mlsanityData to 14 Oct 2026

Can Everybody Sign Now? Exploring Sign Language Video Generation from 2D Poses

arXiv 2012.10941 ↗First posted 20 Dec 2020cs.CV cs.AIcited by 12

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Abstract (arXiv)

Recent work have addressed the generation of human poses represented by 2D/3D coordinates of human joints for sign language. We use the state of the art in Deep Learning for motion transfer and evaluate them on How2Sign, an American Sign Language dataset, to generate videos of signers performing sign language given a 2D pose skeleton. We evaluate the generated videos quantitatively and qualitatively showing that the current models are not enough to generated adequate videos for Sign Language due to lack of detail in hands.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). First posted on arXiv 20 Dec 2020.

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

12 papers.

  1. 2022Signing at Scale: Learning to Co-Articulate Signs for Large-Scale Photo-Realistic Sign Language Productioncited by 61
  2. 2023SignDiff: Diffusion Model for American Sign Language Productioncited by 25
  3. 2022Jointly Harnessing Prior Structures and Temporal Consistency for Sign Language Video Generationcited by 14
  4. 2023An Open-Source Gloss-Based Baseline for Spoken to Signed Language Translationcited by 9
  5. 2024Sign Stitching: A Novel Approach to Sign Language Productioncited by 8
  6. 2024Pose-Guided Fine-Grained Sign Language Video Generationcited by 8
  7. 2025Text-Driven 3D Hand Motion Generation from Sign Language Datacited by 5
  8. 2021Pose-Guided Sign Language Video GAN with Dynamic Lambdacited by 3
  9. 2023A Survey on Deep Multi-modal Learning for Body Language Recognition and Generationcited by 3
  10. 2022Neural Sign Reenactor: Deep Photorealistic Sign Language Retargetingcited by 2
  11. 2024SignLLM: Sign Language Production Large Language Modelscited by 2
  12. 2025HandCraft: Dynamic Sign Generation for Synthetic Data Augmentationcited by 0