ECCV 2026 Papers

Deep generative models, while revolutionizing image gener-ation, largely operate as opaque “black boxes”, hindering human un-derstanding, control, and alignment. While methods like sparse autoen-coders (SAEs) show remarkable empirical success, they often lack theo-retical guarantees, risking subjective insights. Our primary objective is toestablish a principled foundation for interpretable generative models. Wedemonstrate that the principle of causal minimality – favoring the sim-plest causal explanation – can endow the latent representations of gener-ative models with clear causal interpretation and robust, component-wiseidentifiable control. We introduce a novel theoretical framework for hi-erarchical selection models, where higher-level concepts emerge from theconstrained composition of lower-level variables, better capturing thecomplex dependencies in data generation. Under theoretically derivedminimality conditions that manifest as sparsity constraints, we show thatlearned representations can be equivalent to the true latent variables ofthe data-generating process. Empirically, applying these constraints totext-to-image diffusion models allows us to extract their innate hierarchi-cal concept graphs, offering fresh insights into their internal knowledgeorganization. Furthermore, these causally grounded concepts serve aslevers for fine-grained model steering, paving the way for transparent,reliable systems.

The reliance on extensively annotated 3D data confines cur-rent autonomous driving systems to a closed-set detection paradigm, lim-iting their robustness in real-world open-world scenarios where novel ob-jects frequently emerge. To bridge this gap, we propose BEVOpen3D,a novel distillation framework that facilitates open-world 3D object de-tection by effectively transferring knowledge from 2D vision-languagemodels into the 3D domain via the Bird’s-Eye-View (BEV) space undera partial-label setting. Our approach first generates initial 3D proposalsfor both seen and unseen categories using an open-vocabulary 2D detec-tor combined with a greedy spatial search strategy. We then introducea Triple-Source Label Refinement mechanism, which fuses original, lo-cally rectified (via a Seen-Guided Local Query), and globally re-examinedproposals to produce high-quality pseudo labels for unseen-category. Fi-nally, we propose a Heatmap Proposal Distillation strategy, where animage-based BEV teacher transfers its vision-grounded semantic priorsto a LiDAR-based student through aligned BEV heatmaps, enabling thestudent to detect both seen and unseen objects without requiring addi-tional 3D annotations. Extensive experiments validate the effectivenessof BEVOpen3D in open-world 3D detection.

Diffusion models have significantly advanced video super-resolution (VSR) but remain largely constrained to fixed upsamplingscales. Conversely, while coordinate-based arbitrary-scale VSR methodsoffer scale flexibility, they inherently suffer from severe over-smoothingat large scaling factors. Integrating generative priors with continuous de-coding is promising but currently hindered by severe temporal flickeringcaused by the stochasticity of diffusion sampling. To address this, wepropose AVSR-Diff (Arbitrary-scale Video Super-Resolution with Dif-fusion), a novel decoupled framework that separates scale-agnostic la-tent denoising from continuous coordinate rendering, effectively avoid-ing computationally heavy resolution-specific sampling. Our approachintroduces a Temporally-Gated Feature Recurrence (TGFR) module toextract strictly aligned, temporally consistent latent priors. Furthermore,we design a continuous video VAE decoder incorporating a Scale-AwareFourier Refinement (SAFR) module to dynamically adapt frequencycomponents to any target scale. Extensive experiments demonstrate thatAVSR-Diff consistently preserves high-frequency details and strong tem-poral stability across various scales, surpassing state-of-the-art arbitrary-scale baselines. Remarkably, our framework outperforms recent fixed-scale generative models even on their native resolution.

WordArt (artistic text) features highly customized fonts,textures, and layouts, making WordArt-oriented scene TExt Recogni-tion (WATER) substantially more challenging than general Scene TextRecognition (STR). Existing STR datasets and methods, typically builtaround regular scene text and fixed-template inputs, struggle to scale toWATER. Thus, we aim to advance this task from both data and modelperspectives. On the data side, we construct a 2M synthetic dataset,WATER-S, with the scale improved by hundreds of times comparedto existing artistic text data. WATER-S consists of two complementarysubsets. One rendered by an upgraded rendering pipeline (SynthWor-dArt), which provides highly accurate and controllable synthetic Wor-dArt data. The other is generated by combining Qwen3-VL for promptmining and Z-Image for image synthesis, which improves the coverage ofrealistic and diverse data. On the model side, we propose WATERec.It adopts a visual encoder supporting arbitrary-shaped inputs and anautoregressive decoder to model complex layouts, structurally breakingthe bottleneck of fixed-template STR on WordArt. Experiments showthat this architecture outperforms prior STR methods, achieving state-of-the-art performance on irregular texts such as WordArt. Togetherwith WATER-R, carefully reorganized from existing real STR data, ourstrong baseline with the new synthetic data and model design reaches90.40% accuracy on WordArt-Bench, surpassing both general-purposeand OCR-specialized vision-language models by a large margin. Codeand data are available at https://github.com/YesianRohn/WATER.

