mlsanityData to 7 Oct 2026

Evaluating the Immediate Applicability of Pose Estimation for Sign Language Recognition

arXiv 2104.10166 ↗First posted 20 Apr 2021cs.CLcited by 25

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Abstract (arXiv)

Signed languages are visual languages produced by the movement of the hands, face, and body. In this paper, we evaluate representations based on skeleton poses, as these are explainable, person-independent, privacy-preserving, low-dimensional representations. Basically, skeletal representations generalize over an individual's appearance and background, allowing us to focus on the recognition of motion. But how much information is lost by the skeletal representation? We perform two independent studies using two state-of-the-art pose estimation systems. We analyze the applicability of the pose estimation systems to sign language recognition by evaluating the failure cases of the recognition models. Importantly, this allows us to characterize the current limitations of skeletal pose estimation approaches in sign language recognition.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). Builds on 21 of its 43 references that are in the map. First posted on arXiv 20 Apr 2021.

Builds on

21 of 43 references.

References · 21

  1. 2017Attention Is All You Needcited by 59,932
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  3. 2017Quo Vadis, Action Recognition? A New Model and the Kinetics Datasetcited by 3,259
  4. 2018OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fieldscited by 1,161
  5. 2016Realtime Multi-Person 2D Pose Estimation using Part Affinity Fieldscited by 1,061
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  7. 2018DensePose: Dense Human Pose Estimation In The Wildcited by 573
  8. 2016Convolutional Pose Machinescited by 325
  9. 2020Sign Language Transformers: Joint End-to-end Sign Language Recognition and Translationcited by 254
  10. 2017Hand Keypoint Detection in Single Images using Multiview Bootstrappingcited by 231
  11. 2018MS-ASL: A Large-Scale Data Set and Benchmark for Understanding American Sign Languagecited by 118
  12. 2020BSL-1K: Scaling up co-articulated sign language recognition using mouthing cuescited by 106
9 more
  1. 2018Video-based Sign Language Recognition without Temporal Segmentationcited by 88
  2. 2020AUTSL: A Large Scale Multi-modal Turkish Sign Language Dataset and Baseline Methodscited by 75
  3. 2020Multi-channel Transformers for Multi-articulatory Sign Language Translationcited by 71
  4. 2018Neural Sign Language Translation based on Human Keypoint Estimationcited by 65
  5. 2017Adversarial PoseNet: A Structure-aware Convolutional Network for Human Pose Estimationcited by 35
  6. 2020Real-Time Sign Language Detection using Human Pose Estimationcited by 28
  7. 2019Single-Network Whole-Body Pose Estimationcited by 24
  8. 2020A Comprehensive Study on Deep Learning-based Methods for Sign Language Recognitioncited by 12
  9. 2018DeepASL: Enabling Ubiquitous and Non-Intrusive Word and Sentence-Level Sign Language Translationcited by 10

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 1; 2023: 6; 2024: 12; 2025: 3; 2026: 2.

25 papers.

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  1. 2023YouTube-ASL: A Large-Scale, Open-Domain American Sign Language-English Parallel Corpuscited by 74
  2. 2024Towards Privacy-Aware Sign Language Translation at Scalecited by 42
  3. 2024SHuBERT: Self-Supervised Sign Language Representation Learning via Multi-Stream Cluster Predictioncited by 22
  4. 2022Machine Translation from Signed to Spoken Languages: State of the Art and Challengescited by 20
  5. 2024SignCLIP: Connecting Text and Sign Language by Contrastive Learningcited by 20
  6. 2024Systemic Biases in Sign Language AI Research: A Deaf-Led Call to Reevaluate Research Agendascited by 19
  7. 2024SignMusketeers: An Efficient Multi-Stream Approach for Sign Language Translation at Scalecited by 18
  8. 2025SignRep: Enhancing Self-Supervised Sign Representationscited by 15
  9. 2024SignAvatar: Sign Language 3D Motion Reconstruction and Generationcited by 14
  10. 2021Using Motion History Images with 3D Convolutional Networks in Isolated Sign Language Recognitioncited by 12
  11. 2023Reconstructing Signing Avatars From Video Using Linguistic Priorscited by 12
  12. 2024FLEURS-ASL: Including American Sign Language in Massively Multilingual Multitask Evaluationcited by 10
13 more
  1. 2024FSboard: Over 3 million characters of ASL fingerspelling collected via smartphonescited by 6
  2. 2023Towards the extraction of robust sign embeddings for low resource sign language recognitioncited by 5
  3. 2023Learnt Contrastive Concept Embeddings for Sign Recognitioncited by 5
  4. 2023Fingerspelling PoseNet: Enhancing Fingerspelling Translation with Pose-Based Transformer Modelscited by 5
  5. 2024Optimizing Hand Region Detection in MediaPipe Holistic Full-Body Pose Estimation to Improve Accuracy and Avoid Downstream Errorscited by 5
  6. 2023pose-format: Library for Viewing, Augmenting, and Handling .pose Filescited by 3
  7. 2024The NGT200 Dataset: Geometric Multi-View Isolated Sign Recognitioncited by 2
  8. 2024Pose-Based Sign Language Appearance Transfercited by 2
  9. 2024SignAttention: On the Interpretability of Transformer Models for Sign Language Translationcited by 0
  10. 2025Representing Signs as Signs: One-Shot ISLR to Facilitate Functional Sign Language Technologiescited by 0
  11. 2025Sign language recognition from skeletal data using graph and recurrent neural networkscited by 0
  12. 2026Evaluation of Pose Estimation Systems for Sign Language Translationcited by 0
  13. 2026Investigating Temporal Motion Features for Pose-to-Text Indian Sign Language Translationcited by 0