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Search papers from 30+ top conferences, including NeurIPS, ICLR, ICML, CVPR, ACL and AAAI. Filter by keyword, then open any paper for its abstract, authors and links.

ECCV 2026

ANFI: Rethinking Neighbor Feature Interaction in Person Re-ID

Xulin Li, Yan Lu, Bin Liu, Jiaze Li, Qinhong Yang, Tao Gong, Qi Chu, Nenghai Yu

In person re-identi_x001C_cation, neighbor-based methods haveachieved signi_x001C_cant success by interacting with neighbor samples to ob-tain more robust representations. However, existing methods rely onlyon a_x001E_nity relations, causing their success to depend heavily on the re-liability of selected neighbors. We _x001C_nd that a_x001E_nity-only interaction of-ten fails in challenging scenarios due to the inevitable presence of noisyneighbors. To enable e_x001B_ective interactions under noisy neighborhoods, werevisit neighbor-based methods under distinct reliability conditions andpropose a novel Adaptive Neighbor Feature Interaction (ANFI)method. The core idea of ANFI is to account for negative e_x001B_ects fromnoisy neighbors, allowing samples to remain distinguishable from falsepositive neighbors. Unlike existing methods, ANFI models not only a_x001E_n-ity relations but also discrepancy relations, and employs sample-wiseadaptive weighting for these two types of relations. Given that capturingnegative e_x001B_ects from noisy neighbors di_x001B_ers signi_x001C_cantly from traditionalrelation learning, we derive discrepancy relations from a new neighbor-hood similarity, which provides more information than pairwise sim-ilarity. In addition, we propose Noisy Relation Supervision (NRS)to train ANFI, gradually injecting robustness to noisy relations into themodel. Extensive experiments conducted under standard, cross-modal,and cross-domain settings, including comparisons with neighbor-basedmethods and re-ranking methods, demonstrate the superiority of ourmethod across various neighbor distributions.

ECCV 2026

VGEdit: Unlocking Video Generation Priors for Reasoning-Informed Image Editing

Haiquan Lu, Gongfan Fang, Xinyin Ma, Xinchao Wang

Image editing has become an important capability of modernvisual generative models. Most existing approaches generate the targetimage directly given an input image and an editing instruction. Whileeffective for simple appearance modifications, this paradigm often failswhen edits require multi-step inference over spatial, temporal, causal, orcommonsense relationships, which entails reasoning about how the sceneshould evolve through a series of plausible intermediate states from thesource image to the desired result. Notably, modern video generationmodels naturally capture such transformation processes: by modelinghow scenes evolve over time, they encode strong temporal coherence andimplicit physical priors. However, directly applying video models to imageediting remains challenging due to the mismatch between editing instruc-tions and video generation objectives. To bridge this gap, we proposeVGEdit, a reinforcement learning framework that empowers video gener-ation models for image editing. Our method consists of two components:(1) Instruction transfer : an MLLM converts the editing instruction into avideo description, reframing the edit as a plausible transformation process.(2) Reinforcement learning: we adapt the video model with reward-basedoptimization, treating intermediate frames as implicit reasoning chainsand the last frame as the target edited image. This formulation eliminatesthe need for ground-truth editing videos and enables direct optimizationfrom rewards that capture instruction faithfulness, contextual consistency,and user preference alignment. Experiments show that VGEdit signifi-cantly outperforms prior methods on reasoning-informed image editingbenchmarks, demonstrating that connecting image editing and videogeneration in a process-centric manner provides a scalable path towardmore physically grounded and reasoning-capable visual editing.

ECCV 2026

Beyond Inpainting: Unleash 3D Understanding for Stable Camera-Controlled Video Re-rendering

Dongyu Chen, Yixin Guo, Shuojin Yang, Tai-Jiang Mu, Shimin Hu

Camera control has been extensively studied in conditional video generation; however, stable altering camera trajectories while faithfully preserving video content remains a challenging task. The mainstream approach to achieving camera control is warping a 3D representation according to the target trajectory. However, such methods fail to fully leverage the 3D priors of video diffusion models (VDMs) and often fall into the Inpainting Trap, resulting in subject inconsistency and degraded generation quality. To address this problem, we propose DepthDirector, a video re-rendering framework with stable camera controllability. Our key insight is to decompose the conditional injection of video content and camera trajectories by leveraging the depth video from explicit 3D representation as camera-control guidance. Specifically, we design a View-Content Dual-Stream Condition mechanism, enabling VDMs to comprehend camera movements and leverage their 3D understanding capabilities. Additionally, we construct a large-scale multi-camera synchronized dataset named MultiCamWarp Dataset using Unreal Engine 5. Extensive experiments show that DepthDirector outperforms existing methods. Our code and dataset will be publicly available on our project website: https://eleanor6725.github.io/DepthDirector/.

