Existing methods for the 4D reconstruction of general, non-rigidly deforming
objects focus on novel-view synthesis and neglect correspondences. However,
time consistency enables advanced downstream tasks like 3D editing, motion
analysis, or virtual-asset creation. We propose SceNeRFlow to reconstruct a
general, non-rigid scene in a time-consistent manner. Our dynamic-NeRF method
takes multi-view RGB videos and background images from static cameras with
known camera parameters as input. It then reconstructs the deformations of an
estimated canonical model of the geometry and appearance in an online fashion.
Since this canonical model is time-invariant, we obtain correspondences even
for long-term, long-range motions. We employ neural scene representations to
parametrize the components of our method. Like prior dynamic-NeRF methods, we
use a backwards deformation model. We find non-trivial adaptations of this
model necessary to handle larger motions: We decompose the deformations into a
strongly regularized coarse component and a weakly regularized fine component,
where the coarse component also extends the deformation field into the space
surrounding the object, which enables tracking over time. We show
experimentally that, unlike prior work that only handles small motion, our
method enables the reconstruction of studio-scale motions.

利用 SceNeRFlow 提出了一种基于 dynamic-NeRF 方法的时间一致的方式来重建非刚性场景，通过将形状和外观的变形分解成粗糙和微弱的两个组成部分，使得该方法可以处理大范围的运动，并且可以重建大规模运动场景。

SceNeRFlow: 时间一致的普适动态场景重建

SceNeRFlow: Time-Consistent Reconstruction of General Dynamic Scenes

We identify principles characterizing Solomonoff Induction by demands on an
agent's external behaviour. Key concepts are rationality, computability,
indifference and time consistency. Furthermore, we discuss extensions to the
full AI case to derive AIXI.

探讨 Solomonoff 归纳的原则及其对智能体行为的影响，关键概念为理性、可计算性，冷漠和时间一致性，并讨论其扩展到 AIXI 的全面人工智能案例。