ECCV 2026 论文列表

Non-line-of-sight (NLOS) imaging aims to reconstruct hid-den scenes from time-resolved light transport, enabling vision around cor-ners. While classical physics-based methods provide principled inversion,learning-based approaches often rely on dense 3D backbones with cubiccomputational complexity. We propose a hybrid deep learning frame-work that combines a physics-guided initialization with a triplane-basedbackbone for high-fidelity volumetric reconstruction. The initialization isprovided by a learnable Enhanced Light-Cone Transform (ELCT), whichproduces a stable physics-consistent coarse volume, while the learnedbackbone replaces expensive O(N 3 ) 3D processing with scalable O(N 2 )triplane feature extraction. ConvNeXt-style residual convolutions, Restormerattention, and axis-aware cross-attention jointly refine structure and re-cover missing geometry. Experiments on synthetic and real NLOS datademonstrate improved reconstruction fidelity compared to representativephysics-based and learning-based baselines.

Visual Place Recognition (VPR) has been traditionally for-mulated as a single-image retrieval task. Using multiple views offers clearadvantages, yet this setting remains relatively underexplored and existingmethods often struggle to generalize across diverse environments. In thiswork, we introduce UniPR-3D, the first VPR architecture that effectivelyintegrates geometry-aware information from multiple views. UniPR-3Dbuilds on a VGGT backbone capable of encoding multi-view 3D represen-tations, which we adapt by designing feature aggregators and fine-tuningthem for the place recognition task. To construct our descriptor, wejointly leverage VGGT’s 3D tokens, but also intermediate 2D ones. Basedon their distinct characteristics, we design dedicated aggregation modulesfor 2D and 3D features, allowing our descriptor to capture fine-grainedtexture patterns while also reasoning across viewpoints. To further en-hance generalization, we incorporate both single- and multi-frame aggre-gation schemes, along with a variable-length sequence retrieval strategy.Our experiments show that UniPR-3D sets a new state of the art, out-performing both single- and multi-view baselines and highlighting theeffectiveness of geometry-grounded tokens for VPR. Our code and mod-els will be made publicly available on Github. We will release the codeand datasets publicly on https://github.com/dtc111111/UniPR-3D.

Recent 4D Gaussian Splatting (4DGS) methods achieve im-pressive dynamic scene reconstruction but often rely on piecewise linearvelocity approximations and short temporal windows. This disjointedmodeling leads to severe temporal fragmentation, forcing primitives tobe repeatedly eliminated and regenerated to track complex nonlinear dy-namics. This makeshift approximation eliminates the long-term temporalidentity of objects and causes an inevitable proliferation of Gaussians,hindering scalability to extended video sequences. To address this, wepropose TRiGS, a novel 4D representation that utilizes unified, contin-uous geometric transformations. By integrating SE(3) transformations,hierarchical Bézier residuals, and learnable local anchors, TRiGS mod-els geometrically consistent rigid motions for individual primitives. Thiscontinuous formulation preserves temporal identity and effectively mit-igates unbounded memory growth. Extensive experiments demonstratethat TRiGS achieves high fidelity rendering on standard benchmarkswhile uniquely scaling to extended video sequences (e.g., 600 to 1200frames) without severe memory bottlenecks, significantly outperformingprior works in temporal stability.

We introduce Spatial Amsan, a benchmark for evaluatingstate-based spatial reasoning and action evaluation in egocentric manip-ulation videos. The term amsan (Korean for mental arithmetic) reflectsthe core challenge: constructing and updating an internal spatial state toevaluate whether each manipulation advances toward a goal. The bench-mark comprises a tangram task (7 colored blocks) and a wooden puz-zle task (16 blocks), totaling 282 scenarios with 1,801 evaluation turns.Models are evaluated through a multi-turn protocol that diagnosticallyisolates four capabilities: visual perception, temporal tracking, spatialcontact reasoning, and goal-directed action evaluation. Our zero-shotevaluation of nine VLMs spanning frontier API models and open-weightlocal models reveals that contact reasoning is a universal bottleneck (12–16% on the wooden puzzle despite 53–96% on block identification) andthat action evaluation degrades from above 79% to below 16% over 8turns while perception remains stable. These results highlight a fun-damental gap between perceiving scene changes and reasoning abouttheir correctness over time. We release all data, annotations, and codeat https://github.com/Blanchard-lab/SpatialAmsan.

