RGB-to-RAW reconstruction, or the reverse modeling of a camera Image Signal Processing (ISP) pipeline, aims to recover high-fidelity RAW data from RGB images. Despite notable progress, existing learning-based methods typically treat this task as a direct regression objective and still struggle with detail inconsistency and color deviation, due to the ill-posed nature of inverse ISP and the inherent information loss in quantized RGB images. To address these limitations, we pioneer a generative perspective by reformulating RGB-to-RAW reconstruction as a deterministic latent transport problem and introduce a novel framework named RAW-Flow, which leverages flow matching to learn a deterministic vector field in latent space, to effectively bridge the gap between RGB and RAW representations and enable accurate reconstruction of structural details and color information. To further enhance latent transport, we introduce a cross-scale context guidance module that injects hierarchical RGB features into the flow estimation process. Moreover, we design a Dual-domain Latent Autoencoder (DLAE) with a feature alignment constraint to support the proposed latent transport framework, which jointly encodes RGB and RAW inputs while promoting stable training and high-fidelity reconstruction. Extensive experiments demonstrate that RAW-Flow outperforms state-of-the-art approaches both quantitatively and visually.

Learning-based approaches have achieved promising progress in High Dynamic Range (HDR) image reconstruction, particularly in ghost removal. However, they often struggle in ill-posed regions, such as areas with occlusion or saturation, where insufficient or unreliable information leads to persistent residual ghosting artifacts and structural distortions. In this paper, we present UA-Diff, an uncertainty-aware diffusion framework designed to generate visually coherent, ghost-free HDR images. Specifically, our approach introduces an Uncertainty Generation Module (UGM) that estimates pixel-wise reconstruction confidence via a probabilistic Laplacian loss, producing an uncertainty map that explicitly highlights challenging ill-posed regions. To address these regions effectively, we develop an Uncertainty-Aware Diffusion Module (UADM) that operates selectively on the average-coefficient component of a 2D discrete wavelet transform, where dominant artifacts tend to concentrate. This enables reduced computational overhead while preserving high-quality details. Moreover, we propose an Uncertainty-Guided Sampling (UGS) strategy that leverages the uncertainty map to guide the denoising process, ensuring faithful reconstruction in reliable regions and targeted refinement in uncertain areas. Extensive experiments on three public HDR benchmarks demonstrate that UA-Diff surpasses state-of-the-art methods both quantitatively and perceptually, especially in challenging ill-posed scenarios.

Robots can acquire complex manipulation skills by learning policies from expert demonstrations, which is often known as vision-based imitation learning. Generating policies based on diffusion and flow matching models has been shown to be effective, particularly in robotic manipulation tasks. However, recursion-based approaches are inference inefficient in working from noise distributions to policy distributions, posing a challenging trade-off between efficiency and quality. This motivates us to propose FlowPolicy, a novel framework for fast policy generation based on consistency flow matching and 3D vision. Our approach refines the flow dynamics by normalizing the self-consistency of the velocity field, enabling the model to derive task execution policies in a single inference step. Specifically, FlowPolicy conditions on the observed 3D point cloud, where consistency flow matching directly defines straight-line flows from different time states to the same action space, while simultaneously constraining their velocity values, that is, we approximate the trajectories from noise to robot actions by normalizing the self-consistency of the velocity field within the action space, thus improving the inference efficiency. We validate the effectiveness of FlowPolicy in Adroit and Metaworld, demonstrating a 7× increase in inference speed while maintaining competitive average success rates compared to state-of-the-art methods.

We present a novel deep camera path optimization framework for minimum latency online video stabilization. Typically, a stabilization pipeline consists of three steps: motion estimation, path smoothing, and novel view synthesis. Most previous methods concentrate on motion estimation while path optimization receives less attention, particularly in the crucial online setting where future frames are inaccessible. In this work, we adopt off-the-shelf high-quality deep motion models for motion estimation and focus only on the path optimization. Specifically, our camera path smoothing network takes a short 2D camera path in a sliding window as input and outputs the stabilizing warp field of the last frame, which warps the coming frame to its stabilized position. We explore three motion densities: a global single camera path, local mesh-based bundled paths, and dense flow paths. A hybrid loss and an efficient motion smoothing attention (EMSA) module are proposed for spatially and temporally consistent path smoothing. Moreover, we build a motion dataset that contains stable and unstable motion pairs for training. Extensive experiments demonstrate that our method surpasses state-of-the-art online stabilization methods and rivals the performance of offline methods, offering compelling advancements in the field of video stabilization.

This paper presents a novel network structure with illumination-aware gamma correction and complete image modelling to solve the low-light image enhancement problem. Low-light environments usually lead to less informative large-scale dark areas, directly learning deep representations from low-light images is insensitive to recovering normal illumination. We propose to integrate the effectiveness of gamma correction with the strong modelling capacities of deep networks, which enables the correction factor gamma to be learned in a coarse to elaborate manner via adaptively perceiving the deviated illumination. Because exponential operation introduces high computational complexity, we propose to use Taylor Series to approximate gamma correction, accelerating the training and inference speed. Dark areas usually occupy large scales in low-light images, common local modelling structures, e.g., CNN, SwinIR, are thus insufficient to recover accurate illumination across whole low-light images. We propose a novel Transformer block to completely simulate the dependencies of all pixels across images via a local-to-global hierarchical attention mechanism, so that dark areas could be inferred by borrowing the information from far informative regions in a highly effective manner. Extensive experiments on several benchmark datasets demonstrate that our approach outperforms state-of-the-art methods.

We present a novel camera path optimization framework for the task of online video stabilization. Typically, a stabilization pipeline consists of three steps: motion estimating, path smoothing, and novel view rendering. Most previous methods concentrate on motion estimation, proposing various global or local motion models. In contrast, path optimization receives relatively less attention, especially in the important online setting, where no future frames are available. In this work, we adopt recent off-the-shelf high-quality deep motion models for motion estimation to recover the camera trajectory and focus on the latter two steps. Our network takes a short 2D camera path in a sliding window as input and outputs the stabilizing warp field of the last frame in the window, which warps the coming frame to its stabilized position. A hybrid loss is well-defined to constrain the spatial and temporal consistency. In addition, we build a motion dataset that contains stable and unstable motion pairs for the training. Extensive experiments demonstrate that our approach significantly outperforms state-of-the-art online methods both qualitatively and quantitatively and achieves comparable performance to offline methods. Our code and dataset are available at https://github.com/liuzhen03/NNDVS.