ECCV 2026 论文列表

Generative flow and diffusion models provide the continu-ous, multimodal action distributions needed for high-precision roboticpolicies. However, their reliance on iterative sampling introduces severeinference latency, degrading control frequency and harming performancein time-sensitive manipulation. To address this problem, we proposethe One-Step Flow Policy (OFP), a from-scratch self-distillation frame-work for high-fidelity, single-step action generation without a pre-trainedteacher. OFP unifies a self-consistency loss to enforce coherent transportacross time intervals, and a self-guided regularization to sharpen predic-tions toward high-density expert modes. In addition, a warm-start mech-anism leverages temporal action correlations to minimize the generativetransport distance. Evaluations across 56 diverse simulated manipulationtasks demonstrate that a one-step OFP achieves state-of-the-art results,outperforming 100-step diffusion and flow policies while accelerating ac-tion generation by over 100×. We further integrate OFP into the π0.5model on RoboTwin 2.0, where one-step OFP surpasses the original 10-step policy. These results establish OFP as a practical, scalable solutionfor highly accurate and low-latency robot control.

We present SynCity 3000, a framework for generating 3Dscenes that are globally coherent while enabling fine-grained layout con-trol. Building on the ability of current image-to-3D generators to pro-duce complex 3D assets from a single image, we extend this capabilityto the scale of entire scenes by adapting the generator to be applicableas a convolutional operator. We achieve this by fine-tuning the modelon scene-like data generated by a new synthetic data engine, which wepropose to address the scarcity of 3D scene data for training. The convo-lutional generator is then applied to a dimetric image of the entire scene,generated from the user prompt, resulting in 3D scenes of arbitrary sizeand complexity. Across diverse prompts and layouts, SynCity 3000 pro-duces large, coherent, and detailed scenes, addressing the shortcomingsof prior approaches to 3D scene generation.

Autoregressive diffusion models have shown remarkable prog-ress in long video generation by producing frames causally. To boundmemory and computational cost, these models rely on a fixed-size sliding-window KV cache. While attention sinks retain a few initial frames astemporal anchors, the vast majority of historical context is irrecover-ably discarded as the window advances, severing long-range dependen-cies essential for scene consistency and narrative coherence. Meanwhile,the fixed multi-step denoising schedule applies uniform effort to everychunk, ignoring the varying demands imposed by diverse scene dynamicsacross temporal segments. In this paper, we propose Surprise Forcing,a training-free framework that addresses both bottlenecks. We introducea Surprise-Gated Memory Bank that scores each evicted frame by† Corresponding authors.its aggregate divergence from the bank and nearest-neighbor novelty,admitting only the most informative frames via a budget-normalizedgate that adapts to video dynamics. A priority-based eviction policykeeps the bank compact yet informative, while dynamic routing retrievesthe most relevant historical frames at attention time. We further pro-pose Surprise-Aware Denoising, which uses intra-chunk cosine dis-tance among adjacent frames after a single denoising step as a self-referential difficulty predictor, enabling adaptive step reduction for easychunks while preserving full refinement for complex ones. Experimentson VBench show that Surprise Forcing maintains robust narrative coher-ence in long videos without compromising real-time responsiveness.

While recent diffusion models have achieved remarkable suc-cess in visual content editing, extending them to stereo image editingremains highly challenging due to the lack of explicit stereo geomet-ric consistency alignment, which leads to cross-view inconsistencies andgeometric distortions. We propose StereoEdit, a training-free diffusionframework designed to achieve geometrically consistent and structurallycoherent stereo editing. StereoEdit introduces two complementary mod-ules that explicitly enforce geometric reliability during the denoising pro-cess. The Geometrically-Gated Attention Reference (GGAR) adaptivelyfilters cross-view attention based on disparity-aware confidence, ensur-ing stable correspondences across epipolar regions, while the Geometry-Aware Latent Alignment (GALA) performs confidence-weighted, bidi-rectional latent corrections to maintain parallax alignment throughoutthe diffusion process. Together, these modules enable robust and consis-tent stereo edits. Extensive experiments on diverse stereo benchmarksdemonstrate that StereoEdit significantly improves cross-view coherenceand visual realism, establishing a new foundation for controllable andimmersive stereoscopic content editing.