Different stages of manipulation tasks exhibit varying lev-els of difficulty, suggesting stage-dependent motion speeds and temporalprediction horizons. However, existing IL-based visuomotor policies typ-ically imitate the execution speed of expert demonstrations and operatewith a fixed temporal prediction horizon, limiting flexibility and over-all task throughput. In this paper, we introduce AutoSpeed, a model-agnostic learning framework that enables existing visuomotor policies topredict trajectories with stage-adaptive motion speeds, without requir-ing speed or stage annotations. We treat future trajectories at differentspeeds as candidate optimization targets, evaluate each candidate us-ing a composite cost that trades off prediction error against predictionhorizon, and optimize the policy toward the minimum-cost candidate.With a fixed-length action sequence, speed modulation adjusts the ef-fective temporal prediction horizon: simple stages are executed fasterwith a longer prediction horizon, whereas complex stages are executedmore slowly with a shorter prediction horizon. Specifically, we implementspeed modulation in the frequency domain via the discrete cosine trans-form (DCT), which enables smooth, non-integer speed scaling and thuspreserves motion continuity. Extensive evaluations show that AutoSpeedsubstantially reduces task execution time while also improving successrates. Under the AutoSpeed framework, the inferred motion speeds ex-hibit a strong correspondence with task stages.

Adversarial patch attacks that aim to fool deep neural network models in the real world prove effective in ‘every’ possible network type, including vision-language models (VLMs). Surprisingly, most of the existing adversarial patch generation algorithms develop patches of fixed shape (presumably rectangular or circular in a few instances). Due to such biases toward the shape of an adversarial patch, existing adversarial patch detectors, including VLMs, which yield high accuracy, provide a false sense of security. Our extensive experiments equivalently showcase the bias towards the fixed shape of adversarial patches and their high detection, which might leave a hole in adversarial patch defense against adaptive patches. In response, we have first proposed a novel deformable adversarial patch generation algorithm with multifold aims: (i) to highlight the vulnerabilities of state-of-the-art (SOTA) image classification algorithms and (ii) to help provide a strong foundation for developing a universal adversarial patch detector. Introducing smooth and deformable patches that significantly increase practical threats in real-world scenarios. Using the proposed algorithm, we developed large-scale adversarial patch detection datasets comprising 140,000 adversarial patch images for rigorous robustness evaluation and generalizable adversarial detection. The proposed deformable patches effectively fool several SOTA image classification models. While the defense is not the primary focus, our preliminary study highlights that detecting the proposed deformable patches is hard. Still, they can enhance security against various patch shapes, including fixed and deformable patches.

Contrastive pre-training has propelled video-text alignment, yet models often inherit the critical limitations of their image-text predecessors like CLIP, resulting in entangled representations. These challenges are severely exacerbated by two fundamental properties in the video domain: Temporal Misalignment, where textual descriptions often correlate only to specific, constrained temporal windows, leaving other frames text-irrelevant; and Semantic Asymmetry, which dictates a sparse, bidirectional, and non-equivalent relevance between frame-level visual details and caption-level concepts. This failure persists whether captions are short and temporally disjoint, creating ambiguity, or long and detailed, fostering entanglement between static objects and their temporal evolution. In this paper, we establish theoretical conditions that enable flexible alignment between video and text representations across the temporal dimension and at varying levels of granularity. Building on these theoretical insights, we introduce MoVA—Modular Long Video–Text Alignment—which learns dual asymmetric projections: a text-side projection that adaptively selects frame-aware subspaces of the caption, and a video-side projection that disentangles text-relevant visual concepts. Our framework ensures that the model can preserve global cross-modal semantics while disentangling evolving, frame-specific concepts and scale naturally to long captions and videos. Empirical evaluations show that MoVA outperforms existing methods in multiple video-text alignment tasks, demonstrating the effectiveness of our method.

