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Local Temporal Contrasts on Human Motion Data Induce a Latent Geometry
Time series such as recordings of human motion data often encode structure across multiple temporal scales, and when a latent state determining this sequence evolves, this structure needs to survive in the representation space for downstream use. Yet the currently prevalent attention-based encoders
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