ECCV 2026 Papers

Diffusion Transformers (DiTs) are a dominant backbone forhigh-fidelity text-to-image generation due to strong scalability and align-ment at high resolutions. However, quadratic self-attention over densespatial tokens leads to high inference latency and limits deployment. Weobserve that denoising is spatially non-uniform with respect to aestheticdescriptors in the prompt. Regions associated with aesthetic tokens re-ceive concentrated cross-attention and show larger temporal variation,while low-affinity regions evolve smoothly with redundant computation.Based on this insight, we propose AccelAes, a training-free frameworkthat accelerates DiTs through aesthetics-aware spatio-temporal reduc-tion while improving perceptual aesthetics. AccelAes builds AesMask, aone-shot aesthetic focus mask derived from prompt semantics and cross-attention signals. When localized computation is feasible, SkipSparsereallocates computation and guidance to masked regions. We further re-duce temporal redundancy using a lightweight step-level prediction cachethat periodically replaces full Transformer evaluations. Experiments onrepresentative DiT families show consistent acceleration and improvedaesthetics-oriented quality. On Lumina-Next, AccelAes achieves a 2.11×speedup and improves ImageReward by +11.9% over the dense baseline.Code is available at https://github.com/xuanhuayin/AccelAes.

3D Foundation Models (3DFMs) such as VGGT have re-cently pushed the boundaries of 3D vision by predicting rich unifiedrepresentations with feed-foward transformers. The scene representationslearned by these models enable strong performance on multiple 3D visiontasks. In this paper, we investigate using their internal representationsto infer 3D in the scene from new views. Our hypothesis is that in or-der to solve the task of 3D reconstruction, these models need to learn arepresentation that includes a large amount of general knowledge about3D scenes. After showing that it is possible to decode hidden surfacesfrom internal 3DFM representations, we propose a method, Z3D, thatestimates pointmaps in unseen views by doing latent diffusion on 3DFMrepresentation. We show that Z3D can predict realistic depthmaps fornew views across multiple datasets.

While current 6D pose estimation benchmarks have reached near-saturation on household objects, they often fail to capture the stochastic and optical complexities of industrial environments. We introduce XYZ-IBD, a high-precision benchmark for object detection and 6D pose estimation specifically designed for industrial bin-picking. XYZ-IBD addresses the domain gap by providing 75 multi-view real-world scenes containing approximately 273k annotated instances of metallic, symmetrical, and specular objects. Unlike existing datasets, our benchmark features high-density stochastic stacking and multi-instance ambiguity, reflecting authentic robotic manipulation challenges. We employ a rigorous multistage and semi-automatic annotation pipeline, ensuring sub-millimeter annotation accuracy. The annotations are validated through our designed error quantification scheme, securing the reliability of the annotation quality. In addition to real-world evaluation data, we provide a large-scale complementary synthetic training set that is rendered under realistic bin-picking simulation. Benchmarking state-of-the-art (SOTA) methods for 2D detection and 6D pose estimation reveals a significant performance degradation compared to standard household benchmarks, highlighting the unsolved challenges of industrial vision. XYZ-IBD establishes a new frontier for robust pose estimation in complex, high-occlusion, and reflective scenarios. The dataset and benchmark are publicly available at https://xyz-ibd.github.io.

Large-scale language models for autonomous driving enableenhanced global understanding and long-horizon planning. However, whendeployed in isolated vehicles, limited sensing range and occlusions re-strict reliable decision-making, and the substantial computational andlatency overhead makes on-board deployment impractical. Cooperativedriving provides a potential solution by leveraging external agents forinformation exchange, but existing methods remain limited in semanticreasoning capability under practical constraints. To address these chal-lenges, we propose DH-VLM, a dual-horizon cooperative latent reasoningframework that enables asymmetric semantic cooperation between theinfrastructure and ego vehicle. The infrastructure aggregates multi-layerhidden states to form a global-reasoning horizon latent guidance, whichis integrated into the ego model through an Infrastructure-Driven LatentEvolution mechanism for conditional latent refinement. This enables theego vehicle to leverage long-range contextual understanding while pre-serving autonomous decision-making within its local planning horizon.Furthermore, we construct a cooperation-oriented question–answer (QA)dataset covering fundamental scene understanding and ego-personalizedcomprehension to support counterfactual and safety-aware reasoning.Extensive experiments demonstrate that DH-VLM achieves state-of-the-art planning performance, outperforming the previous state of the art by14.6% in L2 error and 26.9% in collision rate. Compared with query-basedend-to-end cooperative driving methods, our approach reduces the com-munication cost by 57.3% and GPU memory usage by 25.5%, while main-taining strong robustness against infrastructure guidance errors, provid-ing a practical and robust paradigm for cooperative autonomous driving.

