ECCV 2026 论文列表

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.

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.

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.

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.

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.

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.

Clinical cardiac MRI spans diverse contrasts, sampling tra-jectories, accelerations, scanners, and patient populations, yet many de-terministic deep-learning reconstruction methods remain protocol spe-cific. We present the Scalable Deep Unrolled Model (SDUM), which in-tegrates a Restormer-based unrolled reconstructor, per-cascade coil sen-sitivity estimation, sampling aware weighted data consistency, and uni-versal conditioning on cascade index and acquisition metadata. A sin-gle SDUM model achieves state-of-the-art performance across all CM-RxRecon2025 tracks without task-specific fine-tuning and outperformsPromptMR+ on CMRxRecon2024 by +0.55 dB. Scaling experimentsshow near-logarithmic gains with depth up to 18 cascades (r=0.986,R2 =0.973) and continued but diminishing gains from data scaling (32.72 dBat 40% to 33.18 dB at 100%). SDUM also generalizes in a zero-shot set-ting to unseen in-house chemical exchange saturation transfer (CEST)MRI (43.57 dB PSNR, 0.9769 SSIM). When trained separately on fastMRIbrain, SDUM surpasses PC-RNN by +1.8 dB. These results supportSDUM as a scalable framework for robust MRI reconstruction acrossheterogeneous acquisition settings beyond cardiac MRI.Code: https://github.com/NVIDIA-Medtech/NV-Raw2insights-MRIModel: https://huggingface.co/nvidia/NV-Raw2insights-MRI

While Text-to-Image (T2I) models have shown remarkablesuccess in generating photorealistic visual content, they still strugglewith the rigorous semantic alignment and logical reasoning requiredfor scientific imagery. Inspired by Peirce’s Semiotic Triad, we introduceScientific Image Reasoning (SciIR), a comprehensive resource for trainingand evaluation of scientific image generation. We formalize scientificreasoning into three core dimensions: Entity Structure (Icon), ScientificProcess (Index ), and Scientific Law (Symbol ). Specifically, to overcomethe scarcity of training data in scientific image generation, we elaboratelycreate SciIR-82k, a large-scale dataset containing over 80,000 high-qualityscientific image-text pairs from cutting-edge publications. The datasetis hierarchically organized according to the semiotic dimensions andincorporates a Scientific Reasoning Chain-of-Thought (Sci-RCoT) toexplicitly model underlying visual logic. For evaluation, we propose SciIR-Bench, which aligns with these three semiotic levels and employs anAtomic Checklist to convert the outcome-oriented scientific accuracyinto process-oriented, verifiable, fine-grained questions. Our extensiveexperiments reveal significant deficiencies in current models’ scientificreasoning capabilities. Furthermore, by fine-tuning on the SciIR-82kdataset, we developed the Qwen-Image-SciIR model, which achieves asubstantial improvement on the SciIR-Bench, increasing the final scorefrom 35% to 43%, laying a solid foundation for future advances in scientificimage generation.

Backdoor attacks, which induce Convolutional Neural Net-works (CNNs) to behave maliciously when a predefined trigger is present,pose serious security risks. While such threats also extend to VisionTransformers (ViTs), previous studies have suggested that existing back-door attacks remain highly effective on ViTs and can evade commondefense mechanisms—often without significantly compromising accuracy.In this paper, we revisit this claim and demonstrate that such con-clusions are overly optimistic, largely due to inadequate adaptation ofCNN-based defenses to ViTs. We show that, with proper adjustments,existing backdoor attacks can in fact be effectively mitigated. More-over, we propose a more robust attack strategy: by introducing slightperturbations to the trigger, existing attacks can be made significantlymore resistant to various defenses. We hope that our findings—bothon the defensibility of current attacks with correct adaptations and theproposed enhanced attack—will inspire deeper investigation into thebackdoor robustness of Vision Transformers. Our code is available athttps://github.com/PKU-ML/ViT_backdoor.

