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ECCV 2026

SAM-MT: Real-Time Interactive Multi-Target Video Segmentation

Ruiqi Shen, Chang Liu, Henghui Ding

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.

ECCV 2026

RADIANCE: Relative Adaptive Denoising with IP-Adapter for Novel Concept Enhancement

Zi-Xiang Ni, Bo-Lun Huang, Teng-Fang Hsiao, Bo-Kai Ruan, Hong-Han Shuai

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.

ECCV 2026

Progressive and Localized Super-Resolution of 3D Objects via Localized Latent Voxel Diffusion

Yuxin Liu, Minshan Xie, Jiawen Liang, Runsong Zhu, Chi-Wing Fu, Tien-Tsin Wong

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.

ECCV 2026

Predicting Consequences and Reinforcing Navigation Policies with Latent World Models

Zengmao Wang, Wei Gao, Shuhan Shen

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/.

ECCV 2026

Panoramic Affordance Prediction

Zixin Zhang, Chenfei Liao, Hongfei Zhang, Harold Haodong Chen, Kanghao Chen, Zichen Wen, Litao Guo, Bin Ren, Xu Zheng, Yinchuan Li, Xuming Hu, Nicu Sebe, Yingcong Chen

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.

ECCV 2026

O-VAD: Industrial Video Anomaly Detection through Object-Centric Tracking and Reasoning

Mei Yuan, Qi Long, Qifeng Wu, Zhenyang Li, Yizhou Zhao, Lei Wang, Yang Liu, Min Xu

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.

ECCV 2026

Sparse-Aware Vector Quantization for Bandwidth-Efficient Collaborative 3D Semantic Occupancy Prediction

Feng Li, Chaokun Zhang, Gong Chen

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×.

ECCV 2026

MultiHaystack: Benchmarking Multimodal Retrieval and Reasoning over 40K Images, Videos, and Documents

Dannong Xu, Zhongyu Yang, Jun Chen, Yingfang Yuan, Ming Hu, Lei Sun, Luc Van Gool, Danda Paudel, Chun-Mei Feng

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.

ECCV 2026

MuCHeR: Multi-Person Camera-Centric Human Detection, Mesh Recovery and Tracking

Guénolé Fiche*, Philippe Weinzaepfel, Romain Brégier, Fabien Baradel

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.

ECCV 2026

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources

Jiahui Yang, Donglin Di, Xuancheng Zhang, Lei Fan, Jianxun Cui, Hao Li, Baorui Ma

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/

ECCV 2026

MCPNet:Masked Coordinate Pooling-based Attention Network for Medical Landmark Detection

Gyu-Sung Ham, Gi Hyun Lim, Kanghan Oh

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.

ECCV 2026

Exposure Bias Can Alleviate Itself via Directional and Frequency Rectification in Flow Matching

Guanbo Huang, Jingjia Mao, Fanding Huang, Fengkai Liu, Xiangyang Luo, Yaoyuan Liang, Jiasheng Lu, xiaoe Wang, Pei Liu, Ruiliu Fu, Ruqi Huang, Shao-Lun Huang

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.

ECCV 2026

MATCH: Flow Matching for Multi-View Anomaly Detection

Mathis Kruse, Melissa Schween, Bodo Rosenhahn

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

ECCV 2026

Learning to Balance: Decoupled Siamese Diffusion Transformer for Reference-Based Remote Sensing Image Super-Resolution

Bin Luo, Runmin Dong, Zhaoyang Luo, Jinxiao Zhang, Jiyao Zhao, Fan Wei, Haohuan Fu

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.

ECCV 2026

Honey, I Shrunk the Arc de Triomphe!

Yuanbo Xiangli, Hanyu Chen, Xueqing Tsang, Noah Snavely

Metric scale monocular geometry estimation has seen signif-icant progress through large-scale data aggregation, yet current founda-tion models suffer from a persistent “scale-collapse” phenomenon: dis-tant landmarks and vast landscapes are metrically underestimated. Thisperformance gap stems from a training data bottleneck, where exist-ing metric-scale datasets are hardware-constrained to unvaried street-level LiDAR or short-range indoor scans, or consist of synthetic datathat lacks the semantic complexity of the physical world. To bridge thisgap, we curate a new metrically-grounded, in-the-wild dataset that wecall MetricScenes, gathered from a variety of sources including Inter-net photo collections and stereo imagery. We estimate camera poses andinitial depth maps for each scene using off-the-shelf methods, and re-cover absolute scale from geo-tagged metadata as well as known stereocamera baselines. We also improve the quality of depth maps derivedfrom MetricScenes via a new two-stage Poisson completion method. Fine-tuning MoGe-2 on our dataset significantly mitigates scale-collapse andachieves superior metric accuracy in unconstrained, open-domain sceneswhile maintaining state-of-the-art performance on standard benchmarks.Project page: https://metricscenes.github.io/.

ECCV 2026

Towards Consistent and Efficient Dataset Distillation via Diffusion-Driven Selection

Xinhao Zhong, Shuoyang Sun, Zhaoyang Xu, Xulin Gu, Bin CHEN, Min Zhang, Yaowei Wang

Dataset distillation provides an effective approach to reducememory and computational costs by optimizing a compact dataset thatachieves performance comparable to the full original. However, for large-scale datasets and complex deep networks (e.g., ImageNet-1K with ResNet-101), the vast optimization space hinders distillation effectiveness, limit-ing practical applications. Recent methods leverage pre-trained diffusionmodels to directly generate informative images, thereby bypassing pixel-level optimization and achieving promising results. Nonetheless, theseapproaches often suffer from distribution shifts between the pre-traineddiffusion prior and target datasets, as well as the need for multiple dis-tillation steps under varying settings. To overcome these challenges, wepropose a novel framework that is orthogonal to existing diffusion-baseddistillation techniques by utilizing the diffusion prior for patch selectionrather than generation. Our method predicts noise from the diffusionmodel conditioned on input images and optional text prompts (with orwithout label information), and computes the associated loss for eachimage-patch pair. Based on the loss differences, we identify distinctiveregions within the original images. Furthermore, we apply intra-classclustering and ranking on the selected patches to enforce diversity con-straints. This streamlined pipeline enables a one-step distillation process.Extensive experiments demonstrate that our approach consistently out-performs state-of-the-art methods across various metrics and settings.

