FlowCoMotion: Text-to-Motion Generation via Token-Latent Flow Modeling explores FlowCoMotion is a text-to-motion generation framework that unifies continuous and discrete motion representations using token-latent coupling to capture both semantic content and high-fidelity motion details, achieving competitive performance.. Commercial viability score: 7/10 in Generative Video.
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Canonical ID flowcomotion-text-to-motion-generation-via-token-latent-flow-modeling | Route /paper/flowcomotion-text-to-motion-generation-via-token-latent-flow-modeling
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/buildability/flowcomotion-text-to-motion-generation-via-token-latent-flow-modeling
Subject: FlowCoMotion: Text-to-Motion Generation via Token-Latent Flow Modeling
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Dimensions overall score 7.0
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This equation captures one of the core mathematical components of the system. The motion sequence is compressed into the latent space using VAE to obtain z ∈Rn×b. (2) Motion representation &
Page and bbox are available; crop image is pending.
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Receipt path
/buildability/flowcomotion-text-to-motion-generation-via-token-latent-flow-modeling
Paper ref
flowcomotion-text-to-motion-generation-via-token-latent-flow-modeling
arXiv id
2604.11083
Generated at
2026-04-14T16:49:54.235Z
Evidence freshness
stale
Last verification
2026-04-14T16:49:54.235Z
Sources
3
References
0
Coverage
50%
Lineage hash
f7f5d01c1b42a4cd57c3e065816d6a089ff65e1dfea43a7fa3e2b8b8aa1b9cb5
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references
This equation captures one of the core mathematical components of the system. In the Latent Branch, a latent vector z ∈Rm is extracted from the distribution using a reparameteri-
Page and bbox are available; crop image is pending.
This equation captures one of the core mathematical components of the system. with all layers sharing a single codebook [11]. For a motion latent feature sequence z ∈Rn×d, the
Page and bbox are available; crop image is pending.
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