Neural Radiance Fields (NeRFs) have shown great potential in modelling 3D
scenes. Dynamic NeRFs extend this model by capturing time-varying elements,
typically using deformation fields. The existing dynamic NeRFs employ a similar
Eulerian representation for both light radiance and deformation fields. This
leads to a close coupling of appearance and motion and lacks a physical
interpretation. In this work, we propose Dynamic Appearance Particle Neural
Radiance Field (DAP-NeRF), which introduces particle-based representation to
model the motions of visual elements in a dynamic 3D scene. DAP-NeRF consists
of superposition of a static field and a dynamic field. The dynamic field is
quantised as a collection of {\em appearance particles}, which carries the
visual information of a small dynamic element in the scene and is equipped with
a motion model. All components, including the static field, the visual features
and motion models of the particles, are learned from monocular videos without
any prior geometric knowledge of the scene. We develop an efficient
computational framework for the particle-based model. We also construct a new
dataset to evaluate motion modelling. Experimental results show that DAP-NeRF
is an effective technique to capture not only the appearance but also the
physically meaningful motions in a 3D dynamic scene.

提出了基于粒子表示的动态外观粒子神经辐射场（DAP-NeRF）模型，用于捕捉 3D 动态场景中的外观和运动，通过学习单目视频构建了有效的计算框架和新的数据集来评估运动建模效果。