ECCV 2026

CoMaTrack: Competitive Multi-Agent Game-Theoretic Tracking with Vision-Language-Action Models

Li Gao, Liu Liu, Mingyang Lyu, Yang Cai

Embodied Visual Tracking (EVT) requires an agent to con-tinuously follow a language-specified target from egocentric observations.Most existing methods adopt single-agent imitation learning, which de-pends on expert demonstrations and can struggle to generalize to sce-narios beyond the training distribution. Inspired by competition-drivencapability evolution in nature, we propose CoMaTrack, a competitivemulti-agent reinforcement learning (RL) framework that trains agentsin a dynamic adversarial setting with competitive subtasks, yieldingstronger adaptive planning and interference-resilient strategies. We fur-ther propose CoMaTrack-Bench, a Habitat-based benchmark for language-conditioned competitive EVT, featuring dynamic dueling scenarios be-tween a tracker and adaptive opponents across diverse environments andinstructions, enabling standardized robustness evaluation under activeadversarial interactions. Experiments show that CoMaTrack achievesstate-of-the-art results on both standard benchmarks and CoMaTrack-Bench. Notably, a 3B VLM trained with our framework surpasses pre-vious single-agent imitation learning methods based on 7B models onthe challenging EVT-Bench, achieving 92.1% in STT, 74.2% in DT, and57.5% in AT.

ECCV 2026

V-Co: A Closer Look at Visual Representation Alignment via Co-Denoising

Han Lin, Xichen Pan, Zun Wang, Yue Zhang, Chu Wang, Jaemin Cho, Mohit Bansal

Pixel-space diffusion has recently re-emerged as a strong al-ternative to latent diffusion, enabling high-quality generation withoutpretrained autoencoders. However, standard pixel-space diffusion mod-els receive relatively weak semantic supervision and are not explicitlydesigned to capture high-level visual structure. Recent representation-alignment methods (e.g., REPA) suggest that pretrained visual featurescan substantially improve diffusion training, and visual co-denoising hasemerged as a promising direction for incorporating such features intothe generative process. However, existing co-denoising approaches of-ten entangle multiple design choices, making it unclear which are trulyessential. We therefore present V-Co, a systematic study of visual co-denoising in a unified JiT-based framework. This controlled setting allowsus to isolate the ingredients that make visual co-denoising effective. Ourstudy reveals two main ingredients. First, co-denoising benefits from pre-serving feature-specific computation while enabling flexible cross-streaminteraction, which leads to a fully dual-stream architecture together witha structurally defined unconditional prediction for classifier-free guid-ance. Second, it requires both stronger semantic supervision and propercross-stream calibration, which we realize through a perceptual-driftinghybrid loss and RMS-based feature rescaling. Together, these findingsyield a simple recipe for visual co-denoising. Experiments on ImageNet-256 show that, at comparable model sizes, V-Co outperforms the un-derlying pixel-space diffusion baseline and strong prior pixel-diffusionmethods while using fewer training epochs, offering practical guidancefor future representation-aligned generative models.

ECCV 2026

Training-free Cross-domain Few-shot Segmentation via Robust Semantic Representation and Matching

