ECCV 2026 论文列表

Blind image deblurring demands the recovery of high-fidelitydetails and coherent structures from complex, unknown degradations.Current blind image deblurring methods struggle with real-world, spa-tially varying degradations, and lack the semantic awareness necessary toreliably differentiate valid textures from artifacts. To bridge this gap, wepropose CogSENet, a dynamic, semantic-aligned reconstruction frame-work inspired by the eagle’s visual system. By mimicking the eagle’sactive saccadic scanning, we devise a Semantic-Driven State Space Mod-ule (SDSSM) with semantic-aware token regrouping via differentiablerouting, enabling prompt-conditioned long-range dependency modeling.To ensure physically interpretable recovery of textures and structures,a BiFreqFusionBlock (BFFB) mirrors functional differentiation of theeagle’s retina by decomposing features into high and low frequenciesusing wavelet transforms. Finally, we estimate a continuous Blur Field(CBF) from blur image and fuse it with CLIP semantic priors to modu-late the deepest latent features, emulating focal adaptation and enablingadaptive restoration under spatially non-uniform blur. Extensive exper-iments demonstrate that CogSENet outperforms state-of-the-art deblur-ring methods in both visual quality and structural fidelity with fewerparameters, while also performing favorably on dehazing, deraining, anddenoising tasks.

Recent advances in Large Language Model (LLM)-based agentshave shown remarkable progress in code generation. However, currentagent methods mainly rely on text-output-based feedback (e.g. command-line outputs) for multi-round debugging and struggle in graphical userinterface (GUI) that involve visual information. This is mainly due totwo limitations: 1) GUI programs are event-driven, yet existing methodscannot simulate user interactions to trigger GUI element logic. 2) GUIprograms possess visual attributes, making it difficult for text-based ap-proaches to assess whether the rendered interface meets user needs. Tosystematically address these challenges, we first introduce InteractGUIBench, a novel benchmark comprising 984 commonly used real-worlddesktop GUI application tasks designed for fine-grained evaluation ofboth interaction logic and visual structure. Furthermore, we propose VF-Coder, a vision-feedback-based multi-agent system for debugging GUIcode. By perceiving visual information and directly interacting with pro-gram interfaces, VF-Coder can identify potential logic and layout issuesin a human-like manner. On InteractGUI Bench, our VF-Coder approachincreases the success rate of Gemini-3-Flash from 21.68% to 28.29% andraises the visual score from 0.4284 to 0.5584, indicating the effectivenessof visual feedback in GUI debugging.

Contrastive Language-Image Pre-training (CLIP) relies onVision Transformers whose attention mechanism is susceptible to spu-rious correlations and scales quadratically with resolution. To addressthese limitations, we present CLIMP, the first fully Mamba-based con-trastive vision-language model that replaces both the vision and text en-coders with state-space architectures. VMamba’s cross-scan mechanismcaptures spatial inductive biases that reduce reliance on spurious correla-tions, producing an embedding space with tighter cross-modal alignmentand lower hubness - geometric properties that translate to superior re-trieval and out-of-distribution robustness, surpassing even CLIP-ViT-Btrained on a dataset 167× larger on ImageNet-O. CLIMP naturally sup-ports variable input resolutions without positional encoding interpolationor specialized training, achieving up to 6.6% higher retrieval accuracyat 16× training resolution while using 5× less memory and 1.8× fewerFLOPs. Mamba’s autoregressive nature further enables processing of ar-bitrarily long text, overcoming CLIP’s fixed 77-token context limitationfor dense captioning retrieval. Our scaling experiments across model sizesand dataset sizes show consistent, unsaturated improvements - indicat-ing that CLIMP’s architectural advantages are not limited by trainingscale. These results demonstrate that Mamba is a compelling alternativeto Transformers for vision-language pre-training. The code and modelsare publicly available at https://github.com/NimrodShabtay/CLIMP

