IAM jointly synthesizes motion sequences and body shape parameters conditioned on multimodal identity signals to achieve more realistic and identity-consistent human motions.
Go to zero: Towards zero-shot motion generation with million-scale data.arXiv preprint arXiv:2507.07095,
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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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IAM: Identity-Aware Human Motion and Shape Joint Generation
IAM jointly synthesizes motion sequences and body shape parameters conditioned on multimodal identity signals to achieve more realistic and identity-consistent human motions.
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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.