Sujun Sun, Mingwu Ren, Haofeng Zhang

Cross-domain Few-shot Segmentation (CD-FSS) aims to tra-nsfer knowledge learned from source domain to distinct target domains,segmenting unseen target classes with only a few annotated samples.Although existing methods have made significant progress, they stillrely on training or fine-tuning processes, which incur high computationalcosts and risk overfitting. We observe that when powerful and general-purpose vision foundation models are incorporated into these methods,their performance shows only marginal improvement or even degradesdue to overfitting. To address this, we eliminate trainable parameters andpropose a training-free framework to avoid both training overhead andoverfitting. Built upon the self-supervised vision encoder DINOv3, ourframework addresses cross-domain challenges through three core mod-ules. First, the Semantic-aware Feature Re-fusion (SAFR) module iden-tifies and re-fuses features that emphasize semantic patterns, generatingrepresentations with enhanced semantic discriminability. Additionally,the Adaptive Support Enhancement (ASE) module narrows semanticgaps between support and query through robust query information ag-gregation. Finally, the Hybrid Prototype Matching (HPM) module inte-grates matching results from diverse prototypes to adapt to varying se-mantic complexity across domains. Extensive experiments on four targetdomain datasets demonstrate that our method achieves state-of-the-artperformance in CD-FSS without any training. Our code is available athttps://github.com/Sparkling-Water/RSRM.

ECCV 2026

Ultra3D: Efficient and High-Fidelity 3D Generation with Part Attention

YiWen Chen, Zhihao Li, Yihao Luo, Yikai Wang, Zhang Hu, Le Li, Qin Li, Chi Zhang, Guosheng Lin

Recent advances in sparse voxel representations have significantly improved the quality of 3D content generation, enabling highresolution modeling with fine-grained geometry. However, existing frameworks suffer from severe computational inefficiencies due to the quadratic complexity of attention mechanisms in their two-stage diffusion pipelines. In this work, we propose Ultra3D, an efficient 3D generation framework that significantly accelerates sparse voxel modeling without compromising quality. Our method leverages the compact VecSet representation to efficiently generate a coarse object layout in the first stage, reducing token count and accelerating voxel coordinate prediction. To refine pervoxel latent features in the second stage, we introduce Part Attention, a geometry-aware localized attention mechanism that restricts attention computation within semantically consistent part regions. This design preserves structural continuity while avoiding unnecessary global attention, achieving up to a 6.7× speed-up in latent generation. To support this mechanism, we construct a scalable part annotation pipeline that converts raw meshes into part-labeled sparse voxels. Extensive experiments demonstrate that Ultra3D supports high-resolution 3D generation at 1024 resolution and achieves state-of-the-art performance in both visual fidelity and user preference.

ECCV 2026

A scalar per patch from pre-trained ViTs enables fast moving navigation in the real world

Steeven JANNY, Leonid Antsfeld, Christian Wolf

Trained policies for real-world robotics rely on computer vi-sion components, typically in the form of pre-trained visual encoders.These encoders are an essential component and it has been shown thattheir power does not emerge from training on robotics downstream lossesalone. Pre-training with auxiliary losses in the form of computer-visionpre-text tasks is a defining factor and heavily conditions agent perfor-mance in robotics tasks. In this unprecedented large-scale study, we ran966 navigation episodes of static point goal navigation in a real-worldbuilding for 24km and asked which components really matter for thecomputer vision aspects of robotics: we evaluate state-of-the art visualencoders in realistic conditions. We explore the usefulness of heteroge-neous multi-teacher distillation leading to encoders with multiple dif-ferent and complementary skills. We investigate how much informationfrom these encoders is necessary for robotics by bottlenecking them in aprincipled and “spatially useful” way and we show that this leads to theemergence of interpretable features linked to affordances. We also arguethat training policies on RGB data alone does not lead to an optimalusage of visual features and show this by finetuning policies pre-trainedon privileged information. All in all, we paint a more complete picture ofwhat aspects of computer vision are relevant for real-world navigation.

ECCV 2026

Text-Guided 6D Object Pose Rearrangement via Closed-Loop VLM Agents

Sangwon Baik, Gunhee Kim, Mingi Choi, Hanbyul Joo

Vision-Language Models (VLMs) exhibit strong visual rea-soning capabilities, yet they still struggle with 3D understanding. Inparticular, VLMs often fail to infer a text-consistent goal 6D pose of atarget object in a 3D scene. However, we find that with some inference-time techniques and iterative reasoning, VLMs can achieve dramatic per-formance gains. Concretely, given a 3D scene represented by an RGB-Dimage (or a compositional scene of 3D meshes) and a text instructionspecifying a desired state change, we repeat the following loop: observethe current scene; evaluate whether it is faithful to the instruction; pro-pose a pose update for the target object; apply the update; and ren-der the updated scene. Through this closed-loop interaction, the VLMeffectively acts as an agent. We further introduce three inference-timetechniques that are essential to this closed-loop process: (i) multi-viewreasoning with supporting view selection, (ii) object-centered coordinatesystem visualization, and (iii) single-axis rotation prediction. Withoutany additional fine-tuning or new modules, our approach surpasses priormethods at predicting the text-guided goal 6D pose of the target object.It works consistently across both closed-source and open-source VLMs.Moreover, when combining our 6D pose prediction with simple robot mo-tion planning, it enables more successful robot manipulation than recentVision-Language-Action models (VLAs). Finally, we conduct an ablationstudy to demonstrate the necessity of each proposed technique.