Implicit neural representation (INR) has emerged as a pow-erful prior for multi-dimensional data (e.g., multispectral images andvideos). However, most INR methods employing periodic activation func-tions (e.g., Sine) predominantly rely on function composition. This mech-anism introduces optimization instability as network depth increases,thereby limiting their performance. Meanwhile, these methods fail to in-corporate proper physical priors to effectively alleviate spectrum bias. Toaddress these issues, inspired by the commonalities between deep periodicnetworks and generalized Fourier series, we propose a novel CalibratedHarmonic Overlaid Implicit Neural Representation (CHOIR). Specifi-cally, we utilize Coordinated Harmonic Superposition (CHS) to replacethe conventional function composition used in most INRs, thereby en-suring optimization stability when scaling network depth. Furthermore,we introduce a Perceptual Spectrum Calibration (PSC) to mitigate spec-trum bias. This calibration embeds the ubiquitous power-law spectrumprior of natural images and adjusts the globally fixed spectrum towards aphysically plausible log-uniform distribution. Extensive experiments onvarious multidimensional data recovery problems demonstrate that ourmethod achieves superior performance over state-of-the-art approaches.Code is available at https://github.com/chorl0229/CHOIR.

Existing imitation learning methods for end-to-end autonomousdriving predominantly learn from successful demonstrations by minimiz-ing geometric deviations from expert trajectories. This paradigm implic-itly assumes that spatial proximity implies behavioral safety, leading toa critical objective mismatch: trajectories with nearly identical imita-tion losses may exhibit drastically different safety outcomes, where oneremains recoverable while the other results in collision. To address thislimitation, we propose BeyondDrive, a failure-aware imitation learn-ing framework that jointly learns from successful and failed driving be-haviors. First, we introduce a flow matching-based negative trajectory† Corresponding author. Code link: https://github.com/wjl2244/BeyondDrive.This work was supported by Beijing Natural Science Foundation-Xiaomi InnovationJoint Fund L253007, by Beijing Nova Program (202604841268) and Beijing NaturalScience Foundation under Grant 4242052.generator that synthesizes safety-critical yet expert-proximate trajecto-ries, enabling explicit modeling of safety asymmetry. Second, we developa diversity-aware sampling strategy that mitigates mode collapse andimproves coverage of diverse failure modes during negative trajectorygeneration. Third, we propose a Repulsive Distance Loss that simultane-ously attracts predictions toward expert demonstrations while repellingthem from hard negative trajectories, thereby establishing discrimina-tive safety boundaries in trajectory space. Applied to the uni-modalbaseline Latent TransFuser, BeyondDrive achieves 89.7 PDMS on theNAVSIMv1 closed-loop benchmark, outperforming prior state-of-the-artmethods. Moreover, BeyondDrive generalizes effectively across differentautonomous driving architectures, including multi-modal planners, andfurther demonstrates strong zero-shot transferability on the HUGSIMbenchmark.

Road surfaces serve as the only contact medium between ve-hicle wheels or robot feet and the physical world. Reconstructing the roadsurface is crucial for unmanned vehicles and mobile robots. Recent stud-ies on Neural Radiance Fields (NeRF) and Gaussian Splatting (GS) haveachieved remarkable results in scene reconstruction. However, they typi-cally rely on multi-view image inputs and require prolonged optimizationtimes. In this paper, we propose BEV-GS, a real-time single-frame roadsurface reconstruction method based on feed-forward Gaussian splatting.BEV-GS consists of a prediction module and a rendering module. Theprediction module introduces separate geometry and texture networksfollowing the Bird’s-Eye-View (BEV) paradigm. Geometric and textureparameters are directly estimated from a single frame, avoiding per-sceneoptimization. In the rendering module, we utilize grid Gaussian for roadsurface representation and novel view synthesis, which better aligns withroad surface characteristics. Our method achieves state-of-the-art per-formance on the real-world dataset RSRD. The road elevation error isreduced to 1.73 cm, and the PSNR of novel view synthesis reaches 28.36dB. The prediction and rendering FPS are 26 and 2061, respectively,enabling high-accuracy and real-time applications. Our code is availableat https://github.com/IRMVLab/BEV-GS.