Recent advances in video diffusion models have greatly im-proved visual fidelity, yet their generated motions often violate physicalplausibility. We observe a common kinematic failure, “motion entangle-ment”, the unintended coupling of independent motion sources, such ascamera movement and object motion. We identify that this issue stemsfrom data bias and the reconstruction-based training design of diffusionmodels. Training on noisy videos that still retain coarse motion cues in-advertently encourages the model to replicate existing motion without anincentive to learn how to model kinematically-grounded motions. To ad-dress this, we propose a Self-Imagination Fine-Tuning (SIFT) paradigm,which enables the model to learn from its own generated videos ratherthan directly reconstructing real ones, breaking the reconstruction short-cut. We further employ motion-aware discriminative supervision and aprogressive hard-case replay strategy to stabilize and accelerate learning.By leveraging freely-generated text prompts, our method can denselycover a broad motion space, including rare or finely-disentangled scenar-ios that would be costly to collect as video data. Extensive experimentsdemonstrate that our approach substantially improves the physical real-ism, motion disentanglement, and controllability of generated videos.

Modern Video Object Segmentation (VOS) involves track-ing and segmenting user-specified targets. While recent approaches haveachieved remarkable performance in single-target scenarios, extendingthem to multi-target settings typically involves replicating the single-target processing for each individual object, resulting in reduced framerates (FPS) with unbounded latency as target density scales. Built uponSegment Anything 2 (SAM2), we propose SAM-MT, which addressesthis by transforming the model into an interactive framework for real-time Multi-Target video segmentation. SAM-MT uses explicit queriespropagated and updated across frames to represent different individualtargets, in parallel with a shared representation for global context. It em-ploys decoupled masked attention to keep individual identities distinctfrom cross-target interference, and sparse memory for stable temporalevolution, along with specialized strategies for occlusion handling andoverlap prevention. SAM-MT successfully decouples latency from thenumber of targets, achieving real-time speed on par with single-targetbaselines (>36 FPS for 10 targets) while maintaining SAM2’s robustvideo segmentation performance.

Text-to-image (T2I) diffusion models have achieved strik-ing progress but still struggle to synthesize rare concepts involving un-usual attribute-object pairings, often resulting in concept omission orsemantic drift where a dominant entity overwhelms the generation. Trac-ing these failures to a lack of compositional balance during the denois-ing trajectory, we propose RADIANCE, a training-free framework thattreats inference as a closed-loop feedback process. RADIANCE augmentspretrained backbones with three modular components: (1) a Composi-tional Similarity Monitor (CSM) that tracks the emergence of objectsand attributes in intermediate latents via CLIP-based feedback; (2) aBidirectional Scale Controller (BSC) that applies a reactive "restoringforce" using positive and negative IP-Adapter scales to rebalance biasedtrajectories; and (3) a Feedback Guidance Scheduler (FGS) that coordi-nates these updates across timesteps without additional training. We fur-ther extend the framework to multi-object prompts via Delayed AdapterActivation (DAA) and Layer-wise Alternating Guidance (LAG) to pre-vent premature concept fusion. By overlapping monitoring and denoisingthrough pipelined execution, RADIANCE maintains competitive latencywhile significantly enhancing the per-sample success rate and effectivethroughput. Experiments on RareBench and T2I-CompBench demon-strate that RADIANCE consistently enhances compositional alignmentand perceptual quality over state-of-the-art baselines.

High-resolution 3D asset generation is vital in various 3D ap-plications. Existing state-of-the-art diffusion-based models remain con-strained by fixed resolutions, limiting their ability to produce details. Inthis paper, we tackle the challenge of generating more detailed, higher-resolution 3D objects by introducing a 3D super-resolution (SR) frame-work built on existing 3D generative foundation models. To this end,we design PLSR, a progressive and localized super-resolution solutionto achieve this goal effectively and memory efficiently. Technically, givena coarse geometry from a pretrained 3D generator, we decompose theglobal SR task into localized sub-tasks via an associative input de-composition scheme, adapt a flow-based 3D generator into a local-ized super-resolution model through low-cost finetuning, and unifythem in an iterative patch-wise denoising pipeline for seamless high-resolution output. Experiments on challenging objects show that our ap-proach is able to generate 3D details with new strong fine-detail fidelitywhile significantly reducing the computational cost, offering a new andpractical solution for high-resolution 3D asset generation.