What if a world simulation model could render not an imag-ined environment but a city that actually exists? Prior generative worldmodels synthesize visually plausible yet artificial environments by imag-ining all content. We present Seoul World Model (SWM), a city-scale world model grounded in the real city of Seoul. SWM anchors au-toregressive video generation through retrieval-augmented conditioningon nearby street-view images. However, this design introduces severalchallenges, including temporal misalignment between retrieved referencesand the dynamic target scene, limited trajectory diversity and data spar-sity from vehicle-mounted captures at sparse intervals. We address thesechallenges through cross-temporal pairing, a large-scale synthetic datasetenabling diverse camera trajectories, and a view interpolation pipelinethat synthesizes coherent training videos from sparse street-view images.We further introduce a Virtual Lookahead Sink to stabilize long-horizongeneration by continuously re-grounding each chunk to a retrieved im-age at a future location. We evaluate SWM against recent video worldmodels across three cities: Seoul, Busan, and Ann Arbor. SWM out-performs existing methods in generating spatially faithful, temporallyconsistent, long-horizon videos grounded in actual urban environmentsover trajectories reaching hundreds of meters, while supporting diversecamera movements and text-prompted scenario variations.

End-to-end (E2E) autonomous driving algorithms requirerigorous closed-loop validation in simulation environments offering highvisual fidelity, strong interactivity, and real-time performance. Exist-ing approaches, from game engines to static neural rendering, inher-ently trade off these requirements and struggle with the dynamic scenecomposition essential for E2E testing. To bridge this gap, we proposea novel decoupled 3D Gaussian Splatting (3DGS) framework tailoredfor large-scale E2E evaluation. We fundamentally decompose scenes intoa high-fidelity static background and manipulable dynamic agents us-ing an object-centric canonical representation. To resolve resulting rep-resentational conflicts, we introduce three targeted modules: (1) assetcompression via perceptual pruning and vector quantization for real-time traffic rendering; (2) map-guided geometric registration leveragingsemantic topology to strictly align trajectories; and (3) proxy-based re-lighting transferring ambient illumination for seamless photometric inte-gration. Extensive experiments demonstrate that DecoupleGS achievesa balanced fidelity-efficiency trade-off, improves metric and photomet-ric consistency, and provides a practical closed-loop sensor simulationplatform for E2E autonomous driving evaluation.

Portrait animation has witnessed tremendous quality im-provements thanks to recent advances in video diffusion models. How-ever, these 2D methods often compromise 3D consistency and speed, lim-iting their applicability in real-world scenarios, such as digital twins ortelepresence. In contrast, 3D-aware feedforward facial animation methods– built upon 3D representations, such as neural radiance fields or Gaus-sian splatting – ensure 3D consistency and achieve faster inference speed,but come with inferior expression details. In this paper, we address thisportrait animation trilemma (speed, 3D consistency, and expressiveness)and propose a pipeline that instantly converts an in-the-wild single im-age into a 3D-consistent, fast yet expressive animatable representationvia a feed-forward encoder. Unlike previous computationally intensiveglobal fusion mechanisms (e.g., multiple attention layers) for fusing 3Dstructural and animation information, our design employs an efficientlightweight local fusion strategy to achieve high animation expressivity.Furthermore, our animation representation is decoupled from the face’s3D representation and learns motion implicitly from data, eliminatingthe dependency on pre-defined parametric models that often constrainanimation capabilities. Our method runs at 107.31 FPS for animationand pose control, representing a 3-4 order of magnitude speedup versusthe state of the art while achieving comparable animation quality, thussurpassing alternative designs that trade speed for quality or vice versa.

