Tag
RealMe 是一个基于高分辨率视频扩散模型的统一框架，能从稀疏、低分辨率的多视角采集生成 2K 分辨率且 4D 一致的沉浸式体积视频，通过布局感知注意力和频域潜空间监督，在 DNA-Rendering 基准和自建 RealVV 数据集上超越现有方法。
GeoState提出了一种视角解耦的几何表示，将场景动态与相机运动分离以实现预测性3D世界建模，在Waymo上超越先前方法，并可零样本泛化到KITTI和nuScenes。
WorldRecon 提出一种生成式多视角网格重建框架，可从无位姿 RGB 图像重建大规模 3D 场景的完整网格。它将 TRELLIS.2 生成模型与重建式条件架构结合，通过邻域感知潜在修补和因果世界生成保证场景级一致性，在合成与真实室内场景上分别将 Chamfer 距离降低 14.9% 和 43.1%，达到最先进性能。
Drive3R 提出一种块状流式几何基础模型，通过块状因果注意力和置信度感知重锚定实现可扩展的长时程多视图 3D 重建，在 nuScenes、KITTI 和 Waymo 上达到新的最先进水平。
T3lescope提出一种单一固定分辨率生成器的推理时粗到细级联方法，从多视角图像实现跨尺度（从单物体到大型户外场景）的任意分辨率高保真3D场景网格重建，无需逐场景优化，在室内、户外和城市规模场景上超越了前馈与生成基线。
SpatialSpeak introduces a two-stage framework for spatial reasoning in vision-language models, using QA-native reconstruction pretraining and spatial chain-of-thought learning to achieve state-of-the-art performance on benchmarks.
VGGT-Diff is a geometry-routed multi-view diffusion model that integrates visual geometry latents with a pretrained diffusion model to improve sparse-view novel view synthesis, achieving competitive or state-of-the-art performance.
LiveGrid is a free browser-based tool that lets users watch multiple live streams from platforms like Twitch, YouTube, and Kick in a customizable grid layout for events such as esports and sports.
A research paper on bundle adjustment for any image set using multi-view matching and monocular priors, achieving state-of-the-art results in computer vision.
CoVeR is a training-free spatial token selector that improves 3D reasoning in Vision-Language Models by enforcing exact token budgets and full scene coverage, outperforming prior state-of-the-art methods.
A paper from the DUSt3R team proposes sparse auto-regressive modeling for 3D scene generation from multi-view images, using a voxel-aligned 3D latent space and an occupancy-aware masked autoregressive transformer.
Presents MBDiff, a multi-view behavior-aware diffusion model for probabilistic utility data imputation that learns user behavior from global, local, and instance-level views and uses a conditional attentional denoising network. Evaluated on real utility data from Florida, it outperforms state-of-the-art baselines.
DSTFView is a dual-input spatio-temporal-frequency multi-view framework for cloud-edge workload forecasting, jointly modeling closeness and period dependencies with an adaptive fusion mechanism to capture abrupt changes.
CodeNib is a multi-view data system that serves repository context to coding agents by building reusable lexical, dense, and structural views per commit, enabling faster updates and efficient context serving.
G-MAD is an open-source framework using Arma 3 to generate synchronized multi-view RGB-T data for aerial object detection, addressing limitations of real-world datasets. It also introduces the AMOD benchmark.
MultiView-Bench is a diagnostic benchmark for evaluating vision-language models on their ability to integrate multiple viewpoints into a coherent 3D mental model, revealing systematic failures in 3D spatial reasoning, and introducing ViewNavigator to mitigate these issues.
Deform360 is a large-scale visuotactile dataset with 198 objects and over 215 hours of observations for studying deformable object dynamics, enabling comparison between 2D video and 3D particle world models for robotic manipulation.
Proposes a method to embed graphs in high-dimensional space and search for informative 2D viewpoints that optimize aesthetic and readability metrics, enabled by a novel differentiable surrogate for edge crossings. Introduces an interactive system, DataFly, for exploring multiple candidate viewpoints.
This paper proposes a feed-forward framework that decomposes 3D scenes into instance-structured token groups from unposed multi-view images, enabling direct object-level reconstruction, segmentation, and manipulation without 3D annotations.
MVTrack4Gen introduces a training framework that uses multi-view point tracking as geometric supervision to enhance motion-aware diffusion models, achieving state-of-the-art geometric consistency and motion fidelity in novel-view video generation from monocular video.