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ECCV 2026

Enhancing Alignment for Unified Multimodal Models via Semantically-Grounded Supervision

Jiyeong Kim, Yerim So, Hyesong Choi, Uiwon Hwang, Dongbo Min

Unified Multimodal Models (UMMs) have emerged as apromising paradigm that integrates multimodal understanding and gen-eration within a unified modeling framework. However, current gener-ative training paradigms suffer from inherent limitations. We presentSemantically-Grounded Supervision (SeGroS), a fine-tuning frameworkdesigned to resolve the granularity mismatch and supervisory redundancyin UMMs. At its core, we propose a novel visual grounding map toconstruct two complementary supervision signals. First, we formulatesemantic Visual Hints to compensate for the sparsity of text prompts.Second, we generate a semantically-grounded Corrupted Input to explic-itly enhance the supervision of masking-based UMMs by restricting theText-to-Image loss to core text-aligned regions. Extensive evaluations onGenEval, DPGBench, and CompBench demonstrate that SeGroS signif-icantly improves generation fidelity and cross-modal alignment acrossvarious UMM architectures.Project page: https://segros-project.github.io/

ECCV 2026

EgoSAT: A Comprehensive Benchmark of Egocentric Streaming Interaction Understanding

Yijia Lei, Jinzhao Li, Yichi Zhang, Jiacheng Hua, Yin Li, Miao Liu

We introduce EgoSAT, the first comprehensive benchmarkfor egocentric video reasoning in streaming settings, designed to evaluatethe capabilities of modern vision–language models (VLMs). The bench-mark targets streaming interaction understanding, where video framesarrive sequentially and models must continuously interpret evolving vi-sual context. EgoSAT unifies several previously distinct tasks within asingle streaming framework. In this formulation, queries about completedevents correspond to retrospective reasoning, queries about ongoing ac-tivities require online understanding, and queries about future actionsinvolve prospective anticipation. This unified setting requires models toreason about the past, present, and future while operating under theconstraint that only previously observed frames are available. EgoSATcontains 1,997 unique videos spanning 165 hours of egocentric footageand around 4,800 high-quality question–answer pairs, carefully designedto probe reasoning across varying temporal contexts. Using this bench-mark, we evaluate a diverse set of both open-weight and closed-weightVLMs, providing a systematic assessment of their ability for streaminginteraction understanding. By distinguishing answerability and conduct-ing diagnostics on confidence of models, we find existing models not onlystruggle with prospective and retrospective modeling, but also exhibit se-vere mis-calibration: confidence often fails to track inherent answerabil-ity, leading to dangerous “confidently wrong” behaviors. Project page:https://leiyj23.github.io/EgoSAT/

ECCV 2026

Dynamic Cluster Data Sampling for Efficient and Long-Tail-Aware Vision-Language Pre-training

Mingliang Liang, Zhuoran Liu, Arjen P. de Vries, Martha Larson

The computational cost of training a vision-language model(VLM) can be reduced by sampling the training data. Previous work onefficient VLM pre-training has pointed to the importance of semanticdata balance, adjusting the distribution of topics in the data to improveVLM accuracy. However, existing efficient pre-training approaches maydisproportionately remove rare concepts from the training corpus. As aresult, long-tail concepts remain insufficiently represented in the trainingdata and are not effectively captured during training. In this work, weintroduce a dynamic cluster-based sampling approach (DynamiCS) thatdownsamples large clusters of data and upsamples small ones. We firstdemonstrate the advantage of our cluster-scaling approach, which main-tains the relative order of semantic clusters in the data and emphasizesthe long-tail. This approach contrasts with current work, which focusesonly on flattening the semantic distribution of the data. Then, we showthe importance of dynamic sampling, which applies sampling at eachepoch to improve cross-epoch data diversity and make upsampling prac-tical. Our experiments show that DynamiCS reduces the computationalcost of VLM training and provides a performance advantage for long-tail concepts. Code available at https://github.com/MingliangLiang3/DynamiCS.