World models enable agents to reason about future outcomesand learn policies from their knowledge of state transition, but existingapproaches primarily focus on reconstructing future observations or fea-tures, which introduces unnecessary complexity and limits their effec-tiveness for decision making. In this work, we propose a compatibilityprediction Latent World Model (LWM) for robot navigation that pre-dicts action-conditioned latent feature compatibility rather than recon-structing observations. Our key insight is that spatial proximity corre-lates with latent feature similarity, enabling action consequences to beevaluated directly in latent space. To support counterfactual training,our model leverages action sequences sampled across trajectories andlearns to predict which sequences lead closer to the goal. Furthermore,we demonstrate how the learned world model can supervise policy learn-ing from unlabeled video data and further improve policies through re-inforcement learning entirely within the world model. This imagination-driven framework eliminates the need for action annotations and addi-tional environment interaction. Extensive experiments on multiple real-world robot navigation datasets show that our approach significantlyoutperforms prior world model and imitation learning methods in pre-diction accuracy, policy learning, and real-world navigation performance.The code, pretrained models, and additional materials are available athttps://wzm206.github.io/latent-world-model-nav/.

Affordance prediction serves as a critical bridge between perception and action in embodied AI. However, existing research is confined to pinhole camera models, which suffer from narrow Fields of View (FoV) and fragmented observations, often missing critical holistic environmental context. In this paper, we present the first exploration into Panoramic Affordance Prediction, utilizing 360-degree imagery to capture global spatial relationships and holistic scene understanding. To facilitate this novel task, we first introduce PAP-12K, a largescale benchmark dataset containing over 1,000 ultra-high-resolution (12k, 11904×5952) panoramic images with over 12k carefully annotated QA pairs and affordance masks. Furthermore, we propose PAP, a trainingfree, coarse-to-fine pipeline inspired by the human foveal visual system to tackle the ultra-high resolution and severe distortion inherent in panoramic images. PAP employs recursive visual routing via grid prompting to progressively locate targets, applies an adaptive gaze mechanism to rectify local geometric distortions, and utilizes a cascaded grounding pipeline to extract precise instance-level masks. Experimental results on PAP-12K reveal that existing affordance prediction methods designed for standard perspective images suffer severe performance degradation and fail due to the unique challenges of panoramic vision. In contrast, PAP framework effectively overcomes these obstacles, significantly outperforming state-of-the-art baselines and highlighting the immense potential of panoramic perception for robust embodied intelligence.

Industrial Video Anomaly Detection (IVAD) aims to identifyanomalous objects and events in an industrial process, which is crucial formodern manufacturing and quality control systems. Existing VLM-basedanomaly reasoning methods are capable of detecting open-ended anoma-lies in general domains. However, their performance declines in industrialsettings characterized by intricate object transformations, strict physics,and procedural constraints. To tackle the complexity of such interaction-intensive detection, we introduce a training-free agentic framework O-VAD for anomaly detection free of domain-specific knowledge, empha-sizing object state evolution like humans inspectors. It is designed totrack spatial-temporal dynamics and underlying transformations of de-tected objects over time, and then reason over the object-wise temporalstate trajectories to identify abnormal objects in grounded frames. Ourmethod overcomes limitations of prior approaches that rely on retrainingon normal clips or injecting domain knowledge as context for test-timeinference. Extensive experiments on three IVAD datasets demonstratethat our method outperforms frontier VLMs, agentic frameworks, andtraditional VAD methods fine-tuned on the respective datasets, whileproviding interpretable reports over anomaly processes and types.

Collaborative perception extends single-agent perception by enabling multiple vehicles to exchange complementary perceptual information. However, it introduces an inherent trade-off between perception gain and communication overhead, which is particularly severe for 3D semantic occupancy prediction that relies on fine-grained spatial structures. Existing methods typically compress 3D features into 2D, causing severe spatial information loss, or transmit dense 3D representations, hindering real-world deployment. To overcome these limitations, we propose a bandwidth-efficient collaborative Vector Quantization Semantic Occupancy Prediction (VQSOP) framework. VQSOP employs a SparseAware Vector Quantization (SAVQ) mechanism that exploits 3D scene sparsity to compactly encode informative regions, drastically reducing communication overhead while preserving complete geometric context. Furthermore, to enhance structural consistency and feature continuity, we design a Dual-Branch Adaptive Spatial Refinement (ASR) module that dynamically fuses local high-frequency details with broad contextual semantics. Extensive experiments demonstrate that our approach achieves state-of-the-art performance while reducing communication volume by up to 82×.

