtual avatar creation. Previous systems focused on tokenizing the human gestures framewisely and predicting the tokens of each frame from the input audio. However, one observation is that the number of frames required for a complete expressive human gesture, defined as granularity, varies among different human gesture patterns. Existing systems fail to model these gesture patterns due to the fixed granularity of their gesture tokens. To solve this problem, we propose a novel framework named Multi-Granular Gesture Generator (M3G) for audio-driven holistic gesture generation. In M3G, we propose a novel Multi-Granular VQ-VAE (MGVQ-VAE) to tokenize motion patterns and reconstruct motion sequences from different temporal granularities. Subsequently, we proposed a multi-granular token predictor that extracts multi-granular information from audio and predicts the corresponding motion tokens. Then M3G reconstructs the human gestures from the predicted tokens using the MGVQ-VAE. Both objective and subjective experiments demonstrate that our proposed M3G framework outperforms the state-of-the-art methods in terms of generating natural and expressive full-body human gestures.