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• BTS-rPPG improves long-range temporal modeling of physiological dynamics compared to existing methods.result 90%
• Many deep learning methods for rPPG rely on temporal shifting or convolutional operators that aggregate information primarily from neighboring frames.limitation 85%
• The Orthogonal Butterfly Temporal Shifting (BTS) framework establishes structured frame interactions via an XOR-based butterfly pairing schedule.method 90%
• The orthogonal feature transfer mechanism (OFT) filters source features with respect to target context before temporal shifting.method 90%
• The orthogonal feature transfer mechanism reduces redundant feature propagation and encourages complementary temporal interaction.technical 85%
• BTS-rPPG consistently outperforms existing temporal modeling strategies for rPPG estimation.result 85%
• The BTS framework is inspired by the butterfly communication pattern in the Fast Fourier Transform (FFT).method 90%
• Existing temporal shifting methods result in predominantly local temporal modeling and limited temporal receptive fields.limitation 80%

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