MotionMERGE proposes a multi-granular LLM framework for fine-grained text-driven human motion editing, reasoning, generation, and explanation, supported by the new MotionFineEdit dataset with spatio-temporal annotations.
MotionLLM: Multimodal motion-language learning with large language models
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CoMoVi co-generates 3D human motions and 2D videos synchronously in a single diffusion denoising loop using 3D-to-2D projection and dual-branch diffusion with 3D-2D cross attentions.
LLaMo scales pretrained LLMs for unified motion-language tasks by encoding motion into continuous causal latents and adding a flow-matching head for real-time autoregressive generation and captioning.
LLMs can generate coherent multimodal behaviors for SIAs that align with intended ability and benevolence levels as confirmed by user perceptions, while also reproducing gender stereotypes.
citing papers explorer
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MotionMERGE: A Multi-granular Framework for Human Motion Editing, Reasoning, Generation, and Explanation
MotionMERGE proposes a multi-granular LLM framework for fine-grained text-driven human motion editing, reasoning, generation, and explanation, supported by the new MotionFineEdit dataset with spatio-temporal annotations.
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CoMoVi: Co-Generation of 3D Human Motions and Realistic Videos
CoMoVi co-generates 3D human motions and 2D videos synchronously in a single diffusion denoising loop using 3D-to-2D projection and dual-branch diffusion with 3D-2D cross attentions.
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LLaMo: Scaling Pretrained Language Models for Unified Motion Understanding and Generation with Continuous Autoregressive Tokens
LLaMo scales pretrained LLMs for unified motion-language tasks by encoding motion into continuous causal latents and adding a flow-matching head for real-time autoregressive generation and captioning.
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Towards Trust Calibration in Socially Interactive Agents: Investigating Gendered Multimodal Behaviors Generation with LLMs
LLMs can generate coherent multimodal behaviors for SIAs that align with intended ability and benevolence levels as confirmed by user perceptions, while also reproducing gender stereotypes.