Introducing SignAvatars: A Comprehensive 3D Sign Language Motion Dataset and Benchmark for Advancing Digital Communication

Conceitos essenciais

SignAvatars is the first large-scale, multi-prompt 3D sign language motion dataset designed to bridge the communication gap for Deaf and hard-of-hearing individuals. It provides accurate 3D annotations of body, hand, and face motions, enabling various tasks such as 3D sign language recognition and production.

Resumo

The SignAvatars dataset is a significant contribution towards bringing the digital world to the Deaf and hard-of-hearing communities. It comprises 70,000 videos from 153 signers, totaling 8.34 million frames, covering both isolated signs and continuous, co-articulated signs, with multiple prompts including HamNoSys, spoken language, and words.
To yield the 3D holistic annotations, including meshes and biomechanically-valid poses of body, hands, and face, as well as 2D and 3D keypoints, the authors introduce an automated annotation pipeline operating on the large corpus of sign language videos. This pipeline utilizes a multi-objective optimization that considers temporal information and respects biomechanical constraints to produce accurate hand poses, even in the presence of complex, interacting hand gestures.
The dataset enables various tasks, such as 3D sign language recognition (SLR) and the novel 3D sign language production (SLP) from diverse inputs like text scripts, individual words, and HamNoSys notation. To evaluate the potential of SignAvatars, the authors propose a unified benchmark for 3D SL holistic motion production, which includes baselines and a strong VQVAE-based model, Sign-VQVAE, that significantly outperforms the other methods.
The authors believe that SignAvatars is a significant step forward towards bringing the 3D digital world and 3D sign language applications to the Deaf and hard-of-hearing communities, fostering future research in 3D sign language understanding.

Estatísticas

There are 466 million Deaf and hard-of-hearing people in the world, with over 70 million communicating via sign languages.
The SignAvatars dataset comprises 70,000 videos from 153 signers, totaling 8.34 million frames.
The dataset covers both isolated signs and continuous, co-articulated signs, with multiple prompts including HamNoSys, spoken language, and words.

Citações

"We believe that this work is a significant step forward towards bringing the digital world to the Deaf and hard-of-hearing communities as well as people interacting with them."
"To yield 3D holistic annotations, including meshes and biomechanically-valid poses of body, hands, and face, as well as 2D and 3D keypoints, we introduce an automated annotation pipeline operating on our large corpus of SL videos."

Principais Insights Extraídos De

SignAvatars

by Zhengdi Yu,S... às arxiv.org 04-04-2024

https://arxiv.org/pdf/2310.20436.pdf

Perguntas Mais Profundas

How can the SignAvatars dataset be leveraged to develop more advanced sign language translation and production systems that can seamlessly integrate with existing digital communication platforms

The SignAvatars dataset provides a valuable resource for developing advanced sign language translation and production systems that can seamlessly integrate with existing digital communication platforms. By leveraging the dataset's large-scale 3D motion annotations and diverse prompts, researchers can train machine learning models to accurately recognize and generate sign language gestures. These models can then be integrated into existing communication platforms to provide real-time translation services for Deaf and hard-of-hearing individuals. Additionally, the dataset's automated annotation pipeline allows for the efficient creation of 3D avatars with natural and expressive movements, enhancing the user experience in digital interactions.

What are the potential challenges and ethical considerations in deploying 3D sign language avatars in real-world applications, and how can the research community address them

Deploying 3D sign language avatars in real-world applications poses several potential challenges and ethical considerations. One challenge is ensuring the accuracy and cultural sensitivity of the avatars, especially when representing diverse sign languages and regional variations. Ethical considerations include issues related to data privacy and consent, as well as the potential misuse of avatar technology for deceptive or harmful purposes. To address these challenges, the research community can implement robust data protection measures, engage with sign language communities for feedback and validation, and adhere to ethical guidelines for the development and deployment of avatar technology. Transparency in the development process and ongoing communication with stakeholders are essential to building trust and ensuring the responsible use of 3D sign language avatars.

How can the insights and techniques developed for the SignAvatars dataset be extended to other sign languages and modalities, such as regional variations or multi-modal sign language that incorporates facial expressions and body language

The insights and techniques developed for the SignAvatars dataset can be extended to other sign languages and modalities by adapting the annotation pipeline and training models to accommodate regional variations and multi-modal expressions. Researchers can collect data from different sign language communities to create datasets that capture the unique characteristics of each language. By incorporating facial expressions, body language, and other non-manual signals into the annotation process, researchers can develop more comprehensive models for multi-modal sign language understanding and production. Collaborating with experts in specific sign languages and conducting cross-cultural studies can help ensure the accuracy and inclusivity of the models across different linguistic and cultural contexts.