Multimodal large language models (MLLMs) achieve strongperformance on benchmarks that evaluate text, image, or video un-derstanding separately. However, these settings do not assess a criticalreal-world requirement, which involves retrieving relevant evidence fromlarge, heterogeneous multimodal corpora prior to reasoning. Most ex-isting benchmarks restrict retrieval to small, single-modality candidatesets, substantially simplifying the search space and overstating end-to-end reliability. To address this gap, we introduce MultiHaystack, thefirst benchmark designed to evaluate both retrieval and reasoning underlarge-scale, cross-modal conditions. MultiHaystack comprises over 46,000multimodal retrieval candidates across documents, images, and videos,along with 747 open yet verifiable questions. Each question is groundedin a unique validated evidence item within the retrieval pool, requiringevidence localization across modalities and fine-grained reasoning. In ourstudy, we find that models perform competitively with evidence, but per-formance drops sharply when retrieving evidence from the full corpus.Additionally, even the strongest retriever, E5-V, achieves only 40.8%Recall@1, while state-of-the-art MLLMs such as GPT-5 experience asignificant drop in reasoning accuracy from 80.86% when provided withthe corresponding evidence to 51.4% under top-5 retrieval. These resultsindicate that multimodal retrieval over heterogeneous pools remains aprimary bottleneck for MLLMs, positioning MultiHaystack as a valuabletestbed that highlights underlying limitations obscured by small-scaleevaluations and promotes retrieval-centric advances in multimodal sys-tems. Our code and benchmark are available at link.

Most advances in human mesh recovery (HMR) have focusedon pelvis-centered recovery, overlooking metric 3D localization and detec-tion accuracy in the camera coordinate system – two key factors for real-world applications such as human–robot interaction and social scene un-derstanding. Current evaluation protocols often ignore these aspects, em-phasizing per-person, root-centered recovery rather than camera-spaceperception. As a result, existing approaches rely on fixed camera as-sumptions or handcrafted post-processing, limiting their robustness andpractical deployment. We introduce Multi-HMR 2, a simple yet ro-bust DETR-based framework for Multi-person camera-centric Humandetection, Mesh Recovery, and tracking. Multi-HMR 2 predicts a scene-consistent camera together with human meshes, enabling metric 3D lo-calization without ground-truth intrinsics. Moreover, by distilling image-based memory features from SAM2, Multi-HMR 2 extends to tracking,achieving consistent identity association without video supervision. De-spite its conceptual simplicity – no handcrafted components, no videoinput, and no ground-truth cameras – Multi-HMR 2 achieves state-of-the-art pelvis-centered performance while substantially improving detec-tion accuracy and metric 3D localization.

Scaling has driven major advances in vision foundation mod-els, yet extending it to metric depth estimation remains challenging dueto heterogeneous sensor noise, camera-dependent biases, and metric am-biguity in noisy cross-source 3D data. We introduce MetricAnything,a simple and scalable pretraining framework that learns metric depthfrom diverse, noisy 3D sources without manual prompts, camera-specificmodeling, or task-specific architectures. At its core is the Sparse Met-ric Prompt, formed by randomly masking depth maps, which acts asa universal interface to decouple spatial reasoning from sensor and cam-era biases. Pretrained on ∼20M image–depth pairs from reconstructed,captured, and rendered 3D data across 10,000+ camera models, Met-ricAnything demonstrates—for the first time—a clear scaling trend inmetric depth. The model excels at prompt-driven tasks such as depthcompletion, super-resolution, and Radar-camera fusion, while its distilledprompt-free student achieves state-of-the-art results in monocular depthestimation, camera intrinsics recovery, single/multi-view metric 3D re-construction, and VLA planning. Using its pretrained ViT as a visualencoder also significantly enhances Multimodal Large Language Modelsin spatial intelligence. These results show that metric depth estimationcan follow the same scaling laws as modern foundation models, paving ascalable and efficient path toward real-world metric perception. Projectpage: https://metric-anything.github.io/metric-anything-io/

