We present Tensor4D, an efficient yet effective approach to dynamic scene modeling. The key of our solution is an efficient 4D tensor decomposition method so that the dynamic scene can be directly represented as a 4D spatio-temporal tensor. To tackle the accompanying memory issue, we decompose the 4D tensor hierarchically by projecting it first into three time-aware volumes and then nine compact feature planes. In this way, spatial information over time can be simultaneously captured in a compact and memory-efficient manner. When applying Tensor4D for dynamic scene reconstruction and rendering, we further factorize the 4D fields to different scales in the sense that structural motions and dynamic detailed changes can be learned from coarse to fine. The effectiveness of our method is validated on both synthetic and real-world scenes. Extensive experiments show that our method is able to achieve high-quality dynamic reconstruction and rendering from sparse-view camera rigs or even a monocular camera. The code and dataset will be released at https://liuyebin.com/tensor4d/tensor4d.html.

翻译：我们提出对动态场景建模的一种高效而有效的方法Tensor4D。 我们解决方案的关键是高效的 4D 感应分解法, 这样动态场景可以直接作为 4D spatio- 时空阵列来代表。 为了解决伴随的记忆问题, 我们通过先将其投射成三个有时间觉的体积然后再投射成九个紧凑的特征平面, 分层分解4D 。 这样, 时空信息可以以紧凑和记忆高效的方式同时捕捉。 当应用 Tensor4D 进行动态场重建与演化时, 我们进一步将4D 字段分解到不同的尺度, 也就是说, 结构动作和动态的详细变化可以从粗糙到细。 我们的方法的有效性在合成和现实世界的场中都得到验证。 广泛的实验表明, 我们的方法能够实现高质量的动态重建, 并且从稀有摄像机甚至从一个单色摄像机中提取。 代码和数据集将在 https://liuyebin.com/tenor4d/tenor4d/tenor4d.html html上发布。