Open-set action recognition (OSAR) requires a model toclassify known actions while rejecting unseen ones. Existing methodstypically use predictive uncertainty only at inference time, treating it asa rejection score after representation learning is complete. This leaves theunderlying representation unchanged and allows spurious background orcontext cues to remain embedded in the model, which weakens known–unknown separation. We propose a staged uncertainty-guided fine-tuningframework that uses ensemble-derived epistemic uncertainty not only forrejection, but also as a training signal for representation refinement. Theframework proceeds in three stages: motion-guided ensemble training tobias learning toward action-relevant evidence, in-distribution epistemicregularization to improve uncertainty reliability on known samples, anduncertainty attribution mask-based input attention (UAM-IA), whichconverts uncertainty attribution maps into input attention masks for re-finement. To improve deployment efficiency, we further distill the refinedensemble into a single student model trained to approximate both theensemble predictive distribution and the ensemble-derived epistemic un-certainty. Under a controlled OSAR protocol with UCF101 as the knownset and HMDB51 and MiT-v2 as unknown sources after removing over-lapping classes, the proposed method improves known–unknown separa-tion across multiple backbones. The distilled student preserves much ofthis benefit while enabling efficient single-model inference.

Inspired by text prompts in large language models, visualprompts have been explored to enhance the perceptual capabilities oflarge vision-language models (LVLMs). However, performance tends tosaturate under single visual prompt designs, making further prompt engi-neering increasingly ineffective. To address this limitation, we shift fromprompt engineering to prompt retrieval and propose AutoV, a lightweightframework for instance-adaptive visual prompt identification. Given aninput image and a textual query, AutoV automatically locates the mostsuitable visual prompt from a diverse candidate pool. Training such a re-trieval framework requires prompt-level supervision, yet prompt qualityis inherently ambiguous and difficult to assess reliably, even for humans.To enable automatic supervision, we evaluate visual prompts using a pre-trained LVLM and label them according to their prediction losses. Usingthe loss-oriented ranking as a robust training signal, AutoV learns to re-trieve the query-aware optimal prompt for each instance without manualannotation. Experiments indicate that AutoV enhances the performanceof various LVLMs on image understanding, captioning, grounding, andclassification tasks. For example, AutoV improves LLaVA-OV by 10.2%on VizWiz and boosts Qwen2.5-VL by 3.8% on MMMU, respectively.

Segmenting thin structures such as infrastructure cracks andanatomical vessels is hampered by topology-sensitive geometry, highannotation cost, and poor cross-domain generalization. We propose FMS2 ,a unified flow-matching framework with two complementary modules.SegFlow is a 2.96M-parameter encoder–decoder segmentation modelthat recasts prediction as continuous image→mask transport: it learnsa time-indexed velocity field with a single flow-matching regression lossand obtains the mask through deterministic ODE integration, ratherthan supervising only end-state logits. This trajectory-level supervisionimproves thin-structure continuity and sharpness over topology-awareloss baselines, without auxiliary topology heads, post-processing, or multi-term loss engineering. SynFlow is a mask-conditioned mask→imagegenerator that produces pixel-aligned image–mask pairs by injectingmask geometry at multiple scales, emphasizing boundary bands withedge-aware gating, and expanding sparsity, width, and branching througha controllable mask generator. On five crack and vessel benchmarks,SegFlow alone outperforms CNN, Transformer, Mamba, and generativebaselines, improving mean IoU from 0.511 to 0.599 (+17.2%) and reducingBetti matching error from 82.145 to 51.524 (−37.3%). With limitedlabels, SynFlow recovers near-full performance using 25% real annotationsand improves cross-domain IoU by 0.11. Unlike classical augmentation,SynFlow provides pixel-aligned supervision with controllable structuralshifts (e.g., sparsity, width, branching), making it effective under domainshift. We release 10k crack and 1k vessel image–mask pairs. The projectpage is available at FMS2 .

