CTNeRF: Cross-time Transformer for dynamic neural radiance field from monocular video.

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. Our code is available on https://github.com/xingy038/CTNeRF. • Presented a novel dynamic neural rendering approach for dynamic monocular videos, leveraging the aggregation of multi-view feature vectors to enhance the quality of rendering novel views. • Combining multi-frame feature vectors can lead to the loss or merging of intricate details, risking the preservation of crucial characteristics from the original data. To address this, we introduce a Ray-based cross-time transformer. • To mitigate potential blurring effects during feature aggregation, we propose the incorporation of a Global Spatio-Temporal Filter. [ABSTRACT FROM AUTHOR]