Test-Time Adaptation (TTA) methods commonly updatethe affine parameters of normalization layers to adapt deployed modelsunder distribution shifts. However, per-channel affine parameters per-form axis-aligned scaling and shifting, making them geometrically inca-pable of correcting cross-channel structural changes induced by distribu-tion shift. To address this limitation, we propose MixTTA, a lightweightplug-in module that equips normalization layers with a low-rank cross-channel transformation, enabling inter-channel mixing at each layer. Toensure that the low-rank branch captures only cross-channel interac-tions, we also propose Decoupling Projection that enforces strict separa-tion from the diagonal affine path, along with Spectral Projection thatprevents rank-1 collapse under non-stationary test streams. MixTTAcan be seamlessly integrated into any existing normalization-based TTAmethod. Experiments in both standard and wild TTA settings show con-sistent improvements over strong baselines while mitigating adaptationfailure under challenging conditions. The source code is publicly availableat https://github.com/delta6189/MixTTA.

Amodal segmentation aims to recover the full shape of objects that are partially occluded; however, most existing approaches are trained and assessed on in-domain benchmarks and exhibit limited generalization in zero-shot scenarios. Human-intuition-guided depth-aware (HIDA) is proposed as a plug-and-play framework that combines two frozen vision foundation models with a lightweight trainable segmentation network to estimate amodal masks using only a visible bounding box. HIDA initially derives a visible prior mask and a pseudo-depth map (PDM) to enable target-centered cropping, which reduces irrelevant context while highlighting occlusion boundaries and hidden regions. An occlusion-aware token-routing soft MoE module is also introduced within a lightweight ViT encoder to adaptively handle varied occlusion patterns and strengthen representations of invisible regions. Evaluations on COCOA-cls, KINS, and D2SA demonstrate that HIDA attains state-of-the-art performance in fully supervised settings and state-of-the-art zero-shot results on COCOA-cls and D2SA while training only a lightweight segmentation network. Code is available at: https://github.com/lipenghuai/HIDA.

Identifying and grounding precise geometric entities, such as edges, planar regions, and curved surfaces within 3D objects, is foundational to computer-aided design (CAD), robotic manipulation, and scientific simulation. Although modern Vision Language Models (VLMs) have advanced referring segmentation (RIS) in the image domain, extending such language-driven localization to structured 3D geometry is substantially harder. The 3D object appearance is highly sensitive to viewpoints; a single perspective may render a target entity clearly observable, while another may suffer from severe occlusion or foreshortening. In this work, we attempt to solve these challenges with MV-GEL (Multi-View Geometric Entity Localization), a framework for localizing fine-grained geometric entities on polygon meshes from natural language queries. Our key insight is that reliable CAD entity (i.e., faces, edges or solids) localization depends on selecting views that make the queried entity maximally interpretable. We introduce GELviews, a prompt-conditioned ranking module that prioritizes viewpoints based on language prompted observability of geometric CAD entities. Selected views are processed by a VLM-based reasoning segmentation backbone, and predicted masks are lifted to the corresponding meshes via geometry-aware ray casting. Our framework is completely CAD agnostic and relies only on 3D meshes. Experiments show up to a 1.7× improvement in face-level IoU and over 4.5× gains in edge-level F1 compared to vanilla baselines, substantially outperforming CLIP-based and random view sampling, particularly for thin and view-sensitive structures. The dataset, code and trained checkpoints are available at https://github.com/kbali1297/MV-GEL.

