论文标题
3D人体运动通过运动压缩和完善估算
3D Human Motion Estimation via Motion Compression and Refinement
论文作者
论文摘要
我们开发了一种从RGB视频序列产生平滑而准确的3D人姿势和运动估计的技术。我们通过变异自动编码器（MEVA）称之为运动估计的方法将人类运动的时间序列分解为平滑运动表示，使用基于自动编码器的运动压缩和通过运动改进学到的残留表示。人类运动的这两步编码在两个阶段捕获了人类运动：一个一般的人类运动估计步骤捕获了粗糙的整体运动，以及剩余的估计，增加了特定于人的运动细节。实验表明，我们的方法会产生平滑而准确的3D人姿势和运动估计。
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.