ECCV 2026

DisRM: Reward Modeling as Discriminative Prediction

Runtao Liu, Jiahao Zhan, Yuxuan Guo, Yingqing He, Chen Wei, Alan Yuille, Qifeng Chen

Reward models are central to post-training and test-timeoptimization for visual generative models, yet existing approaches typi-cally rely on either large-scale pairwise preference annotations (hundredsof thousands to millions of labeled pairs) or heavily engineered multi-metric scoring pipelines, both of which are costly and labor-intensive. Wepresent DisRM, a discriminative reward modeling framework that avoidsthe need for pairwise annotations by training a lightweight discriminatorto distinguish model-generated outputs from a small set of preferred rep-resentative samples, called Preference Proxy Data (PPD). As the rewardmodel is repeatedly re-trained to distinguish outputs from the updatedgenerator from fixed PPD samples, DisRM naturally supports iterative,multi-round co-refinement with the generator. Across evaluations of im-age quality, safety alignment, and video generation, DisRM achieves com-petitive and often better performance than methods trained with up to1 million annotated preference pairs, while using only 500 unlabeled tar-get samples in image-quality setting, and generalizes across Best-of-Nselection, SFT, and DPO.

ECCV 2026

DeRA: Decoupled Representation Alignment for Video Tokenization

Pengbo Guo, Junke Wang, Zhen Xing, Chengxu Liu, Daoguo Dong, Xueming Qian, Zuxuan Wu

This paper presents DeRA, a novel 1D video tokenizer thatdecouples the spatial-temporal representation learning in video tokeniza-tion to achieve better training efficiency and performance. Specifically,DeRA maintains a compact 1D latent space while factorizing video en-coding into appearance and motion streams, which are aligned withpretrained vision foundation models to capture the spatial semanticsand temporal dynamics in videos separately. To address the gradientconflicts introduced by the heterogeneous supervision, we further pro-pose the Symmetric Alignment-Conflict Projection (SACP) module thatproactively reformulates gradients by suppressing the components alongconflicting directions. Extensive experiments demonstrate that DeRAoutperforms LARP, the previous state-of-the-art video tokenizer by 25%on UCF-101 in terms of rFVD. Moreover, using DeRA for autoregressivevideo generation, we also achieve new state-of-the-art results on bothUCF-101 class-conditional generation and K600 frame prediction.

ECCV 2026

Defending from GeoLocalization through Adversarial Road Trips

Niccolò Niccoli, Federico Becattini, Lorenzo Seidenari

Retrieval-based image geolocalization has emerged as a powerful technique for de-termining the location of a query image by matching it against a large, geotagged database.The success of deep learning based approaches has raised concerns regarding privacy andsafety. A way to protect users from geolocalization is to design adversarial attacks for suchmethods. In this paper, we introduce RoadTrip Attack (RTA), a novel and highly effectivetargeted adversarial attack for geolocalization. RTA conceptualizes the adversarial process asfinding an optimal “distractor” journey to a specific, attacker-chosen location. It employs abeam search algorithm to iteratively construct a sequence of incorrect geographic locationsthat form a path to the target. At each step, the attack generates subtle perturbations tothe query image, guiding the geolocalization model toward the next location in this decep-tive path. We show that our method is also strong in black-box settings, obtaining highlytransferable attacks with less perceptible image artifacts.

ECCV 2026

CurveStream: Boosting Streaming Video Understanding in MLLMs via Curvature-Aware Hierarchical Visual Memory Management

