FlowCoMotion: Text-to-Motion Generation via Token-Latent Flow Modeling
Text-to-motion generation is driven by learning motion representations for semantic alignment with language. Existing methods rely on either continuous or discrete motion representations. However, continuous representations entangle semantics with dynamics, while discrete representations lose fine-grained motion details. In this context, we propose FlowCoMotion, a novel motion generation framework that unifies both treatments from a modeling perspective. Specifically, FlowCoMotion employs token-
Record details
Published: 13 April 2026
Source: arXiv
Category: Research
Topics: Safety & alignment
Retrieved: 14 July 2026
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ethics.ai (13 April 2026), “FlowCoMotion: Text-to-Motion Generation via Token-Latent Flow Modeling,” evidence record 5960, https://ethics.ai/record/5960 (originally published by arXiv).
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