Motion-Adapter improves text-to-motion diffusion models for compound actions by using decoupled cross-attention maps as structural masks during denoising to produce more coherent full-body motions.
Spatial temporal graph convolutional networks for skeleton-based action recognition,
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Higher temporal resolution in video significantly improves zero-shot semantic understanding of high-speed human actions like kendo.
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Motion-Adapter: A Diffusion Model Adapter for Text-to-Motion Generation of Compound Actions
Motion-Adapter improves text-to-motion diffusion models for compound actions by using decoupled cross-attention maps as structural masks during denoising to produce more coherent full-body motions.
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High-Speed Vision Improves Zero-Shot Semantic Understanding of Human Actions
Higher temporal resolution in video significantly improves zero-shot semantic understanding of high-speed human actions like kendo.