Chao Wang, Xudong Tan, Jianjian Cao, Kangcong Li, Tao Chen

Multimodal Large Language Models have achieved signif-icant success in offline video understanding, yet their application tostreaming videos is severely limited by the linear explosion of visual to-kens, which often leads to Out-of-Memory (OOM) errors or catastrophicforgetting. Existing visual retention and memory management methodstypically rely on uniform sampling, low-level physical metrics, or pas-sive cache eviction. However, these strategies often lack intrinsic seman-tic awareness, potentially disrupting contextual coherence and blurringtransient yet critical semantic transitions. To address these limitations,we propose CurveStream, a training-free, curvature-aware hierarchicalvisual memory management framework. Our approach is motivated bythe key observation that high-curvature regions along continuous featuretrajectories closely align with critical global semantic transitions. Basedon this geometric insight, CurveStream evaluates real-time semantic in-tensity via a Curvature Score and integrates an online K-Sigma dynamicthreshold to adaptively route frames into clear and blurred memorystates under a strict token budget. Evaluations across diverse tempo-ral scales confirm that this lightweight framework, CurveStream, consis-tently yields absolute performance gains of over 10% (e.g., 10.69% onStreamingBench and 13.58% on OVOBench) over respective baselines,establishing new state-of-the-art results for streaming video perception.

ECCV 2026

Compact and Structurally Transparent Cervical Cytology with Geometry-Driven Features and Closed-Form Attention

Dichao Liu

Cervical cytology is practiced as a sequence of roles: screen-ing decides where to look, careful reading determines what is present,and reporting accounts for the decision. We translate this workflow intoa compact and structurally transparent model. Screening is cast as afree–energy allocation over four explicit, cytology–native concepts (nu-clear–envelope roughness, orientation disorder, local N/C ratio, chro-matin heterogeneity), yielding a closed-form spatial attention as a soft-max of negative energy. Reading is handled by an analytic backbonebuilt from fixed geometric responses, nonnegative near–diagonal chan-nel mixing, and convex residual updates, producing stable and auditablefeatures. A probabilistic readout with a linear head then provides anintrinsic evidence–to–logit accounting without post–hoc explainers. De-spite only 2.2M parameters, the model achieves state-of-the-art accuracyon DSCC (93.2% ACC), competitive results on SIPaKMeD (98.5% ACC)and Herlev (76.8% ACC), and favorable latency. Source code is publiclyavailable at https://github.com/Dichao-Liu/GeoCEAN.

ECCV 2026

CL4D: Contrastive Language–4D Pretraining for Vision-Language Reasoning in Dynamic Scenes

Kumal Hewagamage, Isuranga Senavirathne, Sasika Amarasinghe, Hasitha Gallella, Dulanga Weerakoon, Vigneshwaran Subbaraju, Ranga Rodrigo

4D understanding and reasoning is a fundamental capabil-ity for embodied AI agents operating in dynamic physical environments.However, existing vision encoders are largely limited to static 2D im-ages or 3D point clouds without temporal modeling, or to 2D videosthat lack accurate geometric depth reasoning. Consequently, current ap-proaches fail to jointly capture spatial structure and motion evolutionin dynamic scenes. We present CL4D, the first foundational 4D visionencoder that directly operates on dynamic point clouds, trained witha contrastive learning objective to align spatio-temporal geometric rep-resentations with natural language descriptions. By learning a shared1 ∗These authors contributed equally. † These authors jointly supervised this work.embedding space between text and 4D scene dynamics, CL4D enableszero-shot motion-to-text and text-to-motion retrieval in dynamic en-vironments and serves as a foundational 4D vision encoder for down-stream 4D vision–language tasks. Building on this encoder, we intro-duce 4DVLM, a 4D vision–language model that conditions languagegeneration on dynamic geometric representations. 4DVLM is the firstVLM designed to operate directly on 4D point clouds without relyingon 2D images, 2D videos, or static 3D point clouds. We train CL4D andsubsequently 4DVLM on a newly constructed dataset termed DynAc-tion4D capturing diverse human motions across varying object inter-actions and scene environments. Extensive experiments across multiple4D human action benchmarks demonstrate that CL4D achieves state-of-the-art performance, with improvements of approximately ∼16.75% overprior methods. Furthermore, 4DVLM outperforms frontier video VLMssuch as Gemini and GPT-5 even when these models are provided withRGB video sequences corresponding to the same scenes represented as4D point clouds for 4DVLM.

