PREF: Predictability Regularized Neural Motion Fields

• URL: http://arxiv.org/abs/2209.10691v2
• Date: Wed, 5 Apr 2023 19:26:09 GMT
• Title: PREF: Predictability Regularized Neural Motion Fields
• Authors: Liangchen Song, Xuan Gong, Benjamin Planche, Meng Zheng, David Doermann, Junsong Yuan, Terrence Chen, Ziyan Wu
• Abstract summary: Knowing 3D motions in a dynamic scene is essential to many vision applications. We leverage a neural motion field for estimating the motion of all points in a multiview setting. We propose to regularize the estimated motion to be predictable.
• Score: 68.60019434498703
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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