Out-of-Distribution (OOD) detection is a critical safety requirement for deploying deep neural networks in open-world environments. While recent advances increasingly rely on more computationally intensive training methods involving synthetic outliers, contrastive objectives, or specialized loss functions, their gains often come with substantial computational overhead and implementation complexity. In this work, we revisit the fundamentals of OOD detection and uncover a key flaw in common distance-based detectors: sensitivity to feature magnitude. We show that low-norm OOD samples can appear closer to in-distribution (ID) class centroids than actual ID samples, evading detection. To this end, we introduce Angular Separation Learning (ASL), a simple and highly effective strategy that applies ℓ -normalization to features before 2 the final classification layer. This modification compels the network to optimize for angular separation, achieving robust feature learning without additional regularization mechanisms, synthetic samples, or costly negative mining. Through extensive experiments on diverse benchmarks, we demonstrate that ASL not only matches but often surpasses state-of-theart methods, especially in challenging near-OOD scenarios, while maintaining training efficiency. Our results indicate that a minimalist rethink of standard training can achieve superior OOD performance, prompting a re-evaluation of the complexity-to-performance trade-off in OOD detection. Codes are available at https://github.com/HAIV-Lab/ASL.

While Multimodal Large Language Models (MLLMs) have advanced video understanding, achieving precise temporal and crossmodal alignment in audiovisual video captioning remains a formidable challenge. Most existing approaches suffer from modality detachment and temporal incoherence, failing to accurately bind auditory events to visual entities or capture complex causal dynamics. To address these deficiencies, we propose TCA-Captioner, a framework specifically engineered to enhance Temporal and Cross-Modal Alignment for audiovisual video captioning. We first introduce the Observer-Checker-Corrector (OCC) framework, an iterative refinement strategy that generates high-fidelity, meticulously grounded training data. Leveraging a curated high-density human interaction dataset, TCA-Captioner is optimized to model sophisticated audiovisual interactions. Furthermore, we present TCA-Bench, a diagnostic benchmark utilizing a Decoupled Evaluation Protocol to isolate and quantify model proficiency in audiovisual binding and temporal relational reasoning. Extensive experiments demonstrate that TCACaptioner sets a new standard for temporally-coherent and synchronized audiovisual narratives.

Recent Gaussian Splatting SLAM (GS-SLAM) methods alignrendered and observed images through photometric and geometric resid-uals to achieve high-quality reconstruction and rendering. However, thesesignals weaken in low-texture and repetitive environments, which leadsto floaters, inconsistent geometry, and drift. Existing systems mitigatedrift through loop closure or global layout assumptions, but these mech-anisms are opportunistic or lack persistent structural entities that pro-vide reliable constraints throughout mapping. We introduce a structure-aware GS-SLAM framework that models planar structures as persistentPlanar Gaussian Instances (PGIs) within a 3D Gaussian map. EachPGI represents a planar surface associated with Gaussian primitives andmaintains a consistent identity across frames. This representation pro-motes planes from transient observations to map entities and convertsplanar structures into stable geometric constraints. Building on PGIs,we propose structure closure, which estimates relative camera pose frommultiple non-parallel planes without requiring trajectory revisits. Wefurther integrate PGIs into a joint optimization that combines photo-metric–depth supervision with plane-instance consistency to improvemulti-view geometric coherence and suppress floater growth. Experi-ments on public benchmarks show that the proposed system achievesstate-of-the-art performance in camera tracking, dense reconstruction,and novel-view rendering, with strong robustness in scenes with largetextureless surfaces and repetitive layouts. Our code is publicly availableat https://github.com/yanyan-li/StructureGS-SLAM.

Deep neural networks are highly vulnerable to adversarialperturbations, raising serious safety concerns in the real-world systems.While prior work mainly explores single-task attacks or jointly degradingall tasks in multi-task models, practical scenarios often demand moreselective and stealthy attack strategies. To address this challenge, wepropose Stealthy Multi-Task Adversarial Attack (SMTA2 ), a novelframework that selectively degrades a targeted task while strictly pre-serving the performance of non-targeted tasks. We formulate this objec-tive as a constrained multi-objective optimization problem and designtask-aware adversarial perturbations that maximize degradation on thetargeted task without causing collateral damage on non-targeted tasks.To enhance practicality, we further introduce an automated loss-weighttuning strategy that dynamically balances attack and preservation objec-tives. Experiments on two multi-task benchmarks NYUv2 and Cityscapesdemonstrate that SMTA2 achieves strong attack performance on tar-geted tasks while maintaining non-targeted tasks intact on both unde-fended and adversarially trained models, establishing the first systematicframework for stealthy and selective multi-task attack framework.

