Illumination and texture rerendering are critical dimensions for world-to-world transfer, which is valuable for applications including sim2real and real2real visual data scaling up for embodied AI. Existing techniques generatively re-render the input video to realize the transfer, such as video relighting models and conditioned world generation models. Nevertheless, these models are predominantly limited to the domain of training data (e.g., portrait) or fall into the bottleneck of temporal consistency and computation efficiency, especially when the input video involves complex dynamics and long durations. In this paper, we propose **TC-Light**, a novel paradigm characterized by the proposed two-stage post optimization mechanism. Starting from the video preliminarily relighted by an inflated video relighting model, it optimizes appearance embedding in the first stage to align global illumination. Then it optimizes the proposed canonical video representation, i.e., **Unique Video Tensor (UVT)**, to align fine-grained texture and lighting in the second stage. To comprehensively evaluate performance, we also establish a long and highly dynamic video benchmark. Extensive experiments show that our method enables physically plausible re-rendering results with superior temporal coherence and low computation cost. The code and video demos are available at our [Project Page](https://dekuliutesla.github.io/tclight/).

Aiming at the closed water detection in remote sensing imagery at medium-low resolution, this paper proposes a novel method for closed water detection based on fusion detection which conducts detection via informative fused images blended by Synthetic Aperture Radar (SAR) and optical images. Firstly, it utilizes SAR and optical image pairs containing the same closed water object to generate aligned image pairs according to latitude and longitude information. Next, generative adversarial network (GAN) is adopted to fuse two categories of images. At last, a target detection network driven by optical image samples is used to detect the closed water on the fused image. The experiment result on Sentinel-1&2 shows that the proposed method can effectively make up for the shortage of SAR image in closed water detection and improve the detection performance.

Ship change detection is of great significance for maritime safety supervision, wharf vessel management, and vessel life cycle analysis. Nowadays, the ship change information is obtained through the difference between the multiple independent object detection results of multi-temporal images. However, it neglects the temporal correlation of the concerned features, impeding further improvement of the detection accuracy of change detection. Therefore, based on the sequential network, namely convolution LSTM, we built an end-to-end ship change detection (SCD) R-CNN network. The network extracts abstract semantic information reflecting the changed features of ships, and the time correlation between features of the different phases is established. Specifically, the changed features are utilized to guide the judgement of ship change. It is verified from the RS images that the proposed network avoids the misjudgment caused by the errors of object detection in the conventional method. In addition, a higher efficiency is revealed, maintaining the accuracy of the change detection of ship targets.