Nadia Magnenat-Thalmann

Le Ma, Thiago Freitas dos Santos, Nadia Magnenat-Thalmann et al.

In surgical training for medical students, proficiency development relies on expert-led skill assessment, which is costly, time-limited, difficult to scale, and its expertise remains confined to institutions with available specialists. Automated AI-based assessment offers a viable alternative, but progress is constrained by the lack of datasets containing realistic trainee errors and the multi-view variability needed to train robust computer vision approaches. To address this gap, we present Surgical-Hands (SHands), a large-scale multi-view video dataset for surgical hand-gesture and error recognition for medical training. \textsc{SHands} captures linear incision and suturing using five RGB cameras from complementary viewpoints, performed by 52 participants (20 experts and 32 trainees), each completing three standardized trials per procedure. The videos are annotated at the frame level with 15 gesture primitives and include a validated taxonomy of 8 trainee error types, enabling both gesture recognition and error detection. We further define standardized evaluation protocols for single-view, multi-view, and cross-view generalization, and benchmark state-of-the-art deep learning models on the dataset. SHands is publicly released to support the development of robust and scalable AI systems for surgical training grounded in clinically curated domain knowledge.

Hangyeol Kang, Thiago Freitas dos Santos, Maher Ben Moussa et al.

The uncanny valley effect poses a significant challenge in the development and acceptance of hyper-realistic social robots. This study investigates whether advanced conversational capabilities powered by large language models (LLMs) can mitigate this effect in highly anthropomorphic robots. We conducted a user study with 80 participants interacting with Nadine, a hyper-realistic humanoid robot equipped with LLM-driven communication skills. Through pre- and post-interaction surveys, we assessed changes in perceptions of uncanniness, conversational quality, and overall user experience. Our findings reveal that LLM-enhanced interactions significantly reduce feelings of eeriness while fostering more natural and engaging conversations. Additionally, we identify key factors influencing user acceptance, including conversational naturalness, human-likeness, and interestingness. Based on these insights, we propose design recommendations to enhance the appeal and acceptability of hyper-realistic robots in social contexts. This research contributes to the growing field of human-robot interaction by offering empirical evidence on the potential of LLMs to bridge the uncanny valley, with implications for the future development of social robots.

George Papagiannakis, Walter Greenleaf, Michael Cole et al.

Computational Medical Extended Reality (CMXR), brings together life sciences and neuroscience with mathematics, engineering and computer science. It unifies computational science (scientific computing) with intelligent extended reality and spatial computing for the medical field. It significantly differs from previous "Clinical XR" or "Medical XR" terms, as it is focusing on how to integrate computational methods from neural simulation to computational geometry, computational vision and computer graphics with deep learning models to solve specific hard problems in medicine and neuroscience: from low/no-code/genAI authoring platforms to deep learning XR systems for training, planning, operative navigation, therapy and rehabilitation.

Nidhi Mishra, Manoj Ramanathan, Ranjan Satapathy et al.

Hiring robots for the workplaces is a challenging task as robots have to cater to customer demands, follow organizational protocols and behave with social etiquette. In this study, we propose to have a humanoid social robot, Nadine, as a customer service agent in an open social work environment. The objective of this study is to analyze the effects of humanoid robots on customers at work environment, and see if it can handle social scenarios. We propose to evaluate these objectives through two modes, namely, survey questionnaire and customer feedback. We also propose a novel approach to analyze customer feedback data (text) using sentic computing methods. Specifically, we employ aspect extraction and sentiment analysis to analyze the data. From our framework, we detect sentiment associated to the aspects that mainly concerned the customers during their interaction. This allows us to understand customers expectations and current limitations of robots as employees.

Junhui Hou, Lap-Pui Chau, Nadia Magnenat-Thalmann et al.

Low-rank matrix approximation (LRMA) is a powerful technique for signal processing and pattern analysis. However, its potential for data compression has not yet been fully investigated in the literature. In this paper, we propose sparse low-rank matrix approximation (SLRMA), an effective computational tool for data compression. SLRMA extends the conventional LRMA by exploring both the intra- and inter-coherence of data samples simultaneously. With the aid of prescribed orthogonal transforms (e.g., discrete cosine/wavelet transform and graph transform), SLRMA decomposes a matrix into a product of two smaller matrices, where one matrix is made of extremely sparse and orthogonal column vectors, and the other consists of the transform coefficients. Technically, we formulate SLRMA as a constrained optimization problem, i.e., minimizing the approximation error in the least-squares sense regularized by $\ell_0$-norm and orthogonality, and solve it using the inexact augmented Lagrangian multiplier method. Through extensive tests on real-world data, such as 2D image sets and 3D dynamic meshes, we observe that (i) SLRMA empirically converges well; (ii) SLRMA can produce approximation error comparable to LRMA but in a much sparse form; (iii) SLRMA-based compression schemes significantly outperform the state-of-the-art in terms of rate-distortion performance.

Junhui Hou, Lap-Pui Chau, Nadia Magnenat-Thalmann et al.

Due to the growing needs of human motion capture (mocap) in movie, video games, sports, etc., it is highly desired to compress mocap data for efficient storage and transmission. This paper presents two efficient frameworks for compressing human mocap data with low latency. The first framework processes the data in a frame-by-frame manner so that it is ideal for mocap data streaming and time critical applications. The second one is clip-based and provides a flexible tradeoff between latency and compression performance. Since mocap data exhibits some unique spatial characteristics, we propose a very effective transform, namely learned orthogonal transform (LOT), for reducing the spatial redundancy. The LOT problem is formulated as minimizing square error regularized by orthogonality and sparsity and solved via alternating iteration. We also adopt a predictive coding and temporal DCT for temporal decorrelation in the frame- and clip-based frameworks, respectively. Experimental results show that the proposed frameworks can produce higher compression performance at lower computational cost and latency than the state-of-the-art methods.

Junhui Hou, Lap-Pui Chau, Nadia Magnenat-Thalmann et al.

Human motion capture (mocap) is a widely used technique for digitalizing human movements. With growing usage, compressing mocap data has received increasing attention, since compact data size enables efficient storage and transmission. Our analysis shows that mocap data have some unique characteristics that distinguish themselves from images and videos. Therefore, directly borrowing image or video compression techniques, such as discrete cosine transform, does not work well. In this paper, we propose a novel mocap-tailored transform coding algorithm that takes advantage of these features. Our algorithm segments the input mocap sequences into clips, which are represented in 2D matrices. Then it computes a set of data-dependent orthogonal bases to transform the matrices to frequency domain, in which the transform coefficients have significantly less dependency. Finally, the compression is obtained by entropy coding of the quantized coefficients and the bases. Our method has low computational cost and can be easily extended to compress mocap databases. It also requires neither training nor complicated parameter setting. Experimental results demonstrate that the proposed scheme significantly outperforms state-of-the-art algorithms in terms of compression performance and speed.