75 citations · 88 across the 11 of their papers we have counts for
6 papers · 1 filter
Loss-Invariant Projections as Passive Probes of Learned Representations
Akshay Chandrasekhar, Pavlo Melnyk
Learned feature representations in neural networks often contain structure beyond that directly used by the final task output. We study this structure using $\textit{passive probes…
CQF-HMR: Continuous Quaternion Flows for Probabilistic 3D Human Mesh Recovery from a Single Image
Cuong Le, Bao-Long Tran, Pavlo Melnyk +3
Recovering 3D digital humans from a single 2D image is an ill-posed computer vision problem due to the loss of depth information. Probabilistic 3D human pose estimation compensates…
Benchmark Dataset for Catalysis on 2D MXenes
Pavlo Melnyk, Anmar Karmush, Mårten Wadenbäck +4
Merging first-principles calculations with machine learning (ML), we aim to accelerate the exploration of catalytic behaviour in novel materials. We focus on two-dimensional (2D) T…
QuaMo: Quaternion Motions for Vision-based 3D Human Kinematics Capture
Cuong Le, Pavlo Melnyk, Urs Waldmann +2
Vision-based 3D human motion capture from videos remains a challenge in computer vision. Traditional 3D pose estimation approaches often ignore the temporal consistency between fra…
Flow Matching for Probabilistic Monocular 3D Human Pose Estimation
Cuong Le, Pavlo Melnyk, Bastian Wandt +1
Recovering 3D human poses from a monocular camera view is a highly ill-posed problem due to the depth ambiguity. Earlier studies on 3D human pose lifting from 2D often contain inco…
On the Role of Rotation Equivariance in Monocular 2D-to-3D Human Pose Lifting
Pavlo Melnyk, Cuong Le, Urs Waldmann +2
We consider monocular 3D human pose estimation (HPE), where the goal is to predict 3D human skeletal joints from a single 2D image, typically via 2D keypoint detection followed by…