Deep models are successfully applied to various fields, whiledemanding large amount of annotated data for high-performance recog-nition. To remedy the data-hunger issue, self-supervised learning, espe-cially masked autoencoder (MAE), is a promising approach to effectivelypre-train the deep models. The MAE leverages random masking to con-struct pretext tasks where masked image patches are reconstructed byusing unmasked (visible) ones. In vision domain, however, inherent imageproperties of high redundancy and correlation in local neighbors couldinterfere with the mask-based pretext tasks. In this study, we analyze twomain processes of masking and reconstruction in MAE through the lensof difficulty of the pretext task. The analysis inspires us to propose simpleyet effective approaches based on filtering to improve random maskingas well as raw-pixel reconstruction by properly controlling difficulty ofMAE task with a negligible extra computation cost. In the experimentson image classification, the proposed method renders favorable perfor-mance improvement to MAE using ViTs.

Recent state-of-the-art visual trackers produce high qualityand long-term-stable segmentation masks. We propose to leverage thesestrengths for planar object tracking, in which the goal is to estimate aprecise 8-degrees-of-freedom homography pose, a geometric representationnot estimated by segmentation trackers. We present SAM-H – a planarobject tracker that estimates homographies from segmentation mask con-tours via a training-free pipeline. When SAM-H is applied to masks fromSAM 2 [21], it sets a new state-of-the-art performance on the challengingPlanarTrack [15] benchmark by a large margin, +18.4pp on the p@5metric. We further show that segmentation-based and correspondence-based homography estimation are complementary, and propose WOFT-SAM, which out-performs all prior methods on both PlanarTrack [15]and POT-210 [14]. We also provide precise re-annotations of Planar-Track initial poses, enabling more accurate benchmarking in the high-precision p@5 metric. The code and the re-annotations are available athttps://github.com/serycjon/WOFTSAM.

The nonparametric identification of causal mediation effectsfrom heterogeneous multimodal observational data remains an opentheoretical problem. Existing single-modal methods cannot handlecross-modal causal relationships, while multimodal learning methodslack causal inference capabilities, preventing reliable mediation pathwayidentification and effect quantification under structural uncertainty.We propose the Multimodal Structure-Informed Guided MediationAnalysis (MM-SIGMA) framework, which achieves automated cross-modal mediation pathway identification and end-to-end uncertaintypropagation through probabilistic causal structure discovery. Weestablish nonparametric identification conditions for the Cross-ModalNatural Direct Effect and Cross-Modal Natural Indirect Effect fromheterogeneous multimodal observational data, and build MM-SIGMAupon this theoretical foundation. Specifically, it employs multimodalvariational autoencoders for causality-preserving latent representation,differentiable Flow-Structural Equation Models for asymptoticallyconsistent latent structure learning, Cross-Modal Path Stability Scoringfor high-confidence pathway identification, and Efficient InfluenceFunctions with Bayesian Model Averaging for end-to-end uncertaintypropagation. Experiments on synthetic data demonstrate state-of-the-art performance under structural uncertainty, nonlinearity, andcross-modal heterogeneity. On the HPP dataset, MM-SIGMA identifiescross-modal mediation pathways connecting sleep, fundus imaging, andcardiovascular health, revealing undiscovered cross-system mechanisms.

Reconstructing 3D Human-Object Interaction (HOI) from asingle image is challenging due to the diversity of the reconstructed tar-gets. Ordinarily, the human body follows a fixed parametric template,whereas objects exhibit diverse and irregular shapes. Such differenceslead to inconsistent geometric priors and scale variations, making jointmodeling within a unified framework highly difficult. Existing vertex-level methods struggle to effectively handle this structural discrepancy.We propose to bridge this gap by introducing a unified triplane featuremap that models both human and object meshes within a shared 3Dlatent space. Our framework comprises three components: (1) a HOI Tri-plane Variational AutoEncoder (HOIT-VAE) that learns a compact andstable triplane latent space from large-scale human-object mesh data; (2)a HOI Triplane Transformer (HOIT-T) that predicts triplane featuresfrom a single image, regularized by the learned priors of HOIT-VAE;(3) a pose-fitting method for estimating the human pose from the re-constructed human mesh. Experiments on the BEHAVE and InterCapdatasets show that our approach achieves competitive reconstruction per-formance, validating the effectiveness of the unified triplane representa-tion in capturing the mesh structures and interactions between humansand objects.