Diffusion Transformers (DiTs) power high-fidelity video worldmodels but remain computationally expensive due to sequential denois-ing and costly spatio-temporal attention. Training-free feature cachingaccelerates inference by reusing intermediate activations across denois-ing steps; however, existing methods largely rely on a Zero-Order Holdassumption i.e., reusing cached features as static snapshots when globaldrift is small. This often leads to ghosting artifacts, blur, and motion in-consistencies in dynamic scenes. We propose WorldCache, a Perception-Constrained Dynamical Caching framework that improves both whenand how to reuse features. WorldCache introduces motion-adaptive thresh-olds, saliency-weighted drift estimation, optimal approximation via blend-ing and warping, and phase-aware threshold scheduling across diffusionsteps. Our cohesive approach enables adaptive, motion-consistent featurereuse without retraining. On Cosmos-Predict2.5-2B evaluated on PAI-Bench, WorldCache achieves 2.3× inference speedup while preserving99.4% of baseline quality, substantially outperforming prior training-free caching approaches.

Large-scale text-to-image di_x001B_usion models have become thebackbone of modern image editing, yet text prompts alone do not o_x001B_eradequate control over the editing process. Two properties are especiallydesirable: disentanglement, where changing one attribute does not un-intentionally alter others, and continuous control, where the strengthof an edit can be smoothly adjusted. We introduce a method for dis-entangled and continuous editing through token-level manipulation oftext embeddings. The edits are applied by manipulating the embeddingsalong carefully chosen directions, which control the strength of the tar-get attribute. To identify such directions, we employ a Sparse Autoen-coder (SAE), whose sparse latent space exposes semantically isolateddimensions. Our method operates directly on text embeddings withoutmodifying the di_x001B_usion process, making it model agnostic and broadlyapplicable to various image synthesis backbones. Experiments show thatit enables intuitive and e_x001E_cient manipulations with continuous controlacross diverse attributes and domains.

Latent world models roll observations forward accurately onfactual transitions, but their predictive latents are typically entangledand weakly aligned with the underlying causal factors; as a result, theydegrade under interventions and on counterfactual queries. We addressthis with CausalVAE, a plug-in structural module that attaches to di-verse encoder–transition backbones and organizes their latents througha learned directed acyclic graph (DAG) over causal factors, withoutaltering the base prediction architecture. Across four benchmarks andeight backbones, the plug-in largely preserves factual retrieval while im-proving intervention-aware counterfactual retrieval on most backbone–benchmark pairs; the gains are benchmark- and backbone-dependentrather than universal. Improvements are largest on the Physics bench-mark: for a graph-network backbone trained with a negative-log-likelihoodobjective, counterfactual H@1 (CF-H@1) rises from 11.0 to 41.0, withseveral other backbones gaining +10 to +30 CF-H@1 points. A causalanalysis further shows that the learned structure recovers meaningfulfirst-order physical interaction trends, supporting the interpretability ofthe latent causal structure.

Imitation learning enables robots to acquire complex manipulation skills from expert demonstrations, but offline-trained policies struggle to generalize beyond their training distribution, where small errors compound into severe distribution shift. Dataset Aggregation (DAgger) mitigates this by collecting human corrections during deployment, yet conventional DAgger operates entirely in the real world with a oneto-one ratio between expert effort and training trajectories makes comprehensive out-of-distribution (OOD) coverage impractical. Digital Twin (DT) techniques can scale data generation, but when applied to DAgger data without modification, they ignore the spatial and temporal failure patterns that DAgger rollouts reveal, leaving policies vulnerable precisely where and when they fail most. We propose Twin-DAgger, which treats each real-world intervention as simultaneously a spatial probe identifying failure-prone workspace regions and a temporal prior marking critical deviation moments. Twin-DAgger exploits these signals through two targeted augmentation mechanisms: difficulty-based stratified sampling concentrates synthetic data generation on high-failure-rate regions, and intervention-focused trajectory warping perturbs trajectories at each intervention onset to simulate a distribution of near-failure states while preserving the expert’s corrective recovery. On real-world manipulation tasks, Twin-DAgger matches conventional DAgger performance using only 15% human interventions and outperforms failure-agnostic DT baselines by 20% success rate under equal data budgets, demonstrating that generating data where and when the policy fails, rather than generating more data uniformly, is the critical driver of sample-efficient robustness.

