PREF: Predictability Regularized Neural Motion Fields

Liangchen Song, Xuan Gong, Benjamin Planche, Meng Zheng, D. Doermann, Junsong Yuan, Terrence Chen, Ziyan Wu
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引用次数: 5

Abstract

Knowing the 3D motions in a dynamic scene is essential to many vision applications. Recent progress is mainly focused on estimating the activity of some specific elements like humans. In this paper, we leverage a neural motion field for estimating the motion of all points in a multiview setting. Modeling the motion from a dynamic scene with multiview data is challenging due to the ambiguities in points of similar color and points with time-varying color. We propose to regularize the estimated motion to be predictable. If the motion from previous frames is known, then the motion in the near future should be predictable. Therefore, we introduce a predictability regularization by first conditioning the estimated motion on latent embeddings, then by adopting a predictor network to enforce predictability on the embeddings. The proposed framework PREF (Predictability REgularized Fields) achieves on par or better results than state-of-the-art neural motion field-based dynamic scene representation methods, while requiring no prior knowledge of the scene.
PREF:可预测性正则化神经运动场
了解动态场景中的3D运动对于许多视觉应用来说是必不可少的。最近的进展主要集中在估计某些特定元素(如人类)的活动。在本文中,我们利用神经运动场来估计多视图设置中所有点的运动。由于相似颜色点和时变颜色点的模糊性,多视图动态场景的运动建模具有挑战性。我们建议对估计的运动进行正则化,使其可预测。如果前一帧的运动是已知的,那么在不久的将来的运动应该是可预测的。因此,我们引入了可预测性正则化,首先将估计的运动条件化为潜在嵌入,然后采用预测网络对嵌入进行可预测性。所提出的框架PREF(可预测性正则化域)达到了与最先进的基于神经运动场的动态场景表示方法相当或更好的结果,同时不需要对场景的先验知识。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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