Point clouds are one of the most fundamental and widelyused 3D representations, serving as the most basic geometric represen-tation of 3D shapes. Nevertheless, most existing 3D printing pipelinesrequire a watertight mesh as input, preventing the direct use of pointclouds for fabrication. A common workaround is to reconstruct meshesfrom point clouds; however, the resulting meshes often contain geomet-ric artifacts, such as incorrect faces or topological inconsistencies, thatare difficult to repair and may lead to printing failures. To overcomethese limitations, we propose PrintAnything, a novel framework thatlearns to produce executable 3D printing G-code directly from 3D pointclouds without requiring mesh reconstruction. To enable point cloudsto serve as direct input for slice-wise toolpath generation, we introducea slice-wise point projection strategy that transforms unstructured 3Dpoint clouds into slice-aligned 2D representations consistent with layer-by-layer nature of fused deposition modeling in 3D printing. To eliminatemesh dependency and provide a unified representation that bridges pointclouds and G-code, we propose Geometric plan (G-plan) map, a com-pact 2D representation composed of occupancy, region, and flow mapsthat encode the geometric and extrusion properties required for toolpathsynthesis in 3D printing. As a result, our proposed method accuratelygenerates printable G-code directly from point clouds, enabling a prac-tical and fully mesh-free pipeline for 3D printing. The code is publiclyavailable at https://github.com/Sangminhong/PrintAnything.

Real-world image dehazing remains a challenging problem due to the significant domain gap between synthetic training data and natural haze, often leading to poor generalization. While unsupervised Generative Adversarial Networks (GANs) attempt to bridge this gap, they frequently suffer from training instability and hallucinatory artifacts. Recently, Reinforcement Learning (RL) has emerged as a promising alternative; however, current RL-based restoration paradigms predominantly rely on diffusion models, leading to prohibitive computational costs and ineffective exploration. To address these bottlenecks, we propose Dehaze-RL, an efficient framework tailored for unpaired real-world dehazing. Bypassing the expensive iterative sampling of diffusion models, we design an efficient policy network to predict hybrid actions in a Physics Hybrid Action Space. By explicitly estimating physical parameters, enhancement factors, and a gating policy map, we anchor the agent’s exploration in reliable physical priors, effectively resolving the exploration dilemma. These components are dynamically integrated via a gating fusion mechanism. Furthermore, we introduce a Multi-Granularity Reward Mechanism to provide comprehensive feedback, seamlessly aligning the outputs with human perception without structural distortions. Extensive experiments demonstrate that Dehaze-RL outperforms stateof-the-art methods in both visual fidelity and quantitative metrics.

Conventional visual navigation policies often struggle withmyopic decision-making and mode collapse in complex environments.While world models offer a promising alternative, existing paradigms typ-ically isolate perception, generation, and control, failing to capture theirshared spatio-temporal dynamics. In this paper, we propose NavWM, aunified navigation world model that seamlessly integrates latent worldreasoning, multimodal action prediction, and controllable visual gener-ation. At its core, NavWM leverages latent world tokens to distill geo-metric and semantic priors, endowing the agent with robust structuralunderstanding. To overcome the limitations of deterministic policies, weintroduce an anchor-based multimodal trajectory forecasting frameworkthat generates a diverse action space. This inherent diversity explicitlyempowers the generative world model to act as a robust closed-loop plan-ner, utilizing visual foresight to evaluate and select the optimal path.Extensive experiments across diverse robotics datasets demonstrate thatNavWM significantly advances the state-of-the-art, delivering remark-able improvements in both high-fidelity future state generation and zero-shot navigation success.