Learning efficient and expressive visual representation haslong been the pursuit of computer vision research. While Vision Trans-formers (ViTs) gradually replace traditional Convolutional Neural Net-works (CNNs) as more scalable vision learners, their applications areplagued by the quadratic complexity of the self-attention mechanism. Toaddress the challenge, we introduce a new linear-time sequence modelingmethod Test-Time Training (TTT) into vision and propose Vision-TTT,which treats visual sequences as datasets and compresses the visual to-ken sequences in a novel self-supervised learning manner. By incorporat-ing the dual-dataset strategy and Conv2d-based dataset preprocessing,Vision-TTT effectively extends vanilla TTT to model 2D visual cor-relations with global receptive fields. Extensive experiments show thatVittt-T/S/B achieve 77.7%, 81.8%, 82.7% Top-1 accuracy on ImageNetclassification and also greatly outperform their counterparts on down-stream tasks. At 1280 × 1280 resolution, Vittt-T reduces FLOPs by79.4% and runs 4.72× faster with 88.9% less memory than DeiT-T.These results demonstrate the expressiveness and efficiency of Vision-TTT as an alternative for the next-generation visual backbone. Codesare available at https://github.com/imKQv/Vision-TTT.

Image-goal navigation is a fundamental capability for em-bodied AI, yet its practical deployment is strained by strong prior as-sumptions. Existing methods predominantly rely on temporally orderedvideo streams or auxiliary sensors (e.g., depth, LiDAR) to maintain spa-tial consistency. These sequential and multimodal dependencies severelyrestrict scalability, especially when deploying robots using crowd-sourcedor pre-recorded unordered image collections. When temporal priors areremoved, current methods struggle with severe perceptual aliasing, noisyassociations, and catastrophic mapping failures. To address this under-explored challenge, we propose Unordered Landmark Visual Navigation(ULVN), a unified RGB-only framework free from temporal and odo-metric priors. ULVN systematically mitigates error accumulation by in-tegrating mapping, localization, and planning. Specifically, it constructsa robust 2D topological map directly from unstructured images via cali-brated geometric verification and maximum spanning forest refinement.For closed-loop execution, ULVN abandons sequential heuristics, utiliz-ing a graph-based belief propagation filter with entropy-adaptive fusionfor global localization and dynamic subgoal planning. Extensive experi-ments in simulation and real-world deployments demonstrate that ULVNsignificantly outperforms state-of-the-art methods.

Collaborative perception serves as a pivotal solution to en-hance the perception capability of individual agents in autonomous driv-ing, where a core challenge lies in seeking reliable evidence to quantifyand weight the contribution of each participating agent. Existing meth-ods typically rely on a confidence map (co-trained with the detectionhead), which is, however, inherently correlated with the detection re-sults and thus fails to provide unbiased physical evidence. Furthermore,how to deeply integrate evidence into the cooperative fusion process re-mains an open question. To address these issues, this paper first pro-poses uncertainty map, a physically grounded and unambiguous met-ric for evaluating perception quality. This map is directly supervisedby real-time sensor signals (i.e., LiDAR point density), ensuring de-coupling from detection noise and thereby providing physical scenario-aware evidence for weighting agent contribution. Based on this map,we develop the Uncertainty-Enhanced Collaborative Perception (UECP)framework, centered on the Uncertainty-Aware Pyramid Fusion (UAPF)module. UAPF uses a coarse-to-fine strategy, with two key components:Uncertainty-Weighted Downsampling (UWD) for high-fidelity featurepreservation, and Uncertainty-Guided Residual Fusion (UGRF) to rein-force ego features, suppressing noise and ensuring robust fusion. Exten-sive experiments on real-world datasets show UECP outperforms SOTAmethods in effectiveness and robustness by embedding the uncertaintymap into fusion. Code will be publicly available.

