RelFlexformers enable flexible integrable 3D RPE in attention via NU-FFT, generalizing prior methods to heterogeneous token positions with O(L log L) complexity.
Multimae: Multi-modal multi-task masked autoencoders
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AVI-HT adaptively fuses vision and IMU data via attention to cut 3D hand keypoint error by 16.1% (24.2% wrist-aligned) on a new 100K+ sample DexGloveHOI dataset in occluded hand-object scenarios.
citing papers explorer
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RelFlexformer: Efficient Attention 3D-Transformers for Integrable Relative Positional Encodings
RelFlexformers enable flexible integrable 3D RPE in attention via NU-FFT, generalizing prior methods to heterogeneous token positions with O(L log L) complexity.
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AVI-HT: Adaptive Vision-IMU Fusion for 3D Hand Tracking
AVI-HT adaptively fuses vision and IMU data via attention to cut 3D hand keypoint error by 16.1% (24.2% wrist-aligned) on a new 100K+ sample DexGloveHOI dataset in occluded hand-object scenarios.