Although Multimodal Large Language Models (MLLMs) havedemonstrated impressive performance in Medical Visual Question An-swering (Med-VQA), their reliance on global image features often lacksprecise pixel-level grounding, thereby limiting clinical trustworthiness.To bridge the semantic gap between high-level clinical reasoning andspatial localization, we propose MedREAL (Medical REasoning-drivenAnswering and Localization), a unified framework that seamlessly alignslinguistic reasoning with spatial grounding. Specifically, MedREAL in-troduces Seg Anchored Reasoning Pooling (SARP) to distill task-relevantsemantic evidence directly from [SEG] tokens within the MLLM’s hid-den states. Furthermore, a Reasoning-to-Visual (R2V) fusion mecha-nism is proposed to effectively inject these reasoning-aware features intoa segmentation pipeline for accurate mask decoding. To facilitate thisparadigm, we construct MedRAVS-13K, a comprehensive dataset com-prising 13,824 expertly validated samples across four diverse imagingmodalities. Extensive experiments demonstrate that MedREAL signifi-cantly outperforms state-of-the-arts, achieving 68.49% gIoU and 70.47%cIoU on benchmark evaluations. By generating evidence masks that arestrictly consistent with textual diagnoses, MedREAL provides a robust,interpretable framework for reasoning-driven medical image analysis.

Computed tomography report generation (CTRG) automatesradiology reporting to reduce clinical workload and facilitate patient care.Recent efforts in applying the rapidly developing large language mod-els (LLMs) have advanced the field; yet, they still face a fundamentalchallenge due to the large volume of 3D data: effectively reducing highfeature redundancy and computational burden while simultaneously ex-tracting information-rich representations. To address this challenge, thiswork presents a novel CTRG framework that fully imitates the coarse-to-fine visual search pipeline practiced by radiologists. Our frameworkfirst employs a ViT-based image-text alignment architecture to extracta global token and local patch tokens for each anatomical structure, en-hanced by a mask-guided sparse negative-entropy loss. An abnormality-proposal LLM then processes the global token and clinical metadata topropose a shortlist of candidate abnormalities. This shortlist promptsan abnormality-prompted local token filter (AP-LTF) to select the mostinformative patch tokens, effectively reducing redundancy while preserv-ing critical information. Finally, a report-generation LLM takes in theglobal token, selected local tokens, and clinical metadata to composea full report. During training, ground-truth abnormalities are prefixedto the reference report to enhance awareness of clinic-relevant findings,and group relative policy optimization (GRPO) aligns the abnormality-proposal and report-generation LLMs for collaborative efficacy. Exper-imental results on two public CTRG datasets demonstrate the supe-rior performance of our framework compared to existing state-of-the-artFig. 1: Comparison of token compression strategies for CTRG. (a) 3D spatial pooling[2, 8] achieves high compression rate but incurs significant information loss. (b) Mask-guided volume cropping [12, 22] yields region-specific tokens but omits intra-regionredundancy. (c) [26] selects the top K tokens with the largest attention weights toregion-specific queries, yet may be subject to dominant “sink tokens” [14, 29]. (d) Ourmethod first proposes a shortlist of candidate abnormalities using only a single globaltoken per structure and clinical metadata. Then, an abnormality-prompted local tokenfilter (AP-LTF) combines the shortlist, a learned score, and the attention weight for acomprehensive token selection, thereby preventing sink tokens from dominating.methods in terms of clinical efficacy, RaTEScore, and GREEN scores.Ablation studies further validate the effectiveness of its novel design.

Recent advances integrate physically grounded Newtonian dynamics with neural rendering frameworks, narrowing the gap between photorealistic scene reconstruction and physics-based animation. However, existing approaches focus on mechanically driven dynamics while neglecting temperature, a fundamental yet invisible physical factor underlying phenomena such as melting, solidification, and other thermomechanical processes. In this paper, we propose MeGAS, a novel framework that incorporates thermomechanical phase-change dynamics into 3D Gaussian Splatting (3DGS). Specifically, we propose a new thermomechanical dynamic Gaussian Splatting representation that augments 3DGS with temperature attributes and employs a heat advection-diffusion solver with MPM dynamics incorporating phase transitions, enabling physically plausible and visually realistic synthesis of thermophysical phenomena. Furthermore, a new topology-adaptive Gaussian rendering strategy is proposed to mitigate cracking and floaters under extreme deformation. Extensive experiments demonstrate that MeGAS produces physically consistent thermomechanical behavior while maintaining highfidelity photorealistic rendering, advancing toward physics-integrated world models.

