Generating Realistic 3D Sign Language Avatars from Text

Core Concepts

A diffusion-based generative model that can produce realistic 3D sign language animations from unconstrained text inputs, outperforming previous state-of-the-art methods.

Abstract

The paper introduces a novel method for generating realistic 3D sign language animations from text inputs. Key highlights: The authors curate a large-scale dataset of 3D American Sign Language (ASL) by annotating the How2Sign dataset with high-quality SMPL-X pose parameters. This is the first publicly available 3D sign language dataset. They propose a diffusion-based generative model that directly maps text to 3D sign language animations, without relying on intermediate representations like glosses. The core of the model is a novel, anatomically-informed graph neural network that models the pose and expression distributions. Extensive experiments show that the proposed method significantly outperforms previous state-of-the-art sign language production models across various quantitative metrics and a user study with ASL-fluent participants. Ablation studies demonstrate the importance of the key components, such as the anatomically-inspired pose encoder, the autoregressive decoder, and the text encoding module. The work represents an important step towards realistic neural sign avatars that can bridge the communication gap between the Deaf/Hard-of-Hearing and hearing communities.

Stats

The proposed method outperforms previous state-of-the-art methods on the How2Sign dataset: Mean Per Vertex Position Error (MPVPE) for body: 31.47 mm (vs 55.02 mm for Stoll et al.) MPVPE for left hand: 36.24 mm (vs 68.48 mm for Stoll et al.) MPVPE for right hand: 39.68 mm (vs 60.18 mm for Stoll et al.)

Quotes

"Neural 3D sign language production is an important challenge that aims to aid the Deaf and Hard of Hearing community and can effectively increase their inclusion in any social environment." "The release of additional relevant databases will enable the training of even more robust architectures."

Key Insights Distilled From

Neural Sign Actors

by Vasileios Ba... at arxiv.org 04-08-2024

https://arxiv.org/pdf/2312.02702.pdf

Deeper Inquiries

How can the proposed method be extended to support real-time sign language generation for interactive applications

To extend the proposed method for real-time sign language generation in interactive applications, several key steps can be taken. Firstly, optimizing the model architecture and training process for efficiency is crucial. This can involve implementing techniques like model quantization, pruning, or distillation to reduce the computational load and enable faster inference times. Additionally, leveraging hardware acceleration such as GPUs or TPUs can significantly speed up the generation process. Furthermore, incorporating streaming techniques can help in processing input text in real-time and generating sign language output on the fly. This involves breaking down the input text into smaller segments or frames and feeding them sequentially to the model for continuous generation. Implementing buffering mechanisms can also help in smoothing out the output and ensuring a seamless user experience. Moreover, integrating the model with interactive interfaces or applications requires robust communication protocols and APIs. Developing a user-friendly interface that allows users to input text and receive real-time sign language output is essential. This interface should provide feedback on the generated signs, enable corrections or adjustments, and ensure smooth interaction between the user and the sign language avatar.

What are the potential challenges in deploying such sign language avatars in real-world scenarios, and how can they be addressed

Deploying sign language avatars in real-world scenarios poses several challenges that need to be addressed for successful implementation. One major challenge is ensuring the accuracy and naturalness of the generated sign language motions. This includes capturing the nuances of sign language expressions, gestures, and emotions to convey the intended message effectively. Continuous training and refinement of the model with diverse datasets and user feedback can help improve the quality of the generated signs. Another challenge is the integration of sign language avatars into existing communication platforms or devices. Compatibility issues, data privacy concerns, and accessibility features need to be considered to ensure seamless integration and usability for all users. Providing customization options for users to personalize their sign language avatars can enhance user engagement and satisfaction. Additionally, addressing ethical considerations such as cultural sensitivity, inclusivity, and representation in sign language avatars is crucial. Ensuring that the avatars respect diverse sign language dialects, cultural norms, and individual preferences is essential for fostering positive user experiences and promoting inclusivity.

How can the insights from this work on anatomically-informed pose modeling be applied to other domains of human motion generation, such as dance or sports

The insights from anatomically-informed pose modeling in sign language generation can be applied to other domains of human motion generation, such as dance or sports, to enhance the realism and expressiveness of the generated motions. By incorporating anatomical constraints and kinematic relationships into the pose modeling process, more natural and fluid movements can be achieved in dance routines or sports activities. In the context of dance, understanding the anatomical structure and joint movements specific to different dance styles can help in creating more authentic and visually appealing choreographies. By modeling poses based on anatomical constraints and motion dynamics, dance sequences can be generated with greater precision and artistic expression. Similarly, in sports motion generation, incorporating anatomically-informed pose modeling can improve the accuracy and realism of athletic movements. By considering the biomechanics and joint interactions involved in various sports activities, realistic simulations of actions like running, jumping, or throwing can be generated. This can be valuable for training simulations, sports analysis, and virtual coaching applications.