Recent world-model-based Vision-Language-Action (VLA)architectures have improved robotic manipulation through predictive vi-sual foresight. However, dense future prediction introduces visual redun-dancy and accumulates errors, causing long-horizon plan drift. Mean-while, recent sparse methods typically represent visual foresight usinghigh-level semantic subtasks or implicit latent states. These representa-tions often lack explicit kinematic grounding, weakening the alignmentbetween planning and low-level execution. To address this, we proposeStructVLA, which reformulates a generative world model into an explicitstructured planner for reliable control. Instead of dense rollouts or seman-tic goals, StructVLA predicts sparse, physically meaningful structuredframes. Derived from intrinsic kinematic cues (e.g., gripper transitionsand kinematic turning points), these frames capture spatiotemporal mile-stones closely aligned with task progress. We implement this approachthrough a two-stage training paradigm with a unified discrete token vo-cabulary: the world model is first trained to predict structured frames andsubsequently optimized to map the structured foresight into low-level ac-tions. This approach provides clear physical guidance and bridges visualplanning and motion control. In our experiments, StructVLA achievesstrong average success rates of 75.0% on SimplerEnv-WidowX and 94.8%on LIBERO. Real-world deployments further demonstrate reliable taskcompletion and robust generalization across both basic pick-and-placeand complex long-horizon tasks.

Embodied Instruction Following requires agents to executenatural-language tasks via holistic perception and sound planning. Cur-rent LLM-driven agents suffer from two primary bottlenecks: SpatialMyopia, where limited egocentric views hinder global topological under-standing, and Granularity Imbalance, where planning is polarized be-tween inefficient atomic actions that lack interpretability and ambiguoussubgoals that fail to align with low-level control. To address these is-sues, we propose STEP, a framework that empowers agents with SpatialThinking and Egocentric Pointing. For perception, STEP integrates Hy-brid Map-Egocentric Perception, fusing multi-view streams into Bird’s-Eye-View maps to maintain both global context and local visual cues.For planning, STEP introduces Point-Level Planning, which derives spa-tial waypoints through explicit reasoning chains, achieving highly inter-pretable, intermediate-granularity execution. We also design STEP-CoT,a scalable data engine, to generate a 400k Chain-of-Thought datasetthat aligns diverse modalities—views, maps, actions, and instructions—through reasoning chains. Results on ALFRED and AI2-THOR-Nav,along with real-world deployments, demonstrate STEP’s superior perfor-mance and generalization. Ultimately, STEP achieves a unified paradigm:Look at the View, Think with the Map, and Point to the Goal.

Diffusion models produce high-quality images, but their de-ployment remains expensive because generation requires many denoisingsteps through large transformer or U-Net backbones. Existing methodsimprove efficiency through post-training quantization or timestep reduc-tion, but they optimize these two axes separately. Quantization methodsusually assume a fixed precision, timestep reduction methods assume afixed schedule, and both often rely on proxy costs such as BitOps insteadof real hardware measurements. We introduce DiffPro, a post-trainingframework that jointly optimizes per-layer quantization precision and thedenoising schedule under a unified budget defined by measured memoryand latency on real hardware. DiffPro combines three key ideas: (i) acomposite sensitivity metric that combines PCA dimensionality withdiagonal Hessian curvature for bit allocation, (ii) Dynamic ActivationQuantization (DAQ), which adapts activation scales across timesteps tohandle temporal activation drift, and (iii) a drift-guided timestep selectorthat removes redundant steps while preserving the late refinement phase.On DiT-XL/2 (ImageNet 256 × 256), DiffPro achieves 50% step reduction,7.9× compression, and 2.7× speedup at FID 5.89 (∆FID 3.62), improvingover uniform thinning by 2.5 FID at matched precision. On SDXL U-Net(COCO 2014 1024 × 1024), DiffPro achieves 5.6× compression with FID26.50 (∆FID 2.0) and 45% energy reduction, all without retraining.

