Meng Ma

Minghui Wu, Chenxu Zhao, Anyang Su et al.

Understanding of video creativity and content often varies among individuals, with differences in focal points and cognitive levels across different ages, experiences, and genders. There is currently a lack of research in this area, and most existing benchmarks suffer from several drawbacks: 1) a limited number of modalities and answers with restrictive length; 2) the content and scenarios within the videos are excessively monotonous, transmitting allegories and emotions that are overly simplistic. To bridge the gap to real-world applications, we introduce a large-scale Subjective Response Indicators for Advertisement Videos dataset, namely SRI-ADV. Specifically, we collected real changes in Electroencephalographic (EEG) and eye-tracking regions from different demographics while they viewed identical video content. Utilizing this multi-modal dataset, we developed tasks and protocols to analyze and evaluate the extent of cognitive understanding of video content among different users. Along with the dataset, we designed a Hypergraph Multi-modal Large Language Model (HMLLM) to explore the associations among different demographics, video elements, EEG, and eye-tracking indicators. HMLLM could bridge semantic gaps across rich modalities and integrate information beyond different modalities to perform logical reasoning. Extensive experimental evaluations on SRI-ADV and other additional video-based generative performance benchmarks demonstrate the effectiveness of our method. The codes and dataset will be released at https://github.com/mininglamp-MLLM/HMLLM.

Wenwen Zhang, Arvin Tashakori, Zenan Jiang et al.

The kinematics of human movements and locomotion are closely linked to the activation and contractions of muscles. To investigate this, we present a multimodal dataset with benchmarks collected using a novel pair of Intelligent Knee Sleeves (Texavie MarsWear Knee Sleeves) for human pose estimation. Our system utilizes synchronized datasets that comprise time-series data from the Knee Sleeves and the corresponding ground truth labels from the visualized motion capture camera system. We employ these to generate 3D human models solely based on the wearable data of individuals performing different activities. We demonstrate the effectiveness of this camera-free system and machine learning algorithms in the assessment of various movements and exercises, including extension to unseen exercises and individuals. The results show an average error of 7.21 degrees across all eight lower body joints when compared to the ground truth, indicating the effectiveness and reliability of the Knee Sleeve system for the prediction of different lower body joints beyond the knees. The results enable human pose estimation in a seamless manner without being limited by visual occlusion or the field of view of cameras. Our results show the potential of multimodal wearable sensing in a variety of applications from home fitness to sports, healthcare, and physical rehabilitation focusing on pose and movement estimation.

Séverine Habert, Ma Meng, Pascal Fallavollita et al.

Medical Mixed Reality helps surgeons to contextualize intraoperative data with video of the surgical scene. Nonetheless, the surgical scene and anatomical target are often occluded by surgical instruments and surgeon hands. In this paper and to our knowledge, we propose a multi-layer visualization in Medical Mixed Reality solution which subtly improves a surgeon's visualization by making transparent the occluding objects. As an example scenario, we use an augmented reality C-arm fluoroscope device. A video image is created using a volumetric-based image synthesization technique and stereo-RGBD cameras mounted on the C-arm. From this synthesized view, the background which is occluded by the surgical instruments and surgeon hands is recovered by modifying the volumetric-based image synthesization technique. The occluding objects can, therefore, become transparent over the surgical scene. Experimentation with different augmented reality scenarios yield results demonstrating that the background of the surgical scenes can be recovered with accuracy between 45%-99%. In conclusion, we presented a solution that a Mixed Reality solution for medicine, providing transparency to objects occluding the surgical scene. This work is also the first application of volumetric field for Diminished Reality/ Mixed Reality.