ECCV 2026

SynHMR: Synergistic Joint-Mesh Modeling for LiDAR-based Human Mesh Reconstruction

Rui Shi, Xiaoqi An, Lin Zhao, Di Wang, Chen Gong, Le Zhang

LiDAR-based Human Mesh Reconstruction (HMR) is crucialfor understanding human activities in real-world environments. How-ever, the inherent sparsity of LiDAR point clouds often causes severeloss of anatomical details, limiting reconstruction accuracy. Althoughrecent methods have made promising progress, most still follow a decou-pled pipeline that first estimates skeletal joints and then reconstructsthe mesh, leading to error propagation under sparse observations. Toaddress this issue and fully exploit both the topological guidance of theskeleton on the mesh and the spatial constraints of the surface mesh onthe skeleton, we propose SynHMR, a Synergistic Joint-Mesh Modelingframework for robust LiDAR-based HMR. Specifically, we first initializeboth the skeletal joints and the mesh vertices as queries equipped withpositional information for co-optimization. We then design a Geometry-Aware Transformer that leverages point cloud features to apply the at-tention mechanism between joint and vertex queries, enabling their it-erative refinement through mutual information exchange. Furthermore,we introduce a Noise-Augmented Learning strategy that injects per-turbed joint queries during training. This reduces the impact of noisewhile encouraging bidirectional association between joints and mesh. Ex-tensive experiments on LiDARHuman26M, SLOPER4D, and Human-M3 demonstrate that SynHMR achieves state-of-the-art performanceamong LiDAR-based HMR approaches. The project page is availableat https://github.com/ShiRui1208/SynHMR.

ECCV 2026

SyncVL: Synchronizing Vision ⟷ Language Using Unsupervised Adaptation

Maria Marrium, Muhammad Haris Khan, Sajid Javed, Arif Mahmood

Contrastive vision–language models (VLMs) such as CLIP,EVACLIP, ImageBind, and SigLIP exhibit impressive zero-shot general-ization, yet their performance on specific data distributions can furtherbe enhanced by improving the synchronization of the vision and languagerepresentations. For this purpose, we present SyncVL, a bi-directionalVL synchronization framework that enables fully unsupervised adapta-tion of VLMs. SyncVL jointly refines visual and textual representationsthrough two lightweight transformer-based encoder-decoder synchroniz-ers (Vision-to-Text and Text-to-Vision) that progressively project VLrepresentation into a shared latent subspace without requiring labeleddata. To ensure robust synchronization, we form groups using multipleimage-views and text templates. Both synchronizers are trained using areward function motivated by group relative policy optimization whilekeeping the original VLMs frozen. Additionally, a mutual-distillationmechanism is proposed to iteratively reinforce each synchronizer. Weextensively evaluate SyncVL on 24 benchmark datasets spanning unsu-pervised VL adaptation, out-of-distribution matching, unsupervised clus-tering, cross-modal retrieval, object detection, and segmentation. Acrossall tasks, SyncVL consistently improves over state-of-the-art baselines,establishing a new standard for unsupervised adaptation of VLMs.