Multi-view visual reasoning is essential for intelligent systems that must understand complex environments from sparse and discrete viewpoints, yet existing research has largely focused on single-image or temporally dense video settings. In real-world scenarios, reasoning across views requires integrating partial observations without explicit guidance, while collecting large-scale multi-view data with accurate geometric and semantic annotations remains challenging. To address this gap, we leverage physically grounded simulation to construct diverse, high-fidelity 3D scenes with precise per-view metadata, enabling scalable data generation that remains transferable to real-world settings. Based on this engine, we introduce VIEW2SPACE, a multi-dimensional benchmark for sparse multi-view reasoning, together with a scalable, disjoint training split supporting millions of grounded question–answer pairs. Using this benchmark, a comprehensive evaluation of state-of-theart vision–language and spatial models reveals that multi-view reasoning remains largely unsolved, with most models performing only marginally above random guessing. We further investigate whether training can bridge this gap. Our proposed Grounded Chain-of-Thought with Visual Evidence substantially improves performance under moderate difficulty, and generalizes to real-world data, outperforming existing approaches in cross-dataset evaluation. We further conduct difficulty-aware scaling analyses across model size, data scale, reasoning depth, and visibility constraints, indicating that while geometric perception can benefit from scaling under sufficient visibility, deep compositional reasoning across sparse views remains a fundamental challenge. Code and benchmark are available online3.

While 3D Gaussian Splatting (3DGS) provides an efficientand explicit representation for novel view synthesis, enforcing stylisticcoherence across viewpoints remains challenging. Existing 3D stylizationmethods typically apply 2D feature-matching losses independently perrendered view, which leads to unstable style allocation, many-to-one fea-ture reuse, and limited cross-view consistency. We propose a capacity-controlled framework for multi-view stylization of 3DGS, grounded inoptimal transport. Specifically, we reformulate local style matching as asemi-balanced optimal transport problem. By introducing explicit column-capacity constraints with tunable strength, our formulation mitigatesmany-to-one matching and enables controllable allocation of style fea-tures. This transport-based objective provides a principled mechanismfor balancing feature coverage and stylistic diversity while maintainingstable correspondences across viewpoints. To further enhance cross-viewcoherence, we incorporate a novel cross-view matching guidance to con-strain correspondences between scene content and style patterns. In ad-dition, we introduce several geometric regularizations to enhance thevanilla 3DGS, thereby enabling optimized Gaussian primitives to rep-resent finer-grained textures during stylization. Extensive experimentsdemonstrate that our approach significantly improves multi-view stylis-tic consistency and produces stable, expressive 3D stylizations while pre-serving the core semantic structure of the scene.

Recent progress in multimodal reasoning has enabled agentsthat interpret imagery, connect it with language, and execute struc-tured analytical tasks. Extending these capabilities to remote sensingremains challenging, as models must reason over spatial scale, geographicstructures, and multispectral indices while maintaining coherent multi-step logic. To address this gap, we introduce OpenEarthAgent, a unifiedframework for tool-augmented geospatial reasoning trained on satelliteimagery, natural-language queries, and structured reasoning traces. Be-yond serving as a benchmark, OpenEarthAgent establishes a cohesiveagentic architecture built around a unified executable tool registry andtrajectory-based policy learning. The framework standardizes heteroge-neous visual, spectral, GIS, and georeferenced raster operations undera consistent callable schema, enabling modular orchestration and deter-ministic execution. Training is performed via supervised fine-tuning onstructured reasoning trajectories with deterministic replay validation toensure executability and spatial correctness. The accompanying corpuscomprises 14,538 training and 1,169 evaluation instances with over 107Kreasoning steps, spanning urban, environmental, disaster, and infrastruc-ture domains and incorporating GIS operations alongside index analysessuch as NDVI, NBR, and NDBI. Grounded in explicit reasoning traces,the learned agent demonstrates structured reasoning, stable spatial un-derstanding, and interpretable tool-driven behavior across diverse EOscenarios. We report consistent improvements over a strong baseline andcompetitive performance against recent open and closed-source models.https://github.com/mbzuai-oryx/OpenEarthAgent.