Tri Tung Nguyen Nguyen

Quang Tien Dam, Tri Tung Nguyen Nguyen, Yuki Endo et al.

Accurate pain expression synthesis is essential for improving clinical training and human-robot interaction. Current Robotic Patient Simulators (RPSs) lack realistic pain facial expressions, limiting their effectiveness in medical training. In this work, we introduce PainDiffusion, a generative model that synthesizes naturalistic facial pain expressions. Unlike traditional heuristic or autoregressive methods, PainDiffusion operates in a continuous latent space, ensuring smoother and more natural facial motion while supporting indefinite-length generation via diffusion forcing. Our approach incorporates intrinsic characteristics such as pain expressiveness and emotion, allowing for personalized and controllable pain expression synthesis. We train and evaluate our model using the BioVid HeatPain Database. Additionally, we integrate PainDiffusion into a robotic system to assess its applicability in real-time rehabilitation exercises. Qualitative studies with clinicians reveal that PainDiffusion produces realistic pain expressions, with a 31.2% (std 4.8%) preference rate against ground-truth recordings. Our results suggest that PainDiffusion can serve as a viable alternative to real patients in clinical training and simulation, bridging the gap between synthetic and naturalistic pain expression. Code and videos are available at: https://damtien444.github.io/paindf/

Tri Tung Nguyen Nguyen, Quang Tien Dam, Dinh Tuan Tran et al.

Effective human behavior modeling is critical for successful human-robot interaction. Current state-of-the-art approaches for predicting listening head behavior during dyadic conversations employ continuous-to-discrete representations, where continuous facial motion sequence is converted into discrete latent tokens. However, non-verbal facial motion presents unique challenges owing to its temporal variance and multi-modal nature. State-of-the-art discrete motion token representation struggles to capture underlying non-verbal facial patterns making training the listening head inefficient with low-fidelity generated motion. This study proposes a novel method for representing and predicting non-verbal facial motion by encoding long sequences into a sparse sequence of keyframes and transition frames. By identifying crucial motion steps and interpolating intermediate frames, our method preserves the temporal structure of motion while enhancing instance-wise diversity during the learning process. Additionally, we apply this novel sparse representation to the task of listening head prediction, demonstrating its contribution to improving the explanation of facial motion patterns.