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Language models learn to generate facial responses from speech

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a framework to generate appropriate facial responses for a listener in social interactions based on the speaker's words. This approach treats quantized facial gesture elements as additional language tokens for a transformer-based large language model. Initializing the transformer with pre-trained language model weights yielded higher quality responses than training from scratch, demonstrating fluent and semantically relevant generated motion. AI

IMPACT Demonstrates LLMs' potential for multimodal understanding and generation, extending their capabilities beyond text.

RANK_REASON Academic paper detailing a novel framework for LLMs to generate facial responses. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Language models learn to generate facial responses from speech

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Academic paper detailing a novel framework for LLMs to generate facial responses. [lever_c_demoted from research: ic=1 ai=1.0]
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
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High
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113 days old
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Evonne Ng, Sanjay Subramanian, Dan Klein, Angjoo Kanazawa, Trevor Darrell, Shiry Ginosar ·

    Can Language Models Learn to Listen?

    arXiv:2308.10897v2 Announce Type: replace Abstract: We present a framework for generating appropriate facial responses from a listener in dyadic social interactions based on the speaker's words. Given an input transcription of the speaker's words with their timestamps, our approa…

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