Despite remarkable advancements in Vision-and-LanguageNavigation (VLN), a significant sim-to-real gap remains. Most meth-ods are trained and evaluated in simplified or physics-limited simulators,which ignore the feedback loop between decision making and physical ex-ecution, resulting in unstable navigation performance. To bridge this gap,we propose Hi-Nav, a top-down hierarchical navigation framework thatdecomposes VLN into three controllable levels. The high-level moduleleverages a large language model to split long-horizon instructions intosub-tasks. At the middle level, an Interest Score Occupancy Map (ISOM)integrates instruction-relevant semantics with geometric traversability toperceive the surrounding environment, after which waypoint reasoning isperformed for route refinement. ISOM construction and waypoint reason-ing are jointly modeled within a unified multi-task learning framework,where a Q-Former fuses RGB-D observations and navigation instructionsinto shared query representations for efficient multi-task learning, aug-mented with learnable temporal embeddings to mitigate long-horizon for-getting. Finally, low-level behaviors are executed through ROS, enablingobstacle avoidance. We introduce a novel waypoint reasoning datasetfor comprehensive training and build a ROS/Gazebo VLN benchmarkto evaluate methods in a realistic simulation setting, which significantlyreduces the difficulty of sim-to-real transfer. Extensive experiments con-ducted in both Gazebo and the real world demonstrate strong perfor-mance, achieving up to 80% Success Rate (SR) with 0.65 m NavigationError (NE). Our code will be released at https://hiway-page.github.io/.

Monitoring canopy height change is essential for understand-ing carbon sinks and forest dynamics. Remote sensing enables consis-tent, large-scale observations of such changes, increasingly integratedwith deep learning architectures such as Geospatial Foundation Models(GFMs). However, existing methods and datasets frame the problem asbinary change detection, which overlooks both the continuous nature ofchange, especially for vegetation, and the inherent uncertainty in labels.We present the Canopy Height Change (CHC) dataset, providing 3 mresolution continuous canopy height differences and associated spatiallyresolved uncertainties across 10 598 km2 of northern and western Spain.The dataset is paired with a co-located time series of PlanetScope satel-lite imagery. Based on the dataset, we introduce the task of uncertainty-aware change regression, associated metrics and strategies for fine-tuningGFMs. Furthermore, we evaluate state-of-the-art GFMs and highlightpromising directions and remaining challenges for advancing continuouscanopy height change estimation.

We present a novel implementation of Visual SimultaneousLocalisation and Mapping (VSLAM), whose computation is performedcollaboratively between a sensor-based front-end utilizing efficient in-pixel compute, and a PC-based back-end that sends pose estimationback to the sensor to improve feature matching. The front-end performsthe tasks of point-feature detection and tracking at up to 2000 Hz, whilethe PC back-end conducts the less immediate, higher level tasks, of map-ping and localisation at ≈ 200 Hz. This division of tasks is somewhatreminiscent of biological vision, in which the retina performs low levelcomputations, while the brain handles slower integrative functions. Onlysparse data is exchanged between these two ends, such as feature loca-tions, descriptor data, and sensor pose estimations. Essentially, each sideonly sends data which the other side requires to perform its respectivetask. This is a radical departure from the traditional visual pipeline,where dense image frames are streamed from sensor to PC. By compar-ison, our collaborative compute pipeline is extremely efficient, demon-strating a 10× improvement to speed and latency, > 1000× bandwidthand ∼ 20× reduction in power consumption per processed frame.

In this work, we introduce FSU-QA, a VQA dataset for au-tonomous driving scenarios designed to advance research on ForesightIntelligence—the ability to anticipate and reason about complex, long-horizon futures. Unlike existing benchmarks that mainly focus on imme-diate perception or reactive planning, FSU-QA evaluates future-orienteddriving understanding through multi-agent-aware and rule-grounded coun-terfactual QA. Rather than holding surrounding agents fixed or target-ing geometric path generation, our benchmark requires models to infersemantic future outcomes from front-view historical observations andpast ego trajectories. A comprehensive evaluation on the accompanyingFSU-Bench reveals that state-of-the-art VLMs still face significant chal-lenges in anticipating future events. Furthermore, beyond model perfor-mance, we examine whether WM-generated predictions remain seman-tically consistent by using VLM-based proxy judges, and validate thisevaluation protocol through shuffled control experiments. Fine-tuningmodels on FSU-QA leads to substantial improvements in foresight under-standing, demonstrating the dataset’s effectiveness and offering a prin-cipled foundation for future research.

