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Simply translating symbolic sign language notations into natural language unlocks significantly better motion generation when conditioning on phonological attributes with CLIP.
Gesture-aware pretraining unlocks significant improvements in 3D hand pose estimation, proving that semantic gesture information acts as a powerful inductive bias.
By explicitly modeling per-splat appearance variance, VarSplat enables more robust 3D Gaussian Splatting SLAM, particularly in low-texture or reflective environments where existing methods struggle.