GARD is a context-aware autoregressive diffusion model for gloss-wise sign language production using inter-gloss transition guidance and global motion harmonizer, claiming superior linguistic accuracy and motion similarity on Phoenix-T and CSL-Daily datasets.
arXiv preprint arXiv:2405.15439 (2024) 16 J
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Proposes a feed-forward keyframe-conditioned in-betweening method for arbitrary 4D meshes using a topology-agnostic VAE and MMDiT-based rectified flow model.
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Context-Aware Autoregressive Diffusion for Gloss-Wise Sign Language Production
GARD is a context-aware autoregressive diffusion model for gloss-wise sign language production using inter-gloss transition guidance and global motion harmonizer, claiming superior linguistic accuracy and motion similarity on Phoenix-T and CSL-Daily datasets.
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Feed-forward Motion In-betweening for Any 4D
Proposes a feed-forward keyframe-conditioned in-betweening method for arbitrary 4D meshes using a topology-agnostic VAE and MMDiT-based rectified flow model.