Humans paint images incrementally: they plan a global lay-out, sketch a coarse draft, inspect, and refine details, and most impor-tantly, each step is grounded in the evolving visual states. However, canunified multimodal models trained on text-image interleaved datasetsalso imagine the chain of intermediate states? In this paper, we introduceprocess-driven image generation, a multi-step paradigm that decom-poses synthesis into an interleaved reasoning trajectory of thoughtsand actions. Rather than generating images in a single step, our ap-proach unfolds across multiple iterations, each consisting of 4 stages: tex-tual planning, visual drafting, textual reflection, and visual refinement.The textual reasoning explicitly conditions how the visual state shouldevolve, while the generated visual intermediate in turn constrains andgrounds the next round of textual reasoning. A core challenge of process-driven generation stems from the ambiguity of intermediate states: howcan models evaluate each partially-complete image? We address thisthrough dense, step-wise supervision that maintains two complementaryconstraints: for the visual intermediate states, we enforce the spatial andsemantic consistency; for the textual intermediate states, we preserve theprior visual knowledge while enabling the model to identify and correctprompt-violating elements. This makes the generation process explicit,interpretable, and directly supervisable. To validate proposed method,we conduct experiments under various text-to-image benchmarks.

Real-world intelligent systems often require both distributedcollaboration across data-isolated clients and continual adaptation toevolving tasks. This setting naturally gives rise to Federated Class In-cremental Learning (FCIL), which combines Federated Learning (FL)and Continual Learning (CL). However, their combination introducestwo coupled sources of interference: spatial interference from heteroge-neous clients and temporal interference from sequential tasks, jointlyleading to Spatial_x0015_Temporal Catastrophic Forgetting (ST-CF). Exist-ing approaches typically address spatial and temporal interference withseparate mechanisms, often incurring additional client-side computationor communication, while leaving directional interactions among updatesduring aggregation unregulated. In this paper, we reinterpret FCIL as auni_x001C_ed multi-task learning problem, where both client and task updatesare represented as adaptation vectors in a shared parameter space. Basedon this view, we proposeSurgery & Merge Sum ( ), a purely server-sideframework that performs geometric surgery on adaptation vectors duringaggregation. Spatial Sum mitigates client-level interference within eachSumround, while causal online temporal removes cross-task interferenceover time without additional client-side computation, communication, ormemory beyond standard federated training. Empirically, Sum achievesup to 22% improvement over prior FCIL methods across diverse visionand language benchmarks while remaining robust to unreliable clientsand maintaining computational e_x001E_ciency.

Generative models have achieved remarkable progress, yetapplying them to satellite imagery remains challenging. Unlike natu-ral imagery, satellite scenes are structured by spatially complex andsemantically distinct geometries. Prior work addresses this complexityby adapting natural image frameworks using dense rasters or sparseprompts, trading off annotation cost and fidelity while breaking com-patibility with vector primitives commonly used to represent geographicinformation. We introduce TerraDiT-Ω, a unified spatial control frame-work that generates satellite imagery directly from any native geospatialprimitive. By jointly leveraging precise annotations (polygons, polylines)and coarser ones (bounding boxes, points), the model supports control-lable layouts across varying annotation budgets, broadening applicabilityto design tasks such as urban planning while remaining naturally com-patible with end-to-end GeoAI workflows. To effectively leverage theseprimitives during generation, we propose Geometry-Aware Local Atten-tion, a conditioning mechanism that injects explicit geometric cues intothe attention space. Across all conditioning formats, our approach con-sistently outperforms both dense-control and sparse-control baselines.Furthermore, this flexibility enables controllable synthetic data augmen-tation using a single generative model, improving downstream perfor-mance on land-cover segmentation, object detection, road graph extrac-tion, and scene classification. Code, data, and weights are available at

Autoregressive multimodal large language models (MLLMs)enable 3D generation but struggle to scale to high-resolution shapesdue to inadequate 3D tokenizations. Compact set-based representationsdiscard deterministic spatial ordering, leading to ambiguous sequenceprediction, while uniform or octree-based voxel grids preserve orderingat the cost of severe redundancy and excessively long sequences. Thisstructural trade-off limits stable and efficient autoregressive 3D gener-ation. We present SuperVoxelGPT, a representation-first frameworkthat resolves this tension through adaptive and deterministically orderedsupervoxel tokenization. Given a prompt, we first predict a coarse geo-metric saliency distribution and construct a shape-adaptive supervoxelpartition using saliency-guided centroidal Voronoi tessellation, allocatingfine-grained cells to complex regions and larger cells to smooth regions.Conditioned on this prompt and ordered supervoxel layout, we introducea SuperVoxelVAE and fine-tune a pretrained MLLM to autoregressivelygenerate supervoxel tokens. Experiments using Trellis-500K data showthat SuperVoxelGPT reduces token sequence length to 12.8% of uniformvoxel tokenization while achieving state-of-the-art generation quality andan average 10× speedup over prior methods.