This study introduces a masked coordinate pooling-basedattention network (MCPNet), which is a novel framework designed toimprove medical landmark detection. Coordinate features are essentialin accurately localizing medical landmarks. Recently, many studies haveused coordinate information in final regression heads; however, its directincorporation into the attention mechanism remains largely unexplored.To address this issue, we propose MCPNet, which directly extracts posi-tional features from CNN feature maps and analyzes them in the coordi-nate domain. Specifically, MCPNet employs learnable masks and coordi-nate maps to effectively encode diverse and richer anatomical positionalcues of the landmarks. Moreover, the proposed mask maps are utilizedwhen transforming the analyzed coordinate features back into the origi-nal CNN feature space, ensuring smooth reconstruction within the net-work. Extensive experiments on four public benchmark datasets demon-strate that the proposed MCPNet outperforms state-of-the-art methods,offering improved accuracy and efficiency.

Flow Matching (FM) has achieved remarkable generativeperformance, yet it suffers from exposure bias due to discrepancies be-tween training and inference. Existing mitigation strategies typicallyrely on static constraints or external heuristics. In this work, we pro-pose that exposure bias itself inherently contains dynamic signals thatcan guide its own rectification. To leverage this, we introduce DEFAR(DirEctional-Frequency Adaptive Rectification). This framework simu-lates the single-step inference process during training to identify exposurebias. It utilizes the directional and frequency adaptive feedback signalswithin bias itself to enhance the bias tolerance of the model. It consistsof two key components: (1) Anti-Drift Rectification (ADR). ADRtreats inference-time drift as a signal to learn the direction to steer devi-ated states back toward the target. ADR endows the model with intrin-sic active self-rectification capabilities; (2) Frequency Compensation(FC). Empirically, we observe that accumulated bias often stems from alack of low-frequency components in high-noise stages and exposure biascarries the missing frequency information. FC leverages the bias itself asa self-feedback weighting factor to reinforce the missing frequency com-ponents. Experiments on CIFAR-10, CelebA-64, and ImageNet-256/512show that DEFAR outperforms prior baselines and further demonstratesfavorable scalability, compatibility, and inference robustness. Code willbe made available in https://github.com/wuliwuliy/DEFAR.

Detecting anomalies in industrial objects is an importanttopic for increasing production efficiency. More complex objects oftenrequire the analysis of several view points, which has led to the fieldof multi-view anomaly detection. We present MATCH , the first multi-view anomaly detection method based on Flow Matching (FM). Withthe ODE formulation of Flow Matching, we can estimate likelihoods andthereby derive an anomaly score to detect anomalies in multi-view imagedata at object, image, and pixel-level. The architectural flexibility of FMmodels allows us to efficiently transform features of different spatial sizesto the normal distribution. We evaluate thoroughly on the already estab-lished Real-IAD data set and are also the first to provide a comprehen-sive evaluation of popular anomaly detection methods for the MANTA-Tiny data set. MATCH achieves state-of-the-art performance in bothanomaly detection and segmentation, all while running on consumer-levelhardware. By omitting the costly divergence term needed for likelihoodestimation, we ensure that MATCH is usable in real-time productionscenarios. Lastly, several ablation studies are conducted to validate themethodological choices. Code: https://github.com/m-kruse98/MATCH

Diffusion-based methods demonstrate significant potentialfor remote sensing image super-resolution at large scaling factors, partic-ularly in reference-based super-resolution (RefSR), where high-resolutionreference images provide critical fine-grained texture priors. However, ex-isting methods often suffer from a trade-off between over-reliance on ref-erence information, which leads to texture artifacts, and under-utilizationof such information, which results in insufficient detail recovery. To ad-dress these issues, we propose DS-DiT, a Decoupled Siamese DiffusionTransformer that decouples the interaction between low-resolution (LR)and reference (Ref) conditions within the attention mechanism. By al-lowing LR structural priors and Ref texture information to independentlyinteract with the noisy latent, the framework effectively mitigates com-petition between the two conditional sources. To further compensatefor the limited local modeling ability of global attention, we introduce aPatch-Level Weighting (PLW) module that adaptively modulates the fu-sion of conditional sources. In addition, the siamese architecture enablesan inference-time autoguidance strategy that exploits the prediction dis-crepancy between strong and weak Ref conditions to improve generationquality without additional training. Experimental results across multi-ple datasets and scaling factors show that DS-DiT outperforms existingmethods in both quantitative metrics and visual fidelity. The source codeis available at https://github.com/B1nary-L/DS-DiT.