Automatically detecting abnormal events in videos is cru-cial for modern autonomous systems, yet existing Video Anomaly De-tection (VAD) benchmarks lack the scene diversity, balanced anomalycoverage, and temporal complexity needed to reliably assess real-worldperformance. Meanwhile, the community is increasingly moving towardVideo Anomaly Understanding (VAU), which requires deeper seman-tic and causal reasoning but remains difficult to benchmark due to theheavy manual annotation effort it demands. In this paper, we introducePistachio, a new VAD/VAU benchmark constructed entirely through acontrolled, generation-based pipeline. By leveraging recent advances invideo generation models, Pistachio provides precise control over scenes,anomaly types, and temporal narratives, effectively eliminating the bi-ases and limitations of Internet-collected datasets. Our pipeline inte-grates scene-conditioned anomaly assignment, multi-step storyline gen-eration, and a temporally consistent long-form synthesis strategy thatproduces coherent 41-second videos with minimal human intervention.Extensive experiments demonstrate the scale, diversity, and complexityof Pistachio, revealing new challenges for existing methods and motivat-ing future research on dynamic and multi-event anomaly understanding.

Inferring physical properties from a single image is funda-mentally under-constrained. Attributes such as density, elasticity, andyield stress govern how objects move, yet they are largely invisible ina static frame. Existing physics-aware methods attempt to resolve thisambiguity through task-specific fine-tuning or supervised property es-timation, but both strategies struggle to generalize across diverse ma-terials and scenes. We observe that different motions expose comple-mentary physical cues. Building on this observation, we propose Phy-MAGIC, a training-free framework that actively probes physical prop-erties by synthesizing targeted motions from a single image. Specifically,PhyMAGIC uses a pretrained image-to-video model to construct mo-tion probes that generate diverse dynamic sequences from the inputimage. A vision-language model then analyzes these sequences to esti-mate physical parameters, each accompanied by a confidence score. Pa-rameters with low confidence trigger targeted prompt refinement, whichgenerates additional probe motions to gather complementary evidence.Once all parameters reach sufficient confidence, PhyMAGIC compilesthem into a complete physical specification and executes it in a differ-entiable Material Point Method simulator initialized from 3D Gaussianreconstructions. Experiments on diverse real-world scenes demonstratethat PhyMAGIC achieves stronger text-motion alignment and higherhuman-rated physical plausibility than state-of-the-art open-source videogenerators and physics-aware baselines. Code is available at: https://mengsiwei.github.io/MAGIC/.

Panoramic images capture holistic environments, yet retriev-ing them using fine-grained, localized textual descriptions remains a fun-damental challenge. Through empirical analysis, we reveal that modernVision-Language Models (VLMs) suffer from severe semantic dilutionwhen processing panoramic inputs. By compressing an information-densepanorama into a single global embedding, VLMs inevitably submerge lo-cal details within vast backgrounds, restricting retrieval to coarse scene-level matching. To overcome this bottleneck, we propose PanoRec, amulti-granularity panoramic retrieval framework built on a spatially-structured sequence modeling paradigm. Specifically, PanoRec serializesdistortion-free cubemap faces and a downsampled global panorama along-side spatial anchor tokens into a unified sequence. This enables the ef-ficient extraction of decoupled local and global representations within asingle forward pass. To effectively supervise this multi-granularity featurespace, we formulate a joint spatial InfoNCE objective. For local match-ing, we adopt a MaxSim routing strategy that dynamically aligns eachquery with its most relevant cubemap face. Crucially, this strategy notonly suppresses background noise during inference but also inherentlyintroduces a powerful hard spatial negative mining mechanism duringtraining. Extensive experiments demonstrate that PanoRec achieves im-pressive performance across multiple scenarios, effectively unifying holis-tic scene-level retrieval with fine-grained spatial discrimination.