ECCV 2026

Broadband Wide Field of View Imaging with Computational Mirrors

Vishwanath Saragadam, Niki Nezakati, Amit Roy-Chowdhury, Vivek Boominathan

Traditional glass-based optics are typically optimized for nar-row spectral bands, such as the visible (400–700nm) or shortwave infrared(1000–1800nm). While the emergence of VIS-SWIR sensors (400–1700nm)offers transformative potential, refractive optics struggle to focus thisentire range simultaneously. Mirrors represent a promising achromaticalternative; however, they are often sidelined by field curvature, and off-axis aberrations. This paper introduces Computational Mirrors, aframework that enables high-resolution, full-field-of-view imaging acrossthe complete VIS-SWIR spectrum using a single sensor. Our method isbuilt on the observation that distinct regions of the field of view reachfocus at varying distances from the mirror. By capturing a minimal fo-cal stack (2–4 images), we utilize a computational backend to recovera sharp, all-in-focus image. A key contribution of this paper is Seidel-Conv, a novel, physics-inspired, spatially-varying point spread function(PSF) model designed to accurately characterize and correct the off-axisaberrations inherent in simple concave mirrors. We demonstrate the ef-ficacy of our approach using a first-of-its-kind 50mm F/1 optical systemequipped with a VIS-SWIR sensor. Our system produces sharp imagesacross RGB, NIR, and SWIR wavelengths without requiring refocusing,revealing material details invisible within individual spectral bands. Wefurther validate the scalability of our approach with a 100mm F/2 systemoptimized for long-range imaging.

ECCV 2026

BLASt3R: Bundle Adjustment of Any Image Set with Multi-View Matching and Monocular priors

Vincent Leroy, Philippe Weinzaepfel, Lojze Zust, Yohann Cabon, Jerome Revaud

Recent hybrid Structure-from-Motion (SfM) systems com-bine the robustness of feed-forward 3D reconstruction with the accuracyof traditional bundle adjustment (BA) with pixel matching. They areusually the best performing methods however their scalability and us-ability remains limited since estimating dense correspondences betweenviews is prohibitively costly, especially considering time constraints in-herent to online applications like Visual SLAM (VSLAM). In this paper,we introduce a regularized BA framework that leverages a fast multi-view matcher and monocular priors for initialization and regularization.In contrast to existing systems, our unified approach seamlessly supportsboth online VSLAM and offline reconstruction from unordered image col-lections within the same optimization framework and sharing commonhyperparameters for all tasks. Extensive experiments across both do-mains demonstrate improved performance and speed tradeoffs over tra-ditional, feed-forward, and hybrid baselines. Notably for VSLAM, ouruncalibrated method outperforms all previous calibrated approaches.

ECCV 2026

BioMTBee: Biologically Constrained Multi-View Template-Based 3D Reconstruction of Bumblebee

Yi Zhang, Minchen Ye, Nenggan Zheng

Bumblebees play a key role in agriculture, ecology, and bio-inspired robotics, making accurate 3D surface reconstruction essential forquantitative behavioral analysis. However, their dark dense hairs, com-plex structure and flexible limbs prone to visual occlusion and annotationdifficulties, resulting in difficulties in reconstruction. Current template-free or generic template-based models offer limited structural detail andbiological realism, while high-quality bumblebee templates and reliablemorphological priors are lacking. To address these challenges, we pro-pose BioMTBee, a template-based 3D mesh reconstruction frameworkfor bumblebees. We built a high-fidelity articulated mesh template frommicro-CT scans to provide an accurate morphological prior. A multi-view3D pose estimator with spatio-temporal filtering (BPST) is then intro-duced to extract robust 3D keypoints from 2D detections, guiding stablemesh template fitting. Building on this template, we combine 3D poseand shape supervision with biological constraints—bilateral symmetry,kinematic coupling, and temporal smoothness—to achieve anatomicallyconsistent and temporally stable surface reconstructions. Experiments on3D reconstruction from multi-view images show improvements in mor-phological accuracy and biological plausibility over representative base-lines, capturing fast limb movements and fine structural and texturaldetails, and also generalize well to cross-species reconstruction.