Despite notable progress in text-guided medical image seg-mentation nowadays, these methods are limited to single-round dia-logues and fail to support multi-round reasoning, which is importantfor medical education scenarios. In this work, we introduce Multi-Round Entity-Level Medical Reasoning Segmentation (MEMR-Seg), anew task that requires generating segmentation masks through multi-round queries with entity-level reasoning, helping learners progressivelydevelop their understanding of medical knowledge. To support this task,we construct MR-MedSeg, a large-scale dataset of 177K multi-roundmedical segmentation dialogues, featuring entity-based reasoning acrossrounds. Furthermore, we propose MediRound, an effective baselinemodel designed for multi-round medical reasoning segmentation. To mit-igate the inherent error propagation within the chain-like pipeline ofmulti-round segmentation, we introduce a lightweight yet effective Judg-ment & Correction Mechanism during model inference. Experi-mental results demonstrate that our method effectively addresses theMEMR-Seg task and outperforms conventional medical referring seg-mentation methods. The project is available at https: // github. com/Edisonhimself/ MediRound .

Knowledge-based Visual Question Answering (KB-VQA) re-quires models to combine image understanding with external knowledge.Most prior methods use a fixed retrieve-then-generate pipeline with a pre-selected retriever and a static top-k setting, which is not adaptive duringreasoning. We propose ProMSA, a progressive multimodal search agentfor KB-VQA. Given an image-question pair, the agent iteratively choosesimage search, text search, or stop, under explicit tool-call budgets andwith deduplication to avoid redundant retrieval. For training, we first userejection-sampling SFT to learn valid tool-use formats, then optimize theagent with TN-GSPO, a sequence-level RL objective that normalizesupdates by both generation length and tool-interaction depth. Experi-ments on E-VQA and InfoSeek show consistent gains over strong RAGand agent baselines, and improved retrieval and end-to-end accuracy.Thecode is available at https://github.com/DingWu1021/Promsa.

Video-LLMs often attend to irrelevant frames, which is espe-cially detrimental for sports coaching tasks requiring precise temporal lo-calization. Yet obtaining frame-level supervision is challenging: expensiveto collect from humans and unreliable from other models. We improvetemporal grounding without additional annotations during training byexploiting the observation that related tasks, such as generation andverification, must attend to the same frames. We enforce this via a self-consistency objective over select visual attention maps of tightly-relatedtasks. Using VidDiffBench, which provides ground-truth keyframe an-notations, we first validate that attention misallocation is a meaningfulbottleneck. We then show that training with our objective yields gains of+3.0%, +14.1% accuracy and +0.9 BERTScore over supervised finetun-ing across three sports coaching tasks: ExAct, FitnessQA, and ExpertAF.

In the real world, fashion is about layering: adding a jacketover a shirt, or a sequence of adding and removing layers, rather thanjust a single-layer swap. This fundamental real-world task remains achallenge in existing Virtual Try-On (VTON) methods, which excel atsingle-layer replacement but are not designed to layer or de-layer anexisting outfit. This paper proposes Layering Virtual Try-On (LVTON),a layering benchmark and method that preserves an existing outfit whileenabling sequential layering. We find that current VTON paradigms arefundamentally ill-equipped for LVTON, as their reliance on cloth-agnosticrepresentations and single-item datasets discards essential layering context.Our key insight is that the LVTON challenge must be disentangled intotwo distinct competencies: (1) General VTON Priors (e.g., deformation,identity preservation) and (2) Specific Layering Knowledge (e.g., layeringorder and occlusion reasoning). First, our model obtains general VTONpriors by being trained on data produced by an automatic data generationpipeline that synthesizes samples from fashion videos via segmentationand inpainting. Second, the model is fine-tuned on a small, dedicatedLVTON dataset to learn the layering logic. Our method achieves state-of-the-art results on our LVTON benchmark and demonstrates superiorgeneralizability on traditional VTON benchmarks, setting new state-of-the-art results when fine-tuned and exhibiting zero-shot capabilities.