arXiv:2607.03657v1 Announce Type: cross Abstract: Gloss-free Sign Language Translation (SLT) translates sign language videos into spoken-language sentences without gloss annotations, avoiding costly labeling but requiring fine-grained modeling of hands, body, and facial cues. Existing methods often use single-modality or weakly fused features, limiting performance. We propose ViPo-MLLM, a framework that integrates spatio-temporal RGB and human pose features. Dedicated encoders model intra-modal dynamics and cross-modal attention captures long-range dependencies. The fused representation is con
Advances in multimodal AI and computer vision allow for more sophisticated processing of complex human interaction data, such as sign language, leveraging high-performance compute and MLLM architectures.
This research represents a significant step towards more inclusive and automatic communication technologies, with potential applications in accessibility, human-computer interaction, and specialized AI agents.
The development of a more accurate 'gloss-free' sign language translation system reduces reliance on labor-intensive gloss annotations, making the technology more scalable and practical for real-world use.
- · Deaf and Hard of Hearing communities
- · AI developers focused on accessibility
- · Multimodal AI research institutions
- · Social media platforms
- · Traditional sign language interpreting services (long-term disruption)
- · Developers reliant on simplified speech-to-text models
Improved machine translation accuracy for sign languages, leading to wider adoption by individuals and organizations.
Reduced communication barriers and increased integration for the Deaf community across various societal functions (education, work, public services).
The development of 'universal' communication AI agents that can seamlessly translate between spoken, written, and gestural languages in real-time, becoming indispensable digital companions.
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Read at arXiv cs.AI