ECCV 2026

Audio-Visual Camera Pose Estimation with Passive Scene Sounds and In-the-Wild Video

Ikechukwu D Adebi, Sagnik Majumder, Kristen Grauman

Understanding camera motion is a fundamental problem inembodied perception and 3D scene understanding. While visual methodshave advanced rapidly, they often struggle under visually degraded condi-tions such as motion blur or occlusions. In this work, we show that passivescene sounds provide cues complementary to vision for relative camerapose estimation for in-the-wild videos. We introduce a simple but effec-tive audio-visual framework that integrates direction-of-arrival (DOA)spectra and binauralized embeddings into a state-of-the-art vision-onlypose estimation model. Our results on two large datasets show consistentgains over strong visual baselines, plus robustness when the visual infor-mation is corrupted. To our knowledge, this represents the first work tosuccessfully leverage audio for relative camera pose estimation in real-world videos, and it establishes incidental, everyday audio as an unex-pected but promising signal for a classic spatial challenge.

ECCV 2026

Anchoring and Steering Diffusion: Enhancing the Faithfulness of Text-to-Image Generation at Inference Time

Xinyi Wang, Yuyang Huang, Yalin Su, Pengcheng Luan, Tao Zhang, Feiming Wei, Wenxian Yu

While text-to-image diffusion models achieve impressive vi-sual quality, they frequently struggle to maintain precise alignment withcomplex compositional prompts. An effective strategy is to improve theinference process of diffusion models, thereby better leveraging their pre-trained priors to address misalignment. Existing training-free methodscan be divided into two categories. The first category focuses on improv-ing the randomly sampled initial noise to obtain an initialization thatencapsulates semantics relevant to the target text. However, existing ap-proaches either perform costly search over noise pools, with no guaranteeof finding a truly prompt-compatible noise within the limited candidates,or manipulate sampled noise without simultaneously ensuring reliablesemantic injection and preservation of the Gaussian distribution. Thesecond category focuses on improving the denoising trajectory. However,these methods lack explicit mechanisms to timely diagnose and correctCorresponding author: Tao Zhang.semantic errors, and therefore fail to prevent error propagation duringgeneration. To address these limitations, we propose AnchorSteer, atraining-free framework that exerts fine-grained control over both ini-tialization and the denoising trajectory. AnchorSteer consists oftwo synergistic components: Semantic Anchoring replaces uninfor-mative Gaussian noise with text-aligned initializations via CLIP-basedprior extraction and a novel Latent-Prior Score Distillation Sampling(LP-SDS) objective. Specifically, LP-SDS distills CLIP visual priors intothe knowledge distribution of diffusion models, mitigating the domaingap between CLIP-based priors and diffusion-based priors. ReflectiveSteering transforms passive denoising with an active Think–Erase–Retouch loop that enables mid-generation self-correction. Think phaseemploys VLM-based dual diagnosis to detect semantic deviations. Erasephase performs a targeted latent rollback with negative-guided inversionto suppress erroneous content, and Retouch phase subsequently appliespositive-guided refinement to recover the missing attributes. Therefore,Reflective Steering enables timely correction of semantic deviationsalong the denoising trajectory, preventing error propagation. Extensiveexperiments on GenEval and T2I-CompBench++ demonstrate that An-chorSteer consistently outperforms existing baselines in text–image align-ment while preserving high visual quality.

