CDAMD is a new autoregressive text-to-motion framework operating on continuous motion coordinates with dual constraints and diffusion-inspired components, establishing new benchmarks and claiming SOTA fidelity plus semantic consistency.
Motion generation: A survey of gen- erative approaches and benchmarks
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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Proposes LoRA-based mixture-of-experts with autoencoder routing for continual bidirectional motion-language learning, reporting near-zero forgetting on a 5-task HumanML3D benchmark derived via semantic clustering.
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Coordinate-Based Dual-Constrained Autoregressive Motion Generation
CDAMD is a new autoregressive text-to-motion framework operating on continuous motion coordinates with dual constraints and diffusion-inspired components, establishing new benchmarks and claiming SOTA fidelity plus semantic consistency.