ECCV 2026

OmniStream: Mastering Perception, Reconstruction and Action in Continuous Streams

Yibin Yan, Jilan Xu, Shangzhe Di, Haoning Wu, Weidi Xie

Autonomous agents require representations that are gen-eral, causal, and physically structured to operate in real-time stream-ing environments. However, current vision foundation models remainfragmented, specializing narrowly in image semantic perception, offlinetemporal modeling, or spatial geometry. This paper introduces Om-niStream, a unified streaming visual backbone that effectively per-ceives, reconstructs, and acts from diverse visual inputs. By incorporat-ing causal spatiotemporal attention and 3D rotary positional embeddings(3D-RoPE), our model supports efficient, frame-by-frame online process-ing of video streams via a persistent KV-cache. We pre-train OmniStreamusing a synergistic multi-task framework coupling static and temporalrepresentation learning, streaming geometric reconstruction, and vision-language alignment on 29 datasets. Extensive evaluations show that, evenwith a strictly frozen backbone, OmniStream achieves consistently com-petitive performance with specialized experts across image and videoprobing, streaming geometric reconstruction, complex video and spatialreasoning, and robotic manipulation. Rather than pursuing benchmark-specific dominance, our work demonstrates the viability of training a sin-gle, versatile vision backbone that generalizes across semantic, spatial,and temporal reasoning, i.e., a meaningful step towards general-purposevisual understanding for interactive and embodied agents.

ECCV 2026

Towards Generalizable Robotic Manipulation in Dynamic Environments

Heng Fang, Shangru Li, Shuhan Wang, Xuanyang Xi, Dingkang Liang, Xiang Bai

Vision-Language-Action (VLA) models excel in static manipulation but struggle in dynamic environments with moving targets. This performance gap primarily stems from a scarcity of dynamic manipulation datasets and the reliance of mainstream VLAs on single-frame observations, restricting their spatiotemporal reasoning capabilities. To address this, we introduce DOMINO, a large-scale dataset and benchmark for generalizable dynamic manipulation, featuring 35 tasks with hierarchical complexities, over 110K expert trajectories, and a multidimensional evaluation suite. Through comprehensive experiments, we systematically evaluate existing VLAs on dynamic tasks, explore effective training strategies for dynamic awareness, and validate the generalizability of dynamic data. Furthermore, we propose PUMA, a dynamicsaware VLA architecture. By integrating scene-centric historical optical flow and specialized world queries to implicitly forecast object-centric future states, PUMA couples history-aware perception with short-horizon prediction. Results demonstrate that PUMA achieves state-of-the-art performance, yielding a 6.3% absolute improvement in success rate over baselines. Moreover, we show that training on dynamic data fosters robust spatiotemporal representations that transfer to static tasks.

ECCV 2026

SplitHDR: Saturation-Aware HDR Recovery and Denoising for Real-Time Detection

Jaewon Kim, Sol Namkung, Dongsuk Jeon

High Dynamic Range (HDR) imaging is indispensable forautonomous driving to ensure robust perception under changing light-ing conditions, such as tunnel exits or night driving. However, exist-ing learning-based HDR methods suffer from excessive computationalcosts and latency, making them impractical for real-time edge deploy-ment. In this paper, we propose SplitHDR, an extremely lightweightHDR framework tailored for machine vision. Leveraging advanced sen-sor technologies designed to minimize temporal displacement, our ap-proach avoids relying on heavy alignment modules and introduces aluminance-aware dynamic inference mechanism. Specifically, the modelconstructs a saturation mask based on input intensity, efficiently routingsaturated regions to a multi-frame fusion mode and unsaturated regionsto a lightweight denoising mode. This task decomposition ensures thatcomputational resources are allocated only where necessary. Experimen-tal results demonstrate that our method achieves state-of-the-art effi-ciency with only 0.002M parameters and 3.63 GMACs, reducing the pa-rameter count by over 99% compared to existing multi-exposure models.Despite the extreme compression, the HDR images reconstructed by ourapproach effectively preserve critical structural details, yielding highlycompetitive object detection performance crucial for safety-critical au-tonomous systems.

ECCV 2026

OmniNWM: Unifying the State-Action-Reward Triad for Closed-Loop Panoramic Driving Navigation World Models

Bohan Li, Zhuang Ma, Dalong Du, Baorui Peng, Zhujin Liang, Zhenqiang Liu, Xianda Guo, Chao Ma, Yueming Jin, Zheng Zhu, Hao Zhao, Wenjun Zeng, Xin Jin