ECCV 2026

Zero-Shot Quantization for Object Detectors using Off-the-Shelf Generative Models

Hyunho Lee, Kyomin Hwang, Hyeonjin Kim, Suyoung Kim, Sunghyun Wee, Nojun Kwak

With an increasing number of Object Detection (OD) modelsbeing deployed on edge devices, Zero-Shot Quantization for OD (ZSQ-OD) aims to quantize these models when access to the original trainingdata is prohibited. Existing research on Zero-Shot Quantization-AwareTraining (QAT) for OD synthesizes training sets through noise optimiza-tion. However, this approach struggles to maintain performance in low-bit regions. In this paper, we introduce GoodQ (Generative off-the-shelfmodels for object detector Quantization), a QAT pipeline that utilizesoff-the-shelf generative models to construct a training set. We first iden-tify three challenges that arise when introducing a generative model tothe ZSQ-OD task: 1) each image contains dense information with mul-tiple instances, 2) the class-wise distribution in the original dataset isimbalanced, and 3) the pseudo-labels assigned to the generated imagescan potentially act as noisy signals during QAT. GoodQ addresses thesechallenges by 1) introducing an Information-Dense Prompting strategyto generate multi-instance images, 2) applying Intrinsic Distribution-Aware Selection to match the pretrained class distribution, and 3) em-ploying Teacher-guided Adaptive Noise Reduction to mitigate noise aris-ing from the QAT process. Our framework achieves state-of-the-art per-formance in low-bit ZSQ (W4A4) and extends quantization to extremebit-widths (W3A3). Furthermore, we conduct an extensive analysis touncover the underlying factors contributing to the efficacy of GoodQ.The constructed dataset is available at GoodQ.

ECCV 2026

VLA Knows Its Limits

Haoxuan Wang, Gengyu Zhang, Yan Yan, Ramana Kompella, Gaowen Liu

Action chunking has recently emerged as a standard prac-tice in flow-based Vision-Language-Action (VLA) models. However, thee!ect and choice of the execution horizon—the number of actions tobe executed from each predicted chunk—remains underexplored. In thiswork, we first show that varying the execution horizon leads to sub-stantial performance deviations, with performance initially improvingand then declining as the horizon increases. To uncover the reasons, weanalyze the cross- and self-attention weights in flow-based VLAs and re-veal two key phenomena: (i) intra-chunk actions attend invariantly tovision–language tokens, limiting adaptability to environmental changes;and (ii) the initial and terminal action tokens serve as stable anchors,forming latent centers around which intermediate actions are organized.Motivated by these insights, we interpret action self-attention weights asa proxy for the model’s predictive limit and propose AutoHorizon, thefirst test-time method that dynamically estimates the execution horizonfor each predicted action chunk to adapt to changing perceptual condi-tions. Across simulated and real-world robotic manipulation tasks, Au-toHorizon is performant, incurs negligible computational overhead, andgeneralizes across diverse tasks and flow-based models. Demonstrationvideos are available at this project page.

ECCV 2026

Video-Oasis: Rethinking Evaluation of Video Understanding

Geuntaek Lim, Minho Shim, Sungjune Park, Jaeyun Lee, Inwoong Lee, Taeoh Kim, Dongyoon Wee, Yukyung Choi

The inherent complexity of video understanding makes it dif-ficult to determine whether Video-LLM benchmark performance stemsfrom visual perception, linguistic reasoning, or knowledge priors. Whilemany benchmarks have emerged to assess high-level reasoning, sharedcriteria for evaluating video understanding remain largely overlooked.Instead of introducing yet another benchmark, we take a step back tore-examine the criteria for evaluating video understanding. In this work,we introduce Video-Oasis, a sustainable diagnostic suite for systemati-cally auditing existing video understanding benchmarks. This audit re-veals that 55% of existing benchmark samples are solvable without visualinput or temporal context. After filtering these shortcuts, the remainingvideo-native challenges expose a substantial capability gap: state-of-the-art models perform only marginally above random guessing. Building onthese findings, we use the distilled challenges as a testbed to investigatewhich algorithmic design choices contribute to robust video understand-ing. We hope our work provides a practical foundation for constructingrigorous video benchmarks and evaluating future Video-LLMs. Code isavailable at https://github.com/sejong-rcv/Video-Oasis.

