@inproceedings{dai-sako-2026-diffusion,
title = "Diffusion-Based 3{D} Sign Language Motion Anonymization: A Feasibility Study on Balancing Identity Confusion and Semantic Preservation",
author = "Dai, Zixuan and
Sako, Shinji",
editor = "Efthimiou, Eleni and
Fotinea, Stavroula-Evita and
Hanke, Thomas and
Hochgesang, Julie A. and
Mesch, Johanna and
Schulder, Marc",
booktitle = "Proceedings of the {LREC} 2026 12th Workshop on the Representation and Processing of Sign Languages: Language in Motion",
month = may,
year = "2026",
address = "Palma, Mallorca (Spain)",
publisher = "ELRA Language Resources Association (ELRA)",
url = "https://aclanthology.org/2026.signlang-1.10/",
doi = "10.63317/4nrz6i8vbmt2",
pages = "93--99",
abstract = "Sign language motions contain individual-specific kinematic features. As the engineering applications of sign language become more widespread, privacy protection of sign language data has emerged as a new challenge. This paper proposes a diffusion model-based approach for sign language motion anonymization. The proposed framework combines conditional diffusion processes with adversarial training to transform identity features while preserving semantic information. For the design and preliminary validation of the proposed model, we conduct a proof-of-concept experiment using a subset of 22 signers from the ASL100 dataset of WLASL, which demonstrates the feasibility of the proposed approach for sign language anonymization."
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<abstract>Sign language motions contain individual-specific kinematic features. As the engineering applications of sign language become more widespread, privacy protection of sign language data has emerged as a new challenge. This paper proposes a diffusion model-based approach for sign language motion anonymization. The proposed framework combines conditional diffusion processes with adversarial training to transform identity features while preserving semantic information. For the design and preliminary validation of the proposed model, we conduct a proof-of-concept experiment using a subset of 22 signers from the ASL100 dataset of WLASL, which demonstrates the feasibility of the proposed approach for sign language anonymization.</abstract>
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%0 Conference Proceedings
%T Diffusion-Based 3D Sign Language Motion Anonymization: A Feasibility Study on Balancing Identity Confusion and Semantic Preservation
%A Dai, Zixuan
%A Sako, Shinji
%Y Efthimiou, Eleni
%Y Fotinea, Stavroula-Evita
%Y Hanke, Thomas
%Y Hochgesang, Julie A.
%Y Mesch, Johanna
%Y Schulder, Marc
%S Proceedings of the LREC 2026 12th Workshop on the Representation and Processing of Sign Languages: Language in Motion
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F dai-sako-2026-diffusion
%X Sign language motions contain individual-specific kinematic features. As the engineering applications of sign language become more widespread, privacy protection of sign language data has emerged as a new challenge. This paper proposes a diffusion model-based approach for sign language motion anonymization. The proposed framework combines conditional diffusion processes with adversarial training to transform identity features while preserving semantic information. For the design and preliminary validation of the proposed model, we conduct a proof-of-concept experiment using a subset of 22 signers from the ASL100 dataset of WLASL, which demonstrates the feasibility of the proposed approach for sign language anonymization.
%R 10.63317/4nrz6i8vbmt2
%U https://aclanthology.org/2026.signlang-1.10/
%U https://doi.org/10.63317/4nrz6i8vbmt2
%P 93-99
Markdown (Informal)
[Diffusion-Based 3D Sign Language Motion Anonymization: A Feasibility Study on Balancing Identity Confusion and Semantic Preservation](https://aclanthology.org/2026.signlang-1.10/) (Dai & Sako, SignLang 2026)
ACL