SIGMA-ASL is a multimodal dataset with 93,545 word-level ASL clips from Kinect RGB-D, mmWave radar, and dual IMUs, plus benchmarking protocols for single- and multi-modal recognition.
Quantitative Survey of the State of the Art in Sign Language Recognition.arXiv preprint arXiv:2008.09918
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Introduces the eJSL Dialog dataset (1,920 videos in 480 dialogues from STUDIES corpus) for conversational sign language emotion recognition and benchmarks models revealing a domain gap with generic multimodal approaches.
SignVerse-2M provides a 2-million-clip multilingual pose-native dataset for sign language derived from public videos via DWPose preprocessing to enable robust modeling in real-world conditions.
Reframing head pose estimation as relative pose prediction between image pairs enables a synthetic-only trained model to outperform absolute regression methods on real benchmarks.
citing papers explorer
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SIGMA-ASL: Sensor-Integrated Multimodal Dataset for Sign Language Recognition
SIGMA-ASL is a multimodal dataset with 93,545 word-level ASL clips from Kinect RGB-D, mmWave radar, and dual IMUs, plus benchmarking protocols for single- and multi-modal recognition.
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Emotion Recognition in Sign Language Conversation
Introduces the eJSL Dialog dataset (1,920 videos in 480 dialogues from STUDIES corpus) for conversational sign language emotion recognition and benchmarks models revealing a domain gap with generic multimodal approaches.
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SignVerse-2M: A Two-Million-Clip Pose-Native Universe of 55+ Sign Languages
SignVerse-2M provides a 2-million-clip multilingual pose-native dataset for sign language derived from public videos via DWPose preprocessing to enable robust modeling in real-world conditions.
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VGGT-HPE: Reframing Head Pose Estimation as Relative Pose Prediction
Reframing head pose estimation as relative pose prediction between image pairs enables a synthetic-only trained model to outperform absolute regression methods on real benchmarks.