Neural rendering techniques have significantly advanced 3D human body
modeling. However, previous approaches often overlook dynamics induced by
factors such as motion inertia, leading to challenges in scenarios like abrupt
stops after rotation, where the pose remains static while the appearance
changes. This limitation arises from reliance on a single pose as conditional
input, resulting in ambiguity in mapping one pose to multiple appearances. In
this study, we elucidate that variations in human appearance depend not only on
the current frame's pose condition but also on past pose states. Therefore, we
introduce Dyco, a novel method utilizing the delta pose sequence representation
for non-rigid deformations and canonical space to effectively model temporal
appearance variations. To prevent a decrease in the model's generalization
ability to novel poses, we further propose low-dimensional global context to
reduce unnecessary inter-body part dependencies and a quantization operation to
mitigate overfitting of the delta pose sequence by the model. To validate the
effectiveness of our approach, we collected a novel dataset named I3D-Human,
with a focus on capturing temporal changes in clothing appearance under
approximate poses. Through extensive experiments on both I3D-Human and existing
datasets, our approach demonstrates superior qualitative and quantitative
performance. In addition, our inertia-aware 3D human method can unprecedentedly
simulate appearance changes caused by inertia at different velocities.

在这项研究中，我们引入了 Dyco 这一新方法，利用增量姿势序列表示来有效模拟非刚性变形和时间外貌变化，以解决传统方法中姿势与外貌间的歧义性问题。通过在 I3D-Human 和现有数据集上进行广泛实验，我们的方法体现出卓越的质量和数量性能，并能以前所未有的方式模拟不同速度下由惯性引起的外貌变化。