CTNeRF: Cross-Time Transformer for Dynamic Neural Radiance Field from
  Monocular Video
Xingyu Miao Yang Bai Haoran Duan Yawen Huang Fan Wan Yang Long Yefeng Zheng
The goal of our work is to generate high-quality novel views from monocular
videos of complex and dynamic scenes. Prior methods, such as DynamicNeRF, have
shown impressive performance by leveraging time-varying dynamic radiation
fields. However, these methods have limitations when it comes to accurately
modeling the motion of complex objects, which can lead to inaccurate and blurry
renderings of details. To address this limitation, we propose a novel approach
that builds upon a recent generalization NeRF, which aggregates nearby views
onto new viewpoints. However, such methods are typically only effective for
static scenes. To overcome this challenge, we introduce a module that operates
in both the time and frequency domains to aggregate the features of object
motion. This allows us to learn the relationship between frames and generate
higher-quality images. Our experiments demonstrate significant improvements
over state-of-the-art methods on dynamic scene datasets. Specifically, our
approach outperforms existing methods in terms of both the accuracy and visual
quality of the synthesized views.