Localising multiple sound sources in visual scenes remains afundamental challenge in multimodal perception due to an inherent cir-cular dependency: separating mixed audio requires knowing source loca-tions, while identifying sound-producing regions requires separated audiosignals. In this paper, we focus on the dual-source setting and discover aselective convergence in self-supervised audio-visual learning: when pre-sented with multiple sound sources, contrastive models naturally con-verge to the most salient audio-visual correspondence rather than at-tempting to represent all sources equally. This emergent phenomenon,analogous to human selective auditory attention, enables us to breakthe above circular dependency through a progressive two-stage frame-work: first, leveraging selective convergence to identify dominant sources,and then exploiting these learned priors to uncover remaining sources.Our self-supervised approach achieves the best performance among self-supervised methods on dual-source benchmarks without requiring anymanual annotations, and even surpasses some weakly-supervised ap-proaches on certain metrics. Furthermore, we identify a fundamentalevaluation inconsistency in existing benchmarks: comparing continuouslocalisation heatmaps against bounding-box annotations creates system-atic biases, particularly for non-axis-aligned objects where the boundingbox includes substantial background regions. To address this, we intro-duce pixel-level segmentation masks to the existing benchmark, enablingspatially-aligned evaluation. Together, these results suggest that embrac-ing rather than suppressing selectivity offers a scalable, annotation-freeroute to multi-source localisation.

We introduce WAFT-Stereo, a simple and effective warping-based method for stereo matching. WAFT-Stereo demonstrates that costvolumes, a common design used in many leading methods, are not nec-essary for strong performance and can be replaced by warping with im-proved efficiency. WAFT-Stereo ranks first on ETH3D (BP-0.5), Middle-bury (RMSE), and KITTI (all metrics), reducing the zero-shot error by81% on ETH3D, while being 1.8−6.7× faster than competitive methods.Code and model weights are available at https://github.com/princeton-vl/WAFT-Stereo.

We propose UniDynamics, a diffusion-based framework forfuture 4D dynamic scenes (RGB, depth, and optical flow) generationfrom a single event-RGB pair, without requiring long histories or con-trol priors as in existing methods, while explicitly modeling future mo-tion fields. The core idea is to leverage event streams to offer an al-ternative motion prior for single-RGB extrapolation, and to enforce ge-ometric and motion constraints throughout generation via multimodalmodeling. Specifically, we design an Event Latent Enhancement (ELE)module to align and enhance event latents into diffusion-injectable con-ditioning features, providing robust initial motion priors and reliabletexture/structure cues. We further introduce a Perceptual DynamicsSpace (PDS) embedded in the multi-scale U-Net, which decouples andadaptively interacts depth and flow while continuously feeding back con-straints to appearance features, improving geometric-motion consistencyfor physically plausible and spatiotemporally coherent prediction. Exper-iments on VKitti2 and DSEC demonstrate state-of-the-art performance,producing high-quality, temporally coherent, and 4D-consistent futurepredictions, especially under challenging high-speed motion blur.

Ultra-high-resolution image generation is increasingly vital for applications requiring fine-grained textures and global structural fidelity, yet state-of-the-art text-to-image diffusion models such as FLUX and SD3 remain confined to sub 2MP (< 1K × 2K) resolutions due to the quadratic complexity of attention mechanisms and the scarcity of high-quality high-resolution training data. We present UltraImageGen, a novel framework that introduces hierarchical local attention with low-resolution global guidance, enabling efficient, scalable, and semantically coherent image synthesis at ultra-high resolutions. Specifically, high-resolution latents are divided into hardware aligned fixed-size local windows to reduce attention complexity from quadratic to near-linear, while a low-resolution latent with scaled positional embeddings injects global semantics as an anchor. A lightweight LoRA adaptation bridges global and local pathways during denoising, ensuring consistency across structure and detail. To maximize efficiency and achieve scalable ultrahigh-resolution generation, we repermute token sequence in window-first order, so that the GPU-friendly dense local blocks in attention calculation equals to the fixed-size local window in 2D regardless of resolution. Together UltraImageGen reliably scales pretrained models to resolutions higher than 8K with more than 10× speed up and significantly lower memory usage. Extensive experiments demonstrate that UltraImageGen achieves superior quality while maintaining computational efficiency, establishing a practical paradigm for advancing ultra-high-resolution image generation. Our project page and code are available here.