Continuous 6-DoF pose estimation is essential for au-tonomous UAV operations. Yet, existing visual odometry and SLAMmethods accumulate drift and yield only relative, up-to-scale trajecto-ries. Single-frame geo-localization, in turn, discards temporal continu-ity and remains too slow for real-time use. We present OrthoTrack, atraining-free system that estimates continuous 6-DoF UAV trajectoriesusing only publicly available orthophotos and surface models as a mapprior. OrthoTrack matches keyframes against the orthophoto and liftscorrespondences to metric 3D via the surface model. It then propagatesthese map-anchored correspondences to intermediate frames with opticalflow, producing absolute, metrically scaled poses at every frame with-out GPS or post-hoc alignment. We also introduce the MovingDroneDataset, a large-scale benchmark pairing photorealistic UAV sequenceswith dense 6-DoF ground truth and co-registered multi-modal geodataincluding multi-temporal orthophotos. On MovingDrone and real-worldbenchmarks, OrthoTrack runs in real time on a single GPU. It outper-forms all baselines by a large margin, even those receiving oracle scaleand alignment. By relying on publicly available geodata, OrthoTrackenables deployment to new regions without site-specific adaptation.

Fine-grained Cross-View Localization (CVL) estimates theprecise position and orientation of a ground-level image by aligningit with geo-referenced aerial imagery, offering a scalable alternative toGlobal Navigation Satellite Systems (GNSS) in challenging urban envi-ronments. Existing datasets rely on data collected with high-end sensorsuites, which inherently limit image diversity and scalability. While in-the-wild images are abundant, their noisy geo-tags make them unsuit-able for reliable evaluation. To bridge this gap, we introduce OpenCVL,a large-scale, diverse, and open dataset containing 617,388 ground-aerialimage pairs spanning 41 cities across four European countries. All imagesare sourced from permissive platforms, ensuring long-term accessibilityand supporting open and reproducible research. The training set com-bines images captured with high-end sensors with diverse in-the-wild im-agery. We further develop a data curation framework that filters and cor-rects pose annotations to construct reliable in-the-wild evaluation data.In addition, OpenCVL includes dedicated cross-area and snowy test setsto assess generalization and robustness. Experiments with a state-of-the-art CVL model on OpenCVL show that incorporating noisy in-the-wilddata consistently improves performance on clean test sets, suggesting apromising direction for scaling CVL with diverse real-world imagery.

We present a deployed system for on-orbit wildfire detec-tion aboard OroraTech’s thermal infrared Constellation Plane 1 (OTC-P1), operating under a demanding set of joint constraints: sub-megabytemodel footprint, sub-150 ms per-batch TensorRT FP16 inference on anNVIDIA Jetson Xavier NX, and an end-to-end alert pipeline targetingunder 10 minutes from satellite overpass to fire event communication.The system operates on uncalibrated mid-wave infrared (MWIR) single-band imagery at 200 m ground sampling distance, where fires frequentlyappear as sub-pixel or single-pixel thermal anomalies under extremeclass imbalance—challenges not addressed by the contextual thermal-thresholding pipelines (MODIS, VIIRS) that currently dominate opera-tional fire monitoring.We present an empirical study of lightweight dense representation learn-ing for this regime using a proprietary nine-satellite MWIR dataset. Wecompare dense masked autoencoding (DenseMAE) and a hybrid Dense-MAE+EMA (exponential moving average) distillation variant, and eval-uate representations via linear probing and full-distribution pixel-levelaverage precision (AP) under extreme class imbalance. DenseMAE pre-training enables compact downstream models that sit on the latency–accuracy Pareto frontier: our fastest SSL-pretrained model achieves 0.640test AP and 0.69 event-level Fire-F1 with 65.34 ms TensorRT FP16 la-tency per batch (224 × 224, B=8) and a 0.52 MB engine, achieved with-out pruning or compression. The best-performing configuration reaches0.699 test AP and 0.744 Fire-F1 while remaining below 1 MB. Bothoutperform a supervised baseline (up to 0.650 AP) under comparableconstraints.

Online reinforcement learning (RL) has become an effectivetool for aligning text-to-image models with human preferences, but itsefficiency is often limited by group-relative variance reduction. Exist-ing methods generate multiple trajectories per prompt to estimate localbaselines, which introduces synchronization overhead and can weaken thelearning signal when group rewards become homogeneous. We proposeMonolithic Policy Optimization (MPO), a group-free online RL frame-work based on a single-stream training principle: one prompt, one trajec-tory, and one policy update. MPO retains stochastic differential equation(SDE) exploration within each trajectory, while replacing group-wisebaselines with a persistent Bayesian value tracker. The tracker main-tains a history-aware reward estimate for each prompt and adapts itsuncertainty using a Girsanov-inspired policy-drift proxy, providing stableglobal advantage estimation under non-stationary policy updates. Acrosscompositional generation, visual text rendering, and human-preferencealignment benchmarks, MPO consistently improves alignment qualityover group-based baselines. It also delivers a 26× wall-clock speedupand a 5× sample-efficiency gain under matched training settings.