At the critical temperature of the 2D Potts model (q>4), a disordered layer emerges between two ordered phases, and its boundaries converge to a Brownian watermelon under diffusive scaling.
Mote: Learning motion-text diffusion model for multiple generation tasks
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ViBES introduces a speech-language-behavior model using modality-specific transformer experts that jointly generates dialogue and 3D body actions, showing gains over separate co-speech and text-to-motion baselines on multi-turn metrics.
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.
citing papers explorer
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Encoder-Free Human Motion Understanding via Structured Motion Descriptions
At the critical temperature of the 2D Potts model (q>4), a disordered layer emerges between two ordered phases, and its boundaries converge to a Brownian watermelon under diffusive scaling.
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ViBES: A Conversational Agent with Behaviorally-Intelligent 3D Virtual Body
ViBES introduces a speech-language-behavior model using modality-specific transformer experts that jointly generates dialogue and 3D body actions, showing gains over separate co-speech and text-to-motion baselines on multi-turn metrics.
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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.