Large Body Language Models

• URL: http://arxiv.org/abs/2410.16533v1
• Date: Mon, 21 Oct 2024 21:48:24 GMT
• Title: Large Body Language Models
• Authors: Saif Punjwani, Larry Heck,
• Abstract summary: We introduce Large Body Language Models (LBLMs) and present LBLM-AVA, a novel LBLM architecture that combines a Transformer-XL large language model with a parallelized diffusion model to generate human-like gestures from multimodal inputs (text, audio, and video) LBLM-AVA achieves state-of-the-art performance in generating lifelike and contextually appropriate gestures with a 30% reduction in Freche's Gesture Distance (FGD) and a 25% improvement in Freche's Inception Distance compared to existing approaches.
• Score: 1.9797215742507548
• License:
• Abstract: As virtual agents become increasingly prevalent in human-computer interaction, generating realistic and contextually appropriate gestures in real-time remains a significant challenge. While neural rendering techniques have made substantial progress with static scripts, their applicability to human-computer interactions remains limited. To address this, we introduce Large Body Language Models (LBLMs) and present LBLM-AVA, a novel LBLM architecture that combines a Transformer-XL large language model with a parallelized diffusion model to generate human-like gestures from multimodal inputs (text, audio, and video). LBLM-AVA incorporates several key components enhancing its gesture generation capabilities, such as multimodal-to-pose embeddings, enhanced sequence-to-sequence mapping with redefined attention mechanisms, a temporal smoothing module for gesture sequence coherence, and an attention-based refinement module for enhanced realism. The model is trained on our large-scale proprietary open-source dataset Allo-AVA. LBLM-AVA achieves state-of-the-art performance in generating lifelike and contextually appropriate gestures with a 30% reduction in Fr\'echet Gesture Distance (FGD), and a 25% improvement in Fr\'echet Inception Distance compared to existing approaches.

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