We introduce a method for transferring the signer’s appearance in sign language skeletal poses while preserving the sign content. Using estimated poses, we transfer the appearance of one signer to another, maintaining natural movements and transitions. This approach improves pose-based rendering and sign stitching while obfuscating identity. Our experiments show that while the method reduces signer identification accuracy, it slightly harms sign recognition performance, highlighting a tradeoff between privacy and utility.

This work presents the first investigation into Spontaneous Isolated Sign Language Recognition for Catalan Sign Language (LSC). Our work is grounded on the derivation of a dataset of signs and their glosses from a corpus of spontaneous dialogues and monologues. The recognition model is based on a Multi-Scale Graph Convolutional network fitted to our data. Results are promising since several signs are recognized with a high level of accuracy, and an average accuracy of 71% on the top 5 predicted classes from a total of 105 available. An interactive interface with experimental results is also presented. The data and software are made available to the research community.

Machine translation (MT) has evolved rapidly over the last 70 years thanks to the advances in processing technology, methodologies as well as the ever-increasing volumes of data. This trend is observed in the context of MT for spoken languages. However, when it comes to sign languages (SL) translation technologies, the progress is much slower; SLMT is still in its infancy with limited applications. One of the main factors for this set back is the lack of effective, respectful and fair user involvement across the different phases of the research and development of SLMT. We present a meta-review of 111 articles on SLMT from the perspective of user involvement. Our analysis investigates what users are involved and what tasks they assume in the first four phrases of MT research: (i) Problem and definition, (ii) Dataset construction, (iii) Model Design and Training, (iv) Model Validation and Evaluation. We find out that users have primarily been involved as data creators and monitors as well as evaluators. We assess that effective co-creation, as defined in (Lepp et al., 2025), has not been performed and conclude with recommendations for improving the MT research and development landscape from a co-creative perspective.

This article introduces the parallel sign language translation corpus, PaSCo1, developed as part of the BabelDr project, an automatic speech translation system for medical triage. PaSCo1 aims to make a set of medical data available in Swiss French Sign Language (LSF-CH) in the form of both videos signed by a human and their description in G-SiGML mark-up language. We describe the beginnings of the corpus as part of the BabelDr project, as well as the methodology used to create the videos and generate the G-SiGML language using the SiGLA platform. The resulting FAIR corpus comprises 2 031 medical questions and instructions in the form of videos and G-SiGML code.