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- W3110022498 abstract "We develop a technique for generating smooth and accurate 3D human pose and motion estimates from RGB video sequences. Our method, which we call Motion Estimation via Variational Autoencoder (MEVA), decomposes a temporal sequence of human motion into a smooth motion representation using auto-encoder-based motion compression and a residual representation learned through motion refinement. This two-step encoding of human motion captures human motion in two stages: a general human motion estimation step that captures the coarse overall motion, and a residual estimation that adds back person-specific motion details. Experiments show that our method produces both smooth and accurate 3D human pose and motion estimates." @default.
- W3110022498 created "2020-12-07" @default.
- W3110022498 creator A5030087058 @default.
- W3110022498 creator A5032880356 @default.
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- W3110022498 date "2021-01-01" @default.
- W3110022498 modified "2023-09-30" @default.
- W3110022498 title "3D Human Motion Estimation via Motion Compression and Refinement" @default.
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- W3110022498 doi "https://doi.org/10.1007/978-3-030-69541-5_20" @default.
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