We present a dense RGB-D SLAM system using differen-tiable triangles as the 3D map representation. While 3D Gaussian Splat-ting has emerged as the leading method for novel-view synthesis, trianglesremain the standard primitive for traditional rendering hardware, gameengines, and downstream tasks requiring explicit geometry such as simu-lation, collision, and editing. Recent offline methods have demonstratedthat an unstructured ‘triangle soup’ can be optimised into a photorealis-tic mesh via Delaunay triangulation across a set of posed images. Build-ing upon this insight, we present the first dense SLAM system to employTriangle Splatting to perform both tracking and mapping through on-line differentiable rendering of a triangle soup. The map can be convertedinto a connected mesh on-the-fly via restricted Delaunay triangulation,enabling new online capabilities such as mesh deformation and collisionchecking. On Replica and TUM-RGBD, our system outperforms base-lines on 3D geometry, matches the camera-tracking accuracy, and enablesonline mesh-based scene editing.

Bio-inspired Small Target Motion Detection (STMD) mod-els often suffer “false alarm collapse” due to non-stationary environ-mental interference, failing to adapt to dynamic background statistics.We propose a bio-inspired architecture integrating macro-motion decou-pling with micro-scale Adaptive Gain Control (AGC). Macroscopically, itleverages lobula plate tangential cells (LPTC)-based population codingto eliminate ego-motion-induced baseline drifts in real-time with mini-mal overhead. Microscopically, we introduce an adaptive shunting inhi-bition operator inspired by Natural Scene Statistics (NSS). By quan-tifying local temporal variance, this mechanism maps non-stationaryclutter into a gain control factor to suppress heavy-tailed noise fromhigh-frequency flickers. This versatile plug-and-play module integratesseamlessly into existing STMD frameworks. Extensive experiments onsynthetic and real-world datasets demonstrate that our model signifi-cantly reduces false alarms while maintaining high sensitivity for dimtargets, achieving state-of-the-art precision and robustness in complexdynamic environments. Our code is publicly available at AGC.

True video intelligence demands more than recognizing whatis visible: it requires reasoning about why events unfold, predicting whatwould change under different conditions, and deciding what to do next.We refer to this full progression—from perception through causal reason-ing and simulation to strategic planning—as Strategic Video Intelligence(SVI). No existing benchmark evaluates this capability stack: in-the-wildvideos lack verifiable ground truth for causal and strategic questions,while synthetic environments sacrifice the complexity of real multi-agentsystems. To bridge this gap, we introduce SVI-Bench, a large-scale bench-mark that leverages team sports as a dynamic microworld, a domain thatuniquely combines the complexity of real-world multi-agent interactionwith the verifiability of explicit rules and definitive outcomes. SVI-Benchcomprises ∼35K hours of broadcast video, ∼15M annotated actions,∼15K hours of expert commentary, ∼23K game reports, and ∼103Kstructured statistical records across basketball, soccer, and hockey, allconstructed via a data engine that transforms raw game data into adense, cross-referenced corpus. We organize evaluation into 9 tasks span-ning a progressive four-pillar hierarchy: Dynamic Scene Understanding,Causal Reasoning, Strategic Simulation, and Agentic Synthesis. Evaluat-ing strong multimodal and agentic baselines, we find a capability cliff :models perform competently on perceptual tasks (achieving ∼74% onfine-grained action QA) but degrade sharply at higher levels of the stack.Agentic tasks prove hardest of all: the strongest model achieves only 5%accuracy when required to autonomously gather and integrate evidenceacross a corpus of 1.8M clips. We release the full benchmark to catalyzeprogress toward AI systems capable of strategic intelligence in complex,dynamic multi-agent environments.§ Code github.com/texaser/svi-benchõ Data huggingface.co/mvp-group/svi-bench€ Website svi-bench.github.ioA Extended Paper svi-bench.github.io/svi_bench_extended.pdfPillar 1: Perceptionparsing who is where, doing what and whenPillar 2: Reasoningexplaining why something happenedPillar 3: Simulationgenerating plausible alternative futuresPillar 4: Agencyautonomous analysis over multimodal evidenceThe Strategic Video Intelligence StackFig. 1: Overview of SVI-Bench, illustrated through a single play from the 2022NCAA Final Four. SVI-Bench is the first large-scale video benchmark evaluatingthe full SVI stack: Perception (describing what happens), Reasoning (explaining why),Simulation (generating plausible alternatives), and Agency (autonomous analysis).

