3D Human Motion Estimation via Motion Compression and Refinement
نویسندگان
چکیده
We develop a technique for generating smooth and accurate 3D human pose motion estimates from RGB video sequences. Our method, which we call Motion Estimation via Variational Autoencoder (MEVA), decomposes temporal sequence of into representation using auto-encoder-based compression residual learned through refinement. This two-step encoding captures in two stages: general estimation step that the coarse overall motion, adds back person-specific details. Experiments show our method produces both estimates.
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ژورنال
عنوان ژورنال: Lecture Notes in Computer Science
سال: 2021
ISSN: ['1611-3349', '0302-9743']
DOI: https://doi.org/10.1007/978-3-030-69541-5_20