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Found 12 papers, 2 papers with code
Select and Reorder: A Novel Approach for Neural Sign Language Production
Sign languages, often categorised as low-resource languages, face significant challenges in achieving accurate translation due to the scarcity of parallel annotated datasets.
Gloss Alignment Using Word Embeddings
As a result, research has turned to TV broadcast content as a source of large-scale training data, consisting of both the sign language interpreter and the associated audio subtitle.
Changing the Representation: Examining Language Representation for Neural Sign Language Production
We use language models such as BERT and Word2Vec to create better sentence level embeddings, and apply several tokenization techniques, demonstrating how these improve performance on the low resource translation task of Text to Gloss.
Signing at Scale: Learning to Co-Articulate Signs for Large-Scale Photo-Realistic Sign Language Production
To learn sign co-articulation, we propose a novel Frame Selection Network (FS-Net) that improves the temporal alignment of interpolated dictionary signs to continuous signing sequences.
Skeletal Graph Self-Attention: Embedding a Skeleton Inductive Bias into Sign Language Production
Recent approaches to Sign Language Production (SLP) have adopted spoken language Neural Machine Translation (NMT) architectures, applied without sign-specific modifications.
Mixed SIGNals: Sign Language Production via a Mixture of Motion Primitives
Using a progressive transformer for the translation sub-task, we propose a novel Mixture of Motion Primitives (MoMP) architecture for sign language animation.
AnonySIGN: Novel Human Appearance Synthesis for Sign Language Video Anonymisation
To tackle SLVA, we propose AnonySign, a novel automatic approach for visual anonymisation of sign language data.
Content4All Open Research Sign Language Translation Datasets
Computational sign language research lacks the large-scale datasets that enables the creation of useful reallife applications.
Continuous 3D Multi-Channel Sign Language Production via Progressive Transformers and Mixture Density Networks
Sign languages are multi-channel visual languages, where signers use a continuous 3D space to communicate. Sign Language Production (SLP), the automatic translation from spoken to sign languages, must embody both the continuous articulation and full morphology of sign to be truly understandable by the Deaf community.
Everybody Sign Now: Translating Spoken Language to Photo Realistic Sign Language Video
To be truly understandable and accepted by Deaf communities, an automatic Sign Language Production (SLP) system must generate a photo-realistic signer.
Adversarial Training for Multi-Channel Sign Language Production
Sign Languages are rich multi-channel languages, requiring articulation of both manual (hands) and non-manual (face and body) features in a precise, intricate manner.
Progressive Transformers for End-to-End Sign Language Production
The goal of automatic Sign Language Production (SLP) is to translate spoken language to a continuous stream of sign language video at a level comparable to a human translator.