Skeleton-based Video Anomaly Detection (VAD) offers a ro-bust, privacy-preserving solution for identifying abnormal behaviors. Tomodel the distribution of normal static and moving poses, recent meth-ods train Energy-Based Models (EBMs) via Denoising Score Matching(DSM). However, directly injecting noise, required for training, into rawjoint coordinates creates physically impossible poses, and this structuralcollapse severely worsens as the temporal window expands. To addressthis, we introduce STEP, a simple framework that utilizes PrincipalComponent Analysis (PCA) to project pose sequences into a compact,whitened PC-space. Learning the data density within this well-behavedPC-space ensures that the injected noise translates into physically plau-sible variations, which allows the model to process longer video sequenceswithout the performance collapse of raw coordinate baselines. Addition-ally, to mitigate inherent pose estimation inaccuracies arising from occlu-sions or motion blur, we integrate a sequence-level weighting mechanismbased on the estimator’s confidence scores. Operating at real-time com-putational efficiency, our simple and lightweight framework outperformsthe previous skeleton-based state-of-the-art by 12.2% (90.1% AUROC)on the challenging UBnormal dataset and achieves highly competitiveresults by improving on the ShanghaiTech benchmark.

Multi-shot video generation is crucial for long narrative storytelling, yet current bidirectional architectures suffer from limited interactivity and high latency. We propose ShotStream, a novel causal multi-shot architecture that enables interactive storytelling and efficient on-the-fly frame generation. By reformulating the task as next-shot generation conditioned on historical context, ShotStream allows users to dynamically instruct ongoing narratives via streaming prompts. We achieve this by first fine-tuning a text-to-video model into a bidirectional nextshot generator, which is then distilled into a causal student via Distribution Matching Distillation. To overcome the challenges of inter-shot consistency and error accumulation inherent in autoregressive generation, we introduce two key innovations. First, a dual-cache memory mechanism preserves visual coherence: a global context cache retains conditional frames for inter-shot consistency, while a local context cache holds generated frames within the current shot for intra-shot consistency. And a RoPE discontinuity indicator is employed to explicitly distinguish the two caches to eliminate ambiguity. Second, to mitigate error accumulation, we propose a two-stage distillation strategy. This begins with intra-shot self-forcing conditioned on ground-truth historical shots and progressively extends to inter-shot self-forcing using self-generated histories, effectively bridging the train-test gap. Extensive experiments demonstrate that ShotStream generates coherent multi-shot videos with sub-second latency, achieving 16 FPS on a single GPU. It matches or exceeds the quality of slower bidirectional models, paving the way for real-time interactive storytelling. Code and models will be released.

Recent pre-trained text-to-image flow models have enabledremarkable progress in text-based image editing. Mainstream approachesadopt a corruption-then-restoration paradigm, where the source im-age is first corrupted into an editable “intermediate state” and then re-stored to the target image under the prompt guidance. However, currentmethods construct this intermediate state in a target-agnostic manner,i.e., they mainly focus on realizing source image reconstruction whileneglecting the semantic gaps towards the specific editing target. Thisdesign inherently results in limited editability or inconsistency when thedesired modifications substantially deviate from the source. In this pa-per, we argue that the intermediate state should be target-aware, i.e.,selectively corrupting editing-relevant contents while preserving editing-irrelevant ones. Thus, we propose FlowCycle, an inversion-free and flow-based editing framework that parameterizes corruption with learnablenoises and optimizes them through a cycle-consistent process. By itera-tively editing the source to the target and recovering back to the sourcewith dual consistency constraints, FlowCycle learns to produce a target-aware intermediate state, enabling faithful modifications while preservingsource consistency. For efficiency, we further accelerate the optimizationby dynamically adjusting the sampling steps. Extensive ablations demon-strated that FlowCycle achieves superior editing performance.