ECCV 2026

Harnessing SSL for Segmentation in 3D Microscopy with Noisy Labels and Hard Patches

Lingtong Xu, Ahmadreza Attarpour, Shruti Patel, Fengqing Yu, Matthew Rozak, Bojana Stefanovic, Anne Martel, Maged Goubran

Segmentation in 3D microscopy is challenging due to hard (tosegment) patches, noisy labels resulting from the use of semi-automatedlabeling methods, image artifacts, off-target fluorescence, and lack of la-beled data because of high labeling effort. In this work, we introducea novel unified method to tackle these issues simultaneously in 3D mi-croscopy. First, we introduce µ3DINO, a 3D model pretrained on anultra-large multimodal dataset of over 2 million microscopy volumes.We then create µDivSeg, a segmentation pipeline that uses pretrainedweights to detect noisy labels and hard patches to guide and correct seg-mentation training. We evaluate our methods on a toy dataset and 4 real-world datasets from light-sheet and two-photon microscopy with a varietyof markers, in comparison to two state-of-the-art (SOTA) pipelines. Ourmethods outperform SOTA techniques on increasing levels of syntheticlabel perturbations and real-world data with diverse distributions.

ECCV 2026

FILT3R: Latent State Adaptive Kalman Filter for Streaming 3D Reconstruction

Seonghyun Jin, Jong Chul Ye

Streaming 3D reconstruction maintains a persistent latentstate that is updated online from incoming frames, enabling constant-memory inference. A key failure mode is the state update rule: aggressiveoverwrites forget useful history, while conservative updates fail to tracknew evidence, and both behaviors become unstable beyond the traininghorizon. To address this challenge, we propose FILT3R, a training-freelatent filtering layer that casts recurrent state updates as stochastic stateestimation in token space. FILT3R maintains a per-token variance andcomputes a Kalman-style gain that adaptively balances memory reten-tion against new observations. Process noise – governing how much thelatent state is expected to change between frames – is estimated onlinefrom EMA-normalized temporal drift of candidate tokens. Using exten-sive experiments, we demonstrate that FILT3R yields an interpretable,plug-in update rule that generalizes common overwrite and gating poli-cies as special cases. Specifically, we show that gains shrink in stableregimes as uncertainty contracts with accumulated evidence, and risewhen genuine scene change increases process uncertainty, improving long-horizon stability for depth, pose, and 3D reconstruction, compared to theexisting methods.

ECCV 2026

Fast and Scalable LiDAR Data Generation for Autonomous Driving Simulation without Raycasting

Irfan Nafiz Shahan, Al-Mubin Nabil, Arpan Kusari

Autonomous driving relies on LiDAR simulation for gener-ating realistic 3D point clouds, typically achieved using computation-ally expensive raycasting. We introduce GnoSphere360, a scalable Li-DAR data generation method that eliminates raycasting by projectingtriangular meshes onto a unit sphere and interpolating point sampleswith gnomonic projection. This computational approach bypasses tra-ditional ray-mesh intersection, significantly reducing computation timewhile maintaining compatibility for further downstream physics-basedmodeling. Benchmarking against NVIDIA OptiX and native CARLAengines, GnoSphere360 achieves up to 35× speedup over CARLA forphysical LiDAR models and maintains robust performance against Op-tiX, while preserving similar LiDAR point cloud output. However, forextremely dense LiDAR configurations, GnoSphere360 achieves over 3.6xspeedup against OptiX, showcasing the scalability of our proposed method.Ablation studies demonstrate robustness of GnoSphere360 across vary-ing scenes and sensor models, while preserving data fidelity. Our resultsshow GnoSphere360 is an efficient, accurate, and parallelizable alterna-tive to raycasting and rasterization for autonomous driving simulation.Project website: gnosphere360.github.io

ECCV 2026

Fair and Faithful: A Diffusion-Enhanced Dataset and Hybrid State-Space Mamba for Face Super-Resolution

Tao Wang, Peiwen Xia, Bowen Tang, Jinwei Chen, Kaihao Zhang, Bo Li

Face super-resolution (FSR) aims to enhance low-resolution facial im-ages into high-resolution versions. Existing FSR methods face significant chal-lenges, including the lack of publicly available and racially diverse datasets,which hinders reproducibility and subgroup-aware benchmarking. Additionally,many methods struggle to balance the restoration of fine-grained local detailswith the preservation of global facial structures, often producing results that areeither geometrically inconsistent or lack realistic details. To address these is-sues, we introduce SFHQFSR, a new FSR dataset offering high-quality, raciallydiverse facial images generated from generative models, addressing challengesin reproducibility, fairness, and dataset construction. Building on this, we pro-pose GLASNet, a hybrid state-space network that integrates global-local adaptivescanning with multi-domain refinement for high-quality facial image reconstruc-tion. GLASNet combines global semantic reasoning with region-specific localmodeling through a Region-Adaptive Scan Module and enhances restoration witha Multi-domain Refiner using channel, spatial, and frequency attention. Extensiveexperiments demonstrate that GLASNet achieves state-of-the-art performance.

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