Autonomous driving world models are expected to work effectively across three core dimensions: state, action, and reward. However, existing methods are typically restricted to fragmented modality modeling, short-horizon drift, and imprecise action control, while lacking intrinsic mechanisms for policy evaluation. In this paper, we introduce OmniNWM, an Omniscient panoramic Navigation World Model that addresses all three dimensions within a consistent probabilistic framework. For State, OmniNWM generates panoramic videos of RGB, semantics, metric depth, and 3D occupancy, ensuring pixel-level alignment across modalities with joint distribution modeling. To mitigate autoregressive exposure bias, we propose a structured panoramic forcing strategy to stabilize long-horizon generation via stochastic manifold thickening. For Action, we introduce canonical geometric action encoding with normalized panoramic Plücker ray-maps. This representation decouples motion dynamics from sensor intrinsics, enabling precise, zero-shot trajectory control across heterogeneous datasets and camera configurations. For Reward, we derive intrinsic occupancy-grounded dense rewards directly from generated 3D volumes, establishing a reliable closed-loop simulation cycle for evaluating diverse planning agents. Extensive experiments demonstrate that OmniNWM achieves SOTA performance in generation fidelity and control precision, with remarkable zero-shot robustness to novel scenes on nuPlan and in-house datasets with distinct camera rigs.

ECCV 2026

SOVTrack: Open-Vocabulary Multi-Object Tracking with Self-Supervised Pseudo Labeling and Feature Distillation

Zekun Qian, Ruize Han, Junhui Hou, Wei Feng

Open-Vocabulary Multi-Object Tracking (OVMOT) repre-sents a critical challenge requiring the detection and tracking of diverseobject categories beyond predefined training classes, including both seen(base) and unseen (novel) categories in real-world videos. Existing ap-proaches are fundamentally limited by their reliance on traditional closed-set tracking paradigms trained only on base classes and synthetic imagepairs, failing to exploit the rich temporal dynamics inherent in videosequences for universal tracking capabilities. We present SOVTrack, anovel self-supervised framework that harnesses SAM2’s universal represen-tations to address general-purpose tracking from raw video data withoutmanual supervision. Our approach introduces Dual-Direction PseudoLabeling (DDPL) to automatically generate high-quality pseudo-labelsthrough bidirectional temporal validation while dynamically assessingthe difficulty of each training example, and employs a dual-branch archi-tecture with Multi-Cue Adversarial Distillation (MCAD) to effectivelytransfer SAM2’s multi-cue pixel-level universal tracking capabilities toobject-level tracking scenarios while preserving its generalization poweracross diverse object categories. Experimental results demonstrate thatSOVTrack achieves state-of-the-art performance using only 10K videoframes (2% of existing methods’ data requirements), delivering substantialimprovements in both base and novel category tracking performance.

ECCV 2026

SMP-UWGS: Coupled Physics-Geometry Optimization for Scalable Multi-Partition Underwater 3D Reconstruction

Yuxuan Liu, Jinhui Zhang

We propose SMP-UWGS, an underwater reconstruction frame-work based on 3D Gaussian Splatting (3DGS) that couples scalablemulti-partition geometry with physical light transport modeling. Ourmethod introduces two key components: SMP-GAUSSIAN, a multi-partitionarchitecture enabling distributed optimization for large-scale scenes, andDPR-Net (Differentiable Physical Rendering Network), which combinesWaterParamPredict module for estimating water optical parameters witha Dual-Branch Differential Refinement (DBDR) module to model atten-uation and backscattering. With tailored loss functions, the frameworkjointly estimates water optical parameters and improves color fidelityand geometric accuracy. Experiments on public datasets show that SMP-UWGS achieves state-of-the-art efficiency while enabling scalable, high-fidelity underwater reconstruction, benefiting applications such as ecolog-ical monitoring, habitat mapping, and autonomous underwater robotics.

ECCV 2026

LatentPilot: Scene-Aware Vision-and-Language Navigation by Dreaming Ahead with Latent Visual Reasoning

Haihong Hao, Lei Chen, Mingfei Han, Changlin Li, Dong An, Yuqiang Yang, Zhihui Li, Xiaojun Chang

