#01Nov 2, 2026
cs.CV
3D Gaussian Splatting within Self-Learned Neural View-Dependent Color Fields
Yixiao Yang, Yu-Shen Liu, Zhizhong Han
3D Gaussian Splatting (3DGS) has become a vital tool in novel view synthesis. 3D Gaussian functions with attributes like color, opacity, and scale are stacked together to explicitly model a radiance field. However, discrete 3D Gaussians usually produce poor depth with few geometry details but lots of floaters, which hinders the effective modeling of smooth radiance fields. To address this issue, we propose to learn a radiance field within a continuous view-dependent color field parameterized by neural networks. With continuous constraints in color, the bias on low-frequency signals of neural networks can highly encourage the geometry modeling to contribute to high-frequency color variations on images, getting rid of solely relying on the color attribute itself. Moreover, we impose a constraint to improve the Lipschitz continuity of the neural color field, making large changes of each Gaussian's color also match with large changes of its position in terms of a ratio. To this end, we additionally introduce novel self-learning strategies to improve the learning of the neural view-dependent color field without any external knowledge or priors, aiming for better generalization on unseen scenes. Our numerical and visual comparisons on widely used benchmarks justify our idea and show better ability of high fidelity geometry recovery in 3DGS. Project page: https://github.com/yangyixi21/Neural-Color-Fields-3DGS.
#02Nov 2, 2026
eess.IV
Shared-Pattern Collaborative Network for Multi-Source Remote Sensing Data Classification
Chenfang Yang, Chuanzheng Gong, Feng Gao and 2 more
Multi-source remote sensing data classification has received growing attention recently. However, existing methods still struggle to jointly model long-range contextual dependencies and shared spatial patterns across heterogeneous modalities. To address this issue, we propose a Shared-Pattern Collaborative Network (SPCNet) for HSI and SAR/LiDAR joint classification. To enhance cross-modal collaborative learning, Shared-Pattern Collaborative Block (SPCB) is designed by introducing a shared large-kernel operator and a dynamic convolution branch, enabling the network to capture common spatial structures and content-adaptive local features. Furthermore, Region-Adaptive Modality Allocator (RAMA) is developed to assign spatially varying modality weights for reliable feature fusion. Experiments on Houston2013, Augsburg, and Houston2018 datasets demonstrate that SPCNet achieves superior classification performance over several state-of-the-art methods. The code will be made publicly available at https://github.com/oucailab/SPCNet
#03Nov 2, 2026
cs.CV
VideoEditor-MoE: Scaling Complex Video Editing with Mixture of Experts
Xiao Cao, Fuchen Long, Yansong Qu and 5 more
Complex video editing requires understanding complex instructions while jointly fulfilling multiple editing requests. Meeting these demands through dense model scaling substantially increases computational cost. We introduce VideoEditor-MoE, the first open-source mixture-of-experts framework for video editing. Beyond sparse MLP-MoE layers, MoE-Attention expands attention capacity through routed key--value experts while preserving global interactions among video tokens. Initialized from a 5B video editing model, VideoEditor-MoE scales to 14B and 56B parameters, with approximately 8B parameters activated per token in both variants. Comparisons with existing methods and two complementary baselines, Explicit-MoE and MLP-MoE, assess its benefits over explicit expert guidance and MLP-only expert architectures. Further analyses demonstrate that all experts remain active and reveal that learned routing responds more strongly to sample content and diffusion noise levels than to human-defined editing categories. Spatial analysis further indicates sensitivity to object-level semantic features. We also identify decoupled weight decay as a strategy for stabilizing expert scaling.
#04Nov 2, 2026
cs.CV
Ray-SCR: Generalizing Scene Coordinate Regression Across Camera Models
Jaemin Cho, Oleksii Nasypanyi, Francois Rameau
Among visual localization strategies, scene coordinate regression (SCR) has emerged as an attractive approach for its compactness, accuracy, and speed. Despite these advantages, SCR models generalize poorly across substantially different camera models, such as omnidirectional or strongly rectified imagery. This limitation arises because, changes in field of view, lens distortion, or rectification-induced resampling can make the same scene content appear differently depending on its pixel position, degrading localization accuracy. To cope with this limitation, we introduce Ray-SCR, a lightweight extension to existing SCR architectures that conditions local visual features on their calibrated viewing rays and refines neighboring ray-conditioned features with a shallow local Transformer. We further formulate supervision directly in ray space and use sparse SfM points as metric anchors that link observations across views and camera models. Taken together, these components enable a single scene representation built from a panoramic mapping sequence to accurately localize query images captured with very different cameras, including pinhole, fisheye, and omnidirectional cameras. We evaluate Ray-SCR across multiple SCR architectures and on datasets with pinhole, fisheye, and omnidirectional cameras. The proposed ray conditioning substantially improves generalization across cameras and leads to state-of-the-art localization performance in these challenging cross-camera settings. The project page is available at https://jaeminch0.github.io/ray-scr-generalizing-scene-coordinate-regression-across-camera-models/.
#05Nov 2, 2026
cs.CV
Comprehensive Evaluation of Monocular and Multi-view 3D Human Pose Estimation Models for Fitness Exercise Analysis
Néstor Ortega, Agustín Salgado, Javier Sánchez
Three-dimensional human pose estimation has become a fundamental component of computer vision systems for sports analytics, rehabilitation, and motion understanding. Although recent deep learning approaches have achieved remarkable accuracy on standard benchmarks, their performance under realistic sports scenarios remains insufficiently studied. Furthermore, the trade-offs between recent monocular and multi-view approaches, as well as the benefits of post-processing techniques for improving temporal consistency, have not been systematically evaluated on fitness-oriented datasets. This paper evaluates recent state-of-the-art 3D human pose estimation methods on Fit3D, a dataset of synchronized multi-view recordings of a wide variety of fitness exercises with accurate ground-truth annotations. We study two recent monocular lifting approaches, TCPFormer and PoseMamba-L, and a multi-view model, MVGFormer, under a unified evaluation protocol. To investigate the influence of the input data, monocular methods are evaluated using ground-truth keypoints and detections generated by recent 2D pose estimators, RTMPose and YOLO-Pose. Additionally, we analyze the effectiveness of three temporal filters for reducing prediction jitter while preserving motion dynamics. Experimental results provide a detailed comparison of accuracy, temporal consistency, and robustness to imperfect 2D detections across the evaluated methods. Our analysis highlights the impact of detector quality on lifting-based approaches, compares transformer- and state-space-based temporal architectures, and discusses the practical trade-offs between monocular and multi-view systems for fitness motion analysis. Our results show that 2D detection quality, temporal modeling, and post-processing must be considered jointly in pose estimation pipelines for exercise analysis.