5 citations · 5 across the 8 of their papers we have counts for
10 papers · 1 filter
SynthGait-19K: A Physically Grounded Synthetic Video Dataset for Gait Parameter Estimation
Soroush Mehraban, Xin Lei Lin, Vida Adeli +5
Accurate estimation of clinically meaningful gait parameters from monocular video is important for scalable mobility assessment, yet progress is limited by the small scale, restric…
Puppeteer: Object-Grounded Posture-Aware Co-Speech Gesture Generation
Vida Adeli, Soroush Mehraban, Jacob Rommann +3
Generating co-speech gestures that are temporally coherent, semantically aligned with speech, and grounded with surrounding objects remains challenging. Prior speech-driven gesture…
FastHMR: Accelerating Human Mesh Recovery via Token and Layer Merging with Diffusion Decoding
Soroush Mehraban, Andrea Iaboni, Babak Taati
Recent transformer-based models for 3D Human Mesh Recovery (HMR) have achieved strong performance but often suffer from high computational cost and complexity due to deep transform…
CARE-PD: A Multi-Site Anonymized Clinical Dataset for Parkinson's Disease Gait Assessment
Vida Adeli, Ivan Klabucar, Javad Rajabi +25
Objective gait assessment in Parkinson's Disease (PD) is limited by the absence of large, diverse, and clinically annotated motion datasets. We introduce CARE-PD, the largest publi…
PickStyle: Video-to-Video Style Transfer with Context-Style Adapters
Soroush Mehraban, Vida Adeli, Jacob Rommann +4
We address the task of video style transfer with diffusion models, where the goal is to preserve the context of an input video while rendering it in a target style specified by a t…
Pain in 3D: Generating Controllable Synthetic Faces for Automated Pain Assessment
Xin Lei Lin, Soroush Mehraban, Abhishek Moturu +1
Automated pain assessment from facial expressions is crucial for non-communicative patient. Progress has been limited by two challenges: (i) existing datasets exhibit severe demogr…
GAITGen: Disentangled Motion-Pathology Impaired Gait Generative Model -- Bringing Motion Generation to the Clinical Domain
Vida Adeli, Soroush Mehraban, Majid Mirmehdi +6
Gait analysis is crucial for the diagnosis and monitoring of movement disorders like Parkinson's Disease. While computer vision models have shown potential for objectively evaluati…
STARS: Self-supervised Tuning for 3D Action Recognition in Skeleton Sequences
Soroush Mehraban, Mohammad Javad Rajabi, Andrea Iaboni +1
Self-supervised pretraining methods with masked prediction demonstrate remarkable within-dataset performance in skeleton-based action recognition. However, we show that, unlike con…
Benchmarking Skeleton-based Motion Encoder Models for Clinical Applications: Estimating Parkinson's Disease Severity in Walking Sequences
Vida Adeli, Soroush Mehraban, Irene Ballester +4
This study investigates the application of general human motion encoders trained on large-scale human motion datasets for analyzing gait patterns in PD patients. Although these mod…
MotionAGFormer: Enhancing 3D Human Pose Estimation with a Transformer-GCNFormer Network
Soroush Mehraban, Vida Adeli, Babak Taati
Recent transformer-based approaches have demonstrated excellent performance in 3D human pose estimation. However, they have a holistic view and by encoding global relationships bet…