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Found 6 papers, 1 papers with code
Towards Part-aware Monocular 3D Human Pose Estimation: An Architecture Search Approach
With roughly the same computational complexity as previous models, our approach achieves state-of-the-art results on both the single-person and multi-person 3D pose estimation benchmarks.
Prediction and Recovery for Adaptive Low-Resolution Person Re-Identification
In this paper, we propose a novel Prediction, Recovery and Identification (PRI) model for LR re-id, which adaptively recovers missing details by predicting a preferable scale factor based on the image content.
Learning Explicit Contact for Implicit Reconstruction of Hand-held Objects from Monocular Images
In the second part, we introduce a novel method to diffuse estimated contact states from the hand mesh surface to nearby 3D space and leverage diffused contact probabilities to construct the implicit neural representation for the manipulated object.
gSDF: Geometry-Driven Signed Distance Functions for 3D Hand-Object Reconstruction
In particular, we address reconstruction of hands and manipulated objects from monocular RGB images.
Ranked #5 on
hand-object pose
on DexYCB
AlignSDF: Pose-Aligned Signed Distance Fields for Hand-Object Reconstruction
We show that such aligned SDFs better focus on reconstructing shape details and improve reconstruction accuracy both for hands and objects.
Ranked #9 on
hand-object pose
on DexYCB
A Deep Learning Network for the Classification of Intracardiac Electrograms in Atrial Tachycardia
From our findings, the CNN-LSTM model achieved an accuracy of 81% for the balanced dataset.