ECCV 2026

Unleashing the Power of Large-Scale ViT in Zero-Shot SBIR: A Strong Baseline with Multi-Layer Feature Aggregation

Yang Liu, Yongjing Guo, Suisui Jia, Huaizhou Qi, Xun Du, Haonan Chen

The domain gap between abstract sketches and natural im-ages, combined with the challenge of zero-shot generalization, remainsa critical bottleneck in Sketch-Based Image Retrieval (ZS-SBIR). Whilescaling up to large-scale Vision-Language Models offers powerful baselinerepresentations, naive utilization—such as relying solely on the terminal[CLS] token—fails to fully exploit their potential, often discarding thefine-grained structural primitives essential for abstract sketch alignment.In this paper, we propose a robust framework that bridges this modalitygap by decoupling and composing visual states and semantics. First, ourMulti-Layer Feature Aggregation (MLFA) acts as an anti-compressionmechanism, explicitly reviving low-level geometric primitives alongsidehigh-level semantic objects. Second, a Phased Cross-Modal Interaction(PCMI) serves as a dynamic compositional engine: learnable promptsprogressively query and bind these structural states with global seman-tics for precise coarse-to-fine alignment. Extensive experiments demon-strate our framework achieves state-of-the-art results on several bench-marks and remains competitive on QuickDraw Ext.

ECCV 2026

Unfold The World: Factorize 4D Properties in Reinforcing Spatial Understanding

Yijun Yang, Shenghe Zheng, Wenbo Li, Jianhui Liu, Haoze Sun, Yanbing Zhang, Jiaxiu Jiang, Lin Song, Haoyang Huang, Nan Duan, Lei Zhu

Despite the remarkable prowess of Vision-Language Models(VLMs) in general multimodal tasks, they remain fundamentally “flat”when reasoning about the physical world. We argue that this spatialbottleneck stems from a profound dimensional mismatch: while VLMsare trained to interpret 2D projections, true spatial reasoning demandsthe recovery of latent 3D geometry and temporal continuity. To conquerthis high-dimensional complexity, we advocate a shift from monolithiclearning to a “divide and conquer” paradigm. We present FactoSR, afactorized reinforcement learning framework that explicitly interpret the* Equal Contribution, † Project Lead, B Corresponding Authordimensions collapsed by visual projection. At its core, FactoSR decom-poses the monolithic problem of world-consistent reasoning into three or-thogonal, geometric sub-objectives: planar correspondence (XY ), depthconsistency (Z), and temporal reversibility (T ). By optimizing these ver-ifiable constraints within a unified policy learning mechanism, we effec-tively transform an ill-posed projection recovery problem into a series oftangible reasoning steps. Extensive evaluations on multi-view and videobenchmarks demonstrate that this elegant decomposition yields substan-tial gains in 3D and 4D reasoning, achieving a 5.9% boost on VSI-Benchand 4.5% on All-Angles-Bench. Our findings suggest that reinforcing ex-plicit, factorized 4D consistency is a critical step toward evolving VLMsinto robust, world-aware reasoners.Code: https://github.com/scott-yjyang/FactoSR

ECCV 2026

TurboMPLE: Joint Infrared Turbulence Mitigation and Physical Fields Estimation via Mutual Progressive Layered Extraction

Yitong An, Yubo Jiang, Xiangzhi Bai*

Atmospheric turbulence severely degrades thermal infraredimaging quality, yet most existing mitigation methods focus solely on im-age restoration while ignoring underlying physical characteristics. In thisstudy, we construct a large-scale infrared turbulence imaging dataset andproposed the Turbulence-oriented Mutual Progressive Layered Extrac-tion (TurboMPLE), a joint network for infrared turbulence mitigationand physical fields estimation. Through Mutual PLE Blocks, TurboM-PLE achieves progressive feature-level collaboration, introducing turbu-lence priors into the mitigation process while incorporating richer visualinformation into physical fields estimation. In addition, embedding theLambert–Beer law and turbulent physics constraints enables more in-terpretable results. With a lightweight and computationally efficient de-sign, TurboMPLE achieves state-of-the-art performance on both tasksand demonstrates strong generalization capability on real-world data.The code is available at https://github.com/Ayt777/TurboMPLE.

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