Transformer-based Single Image Deraining (SID) methods have achieved remarkable success, primarily attributed to their robust capability in capturing long-range interactions. However, we've noticed that current methods handle rain-affected and unaffected regions concurrently, overlooking the disparities between these areas, resulting in confusion between rain streaks and background parts, and inabilities to obtain effective interactions, ultimately resulting in suboptimal deraining outcomes. To address the above issue, we introduce the Region Transformer (Regformer), a novel SID method that underlines the importance of independently processing rain-affected and unaffected regions while considering their combined impact for high-quality image reconstruction. The crux of our method is the innovative Region Transformer Block (RTB), which integrates a Region Masked Attention (RMA) mechanism and a Mixed Gate Forward Block (MGFB). Our RTB is used for attention selection of rain-affected and unaffected regions and local modeling of mixed scales. The RMA generates attention maps tailored to these two regions and their interactions, enabling our model to capture comprehensive features essential for rain removal. To better recover high-frequency textures and capture more local details, we develop the MGFB as a compensation module to complete local mixed scale modeling. Extensive experiments demonstrate that our model reaches state-of-the-art performance, significantly improving the image deraining quality. Our code and trained models are publicly available athttps://github.com/ztMotaLee/Regformer.

Improving motor performance is essential for sustaining long-term sports participation. In this study, we proposed a novel observational learning scheme for full-body sports movements, aiming to enhance its effectiveness by inducing a kinesthetic illusion. To implement this scheme, we developed a virtual reality (VR) system which can apply synchronized visuo-motor and visuo-tactile stimuli. In an experiment, participants who experienced the proposed scheme reported significantly higher perceived synchrony and subjectively rated their ball trajectory accuracy as improved after they performed actual golf driver swings. These findings suggest that our proposed observational learning scheme has the potential to support performance improvement in full-body sports movements.

Many consumer cameras are equipped with 8-bit image sensors, which often struggle to capture scenes with a High Dynamic Range (HDR). This limitation can result in overexposed or underexposed regions, a loss of fine details due to low bit-depth compression, skewed color distributions, and noticeable noise in dark areas. Traditional Standard Dynamic Range (SDR) image enhancement methods typically focus on color mapping by expanding the color range and adjusting brightness. However, they often fail to restore details in dynamic range extremes, i.e. regions where pixel values approach the minimum or maximum limits. We define “exposure-limited image enhancement” as the process of enhancing images with large missing areas due to exposure issues within the SDR space, which differs from existing “mis-exposed image enhancement” methods primarily aimed at correcting color distributions. To enhance these exposure-limited images and overcome the limitations of current models, we propose a novel two-stage approach. In the first stage, we remap color and brightness to a suitable range while preserving existing details. In the second stage, we use a diffusion prior to generate content in severely overexposed or underexposed regions, which are otherwise lost during capture. Notably, this generative refinement module can also serve as a plug-and-play component alongside existing enhancement methods. Extensive experiments demonstrate that our method significantly improves image quality and detail, outperforming state-of-the-art techniques in dynamic range extremes. The project page is at https://Sagiri0208.github.io.

Hierarchical structures of motion exist across research fields, including computer vision, graphics, and robotics, where complex dynamics typically arise from coordinated interactions among simpler motion components. Existing methods to model such dynamics typically rely on manually-defined or heuristic hierarchies with fixed motion primitives, limiting their generalizability across different tasks. In this work, we propose a general hierarchical motion modeling method that learns structured, interpretable motion relationships directly from data. Our method represents observed motions using graph-based hierarchies, explicitly decomposing global absolute motions into parent-inherited patterns and local motion residuals. We formulate hierarchy inference as a differentiable graph learning problem, where vertices represent elemental motions and directed edges capture learned parent-child dependencies through graph neural networks. We evaluate our hierarchical reconstruction approach on three examples: 1D translational motion, 2D rotational motion, and dynamic 3D scene deformation via Gaussian splatting. Experimental results show that our method reconstructs the intrinsic motion hierarchy in 1D and 2D cases, and produces more realistic and interpretable deformations compared to the baseline on dynamic 3D Gaussian splatting scenes. By providing an adaptable, data-driven hierarchical modeling paradigm, our method offers a formulation applicable to a broad range of motion-centric tasks.

Low-Light Stereo Image Enhancement (LLSIE) tackles the challenge of improving the illumination and restoring the details in stereo images. However, existing deep learning-based LLSIE methods trained on high-resolution low-light images often exhibit sub-optimal performance when interacting with information from the left and right views. We find that this is because of: (1) the high computational cost arising from quadratic complexity, which hinders the enhancement model's ability to process high-resolution images; and (2) the limitations of conventional fusion strategies in previous work, which inadequately capture cross-view cues, resulting in weak feature representation and compromised detail recovery. To address these limitations, we propose a novel Dynamic Grouped Interaction Network (DGI-Net) to enhance illumination and recover more details while reducing the computational cost. Specifically, DGI-Net employs the U-Net structure, which effectively mitigates noise during the low-light enhancement. Furthermore, we design a Grouped Stereo Interaction Module (GSIM) with a grouping strategy to efficiently discover cross-view cues while minimizing computations. To dynamically fuse stereo information and fully exploit cross-view correlations, we also introduce a Dynamic Embedding Module (DEM) to establish dynamic connections between inter-view cues and intra-view features, which performs dynamic weight processing on cross-view cues to eliminate noise during fusion. For intra-view processing, we present a Diversity Enhanced Block (DEB) to extract multi-scale features, thereby improving diversity and feature representation. This multi-scale feature extraction also addresses low image contrast in dark lighting conditions. Experimental results demonstrate that DGI-Net outperforms current state-of-the-art methods in low-light stereo image enhancement.