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Found 5 papers, 0 papers with code
Exploiting Spatial-Temporal Context for Interacting Hand Reconstruction on Monocular RGB Video
Reconstructing interacting hands from monocular RGB data is a challenging task, as it involves many interfering factors, e. g. self- and mutual occlusion and similar textures.
SignBERT+: Hand-model-aware Self-supervised Pre-training for Sign Language Understanding
In our framework, the hand pose is regarded as a visual token, which is derived from an off-the-shelf detector.
Ranked #1 on
Sign Language Recognition
on WLASL
BEST: BERT Pre-Training for Sign Language Recognition with Coupling Tokenization
In this work, we are dedicated to leveraging the BERT pre-training success and modeling the domain-specific statistics to fertilize the sign language recognition~(SLR) model.
SignBERT: Pre-Training of Hand-Model-Aware Representation for Sign Language Recognition
To validate the effectiveness of our method on SLR, we perform extensive experiments on four public benchmark datasets, i. e., NMFs-CSL, SLR500, MSASL and WLASL.
Ranked #1 on
Sign Language Recognition
on WLASL100
(using extra training data)
Model-Aware Gesture-to-Gesture Translation
Then, a transformation flow is calculated based on the correspondence of the source and target topology map.