Existing vision-and-language navigation (VLN) models pri-marily reason over past and current visual observations, while largelyignoring the future visual dynamics induced by actions. As a result,they often lack an effective understanding of the causal relationship be-tween actions and how the visual world changes, limiting robust decision-making. Humans, in contrast, can “imagine” the near future by leveragingaction–dynamics causality, which improves both environmental under-standing and navigation choices. Inspired by this capability, we proposeLatentPilot, a new paradigm that exploits future observations duringtraining as a valuable data source to learn action-conditioned visual dy-namics, while requiring no access to future frames at inference. Con-cretely, we propose a flywheel-style training mechanism that iterativelycollects on-policy trajectories and retrains the model to better match theagent’s behavior distribution, with an expert takeover triggered whenthe agent deviates excessively. LatentPilot further learns visual latenttokens without explicit supervision; these latent tokens attend globallyin a continuous latent space and are carried across steps, serving asboth the current output and the next input, which enabling the agentto “dream ahead” and reason about how actions will affect subsequentobservations. Experiments on R2R-CE, RxR-CE, and R2R-PE bench-marks achieve new SOTA results, and real-robot tests across diverseenvironments demonstrate LatentPilot’s superior understanding of envi-ronment–action dynamics in scene.

ECCV 2026

SkyLume: A Large-Scale Multi-Illumination Aerial Benchmark for Urban Scene Reconstruction and Beyond

Zhuoxiao Li, Wenzong Ma, Taoyu Wu, Jinjing Zhu, Shuai Zhang, Jing OU, Tongyan Hua, Yinrui Ren, Rongjun Qin, Hui Xiong, Wufan Zhao

Recent advances in Neural Radiance Fields and 3D Gaus-sian Splatting have demonstrated immense potential for large-scale UAV-based 3D reconstruction. However, real-world data collection often spansmultiple times of the day, violating the fundamental photometric consis-tency assumption. Such multi-temporal illumination inconsistencies in-evitably lead to severe color artifacts, coupled shadows, and degradedgeometric accuracy. Due to the lack of UAV datasets that systematicallycapture the same areas under varying lighting conditions, this crucialchallenge remains largely underexplored. To bridge this gap, we introduceSkyLume, a large-scale, real-world UAV dataset specifically designed forstudying illumination-robust 3D reconstruction and urban scene model-ing. Our dataset features three primary contributions: (1) We collect over100k high-resolution UAV images (nadir and four oblique views) span-ning 10 diverse urban regions, with each region meticulously capturedacross three distinct time periods to systematically isolate illuminationvariations. (2) To enable rigorous evaluation, we provide comprehensiveper-scene LiDAR scans, offering highly accurate ground truth for as-sessing depth, surface normals, and geometric fidelity. (3) For inverserendering and appearance decoupling tasks, we introduce the Tempo-ral Consistency Coefficient (TCC), a novel metric designed to quantifycross-time rendering stability. We envision SkyLume as a foundationalbenchmark that will advance research and real-world evaluation in large-scale inverse rendering, robust geometry reconstruction, and novel viewsynthesis. Project Page: skylume.

ECCV 2026

SGC-Lane: Monocular 3D Lane Detection with Standard-Definition Map Guidance and Lane Completion

Fuqiang Jiang, Wei Li, Tianyao Zhao, Yu Hu

Monocular 3D lane detection offers a cost-effective solutionfor autonomous driving perception but is inherently challenged by theill-posed problem of inferring 3D geometry from a 2D image. Standard-Definition (SD) maps offer a lightweight prior for road topology, promis-ing enhanced accuracy for distant or occluded lanes. However, their in-herent inaccuracies and representation as road-level centerlines, whichmisalign with the actual lanes to be perceived, pose significant challengesfor direct integration. To tackle this, we propose SGC-Lane, a novelframework that advances monocular 3D lane detection through SD mapguidance and lane completion. Specifically, we propose an FV-GuidedMap Encoder that generates a lane probability map and structured mapfeatures, where the feature channels are aligned with potential lane in-stances. This probability map directs attention toward lane-specific re-gions in the front view (FV), refining image features and initializing lanequeries by the Local Spatial Cross-Attention module for precise geome-try decoding. In addition, to mitigate short-range truncation, we designa Step Consistency Completion (SCC) Head that is used to predict andcluster lane endpoints, effectively recovering missing segments to producecomplete 3D lane representations. On the large-scale OpenLane dataset,our method achieves a 65.6% F1-score, showing improved performanceagainst existing approaches with the assistance of only coarse road-levelSD map priors. The improvements are especially evident in challengingconditions, such as roads with sharp curves and complex intersections.Code is released at https://github.com/FuqingJIang/SGC-Lane.

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