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ECCV 2026

VQT: Vector Quantization Tuning for Efficient Fine-tuning and Compression of Pre-trained Vision Transformers

Yinglong Li, Jiyuan Xia, Jingcheng Xie, Zhiwei Xiong

Pre-trained Vision Transformers (ViTs) have shown strongperformance across diverse tasks, creating increasing demand for efficientfine-tuning and compression for downstream deployment. Existing VectorQuantization (VQ) methods achieve high compression ratios, yet strug-gle to balance fine-tuning efficiency and downstream performance. In thiswork, we propose Vector Quantization Tuning (VQT), a novel frameworkfor efficient fine-tuning and compression of pre-trained ViTs. We iden-tify Coupled Vector Quantization Noise (CVQN) as a key factor limitingperformance. It arises from the interaction between Low-Rank Adapta-tion (LoRA) and the Straight-Through Estimator (STE), which degradesoptimization effectiveness under vector quantization. To address this,VQT employs a VQ-aware fine-tuning process that stochastically replacesvector-quantized weights with continuous counterparts to reduce CVQN,while progressively aligning the model toward fully vector-quantized rep-resentations to ensure consistency between fine-tuning and deployment.Furthermore, VQT enables efficient storage for multi-task deploymentby storing only lightweight task-specific parameters in the cloud, withless than 1 bit per parameter needed to reconstruct compressed models.Extensive experiments show the advantages of VQT over state-of-the-art baselines in performance and efficiency at high compression ratios.For example, at 1-bit compression, VQT surpasses the advanced baselineby 9.5% on the VTAB-1K benchmark while fine-tuning only 0.8% of theparameters. The code is available: https://github.com/leenas233/VQT.

ECCV 2026

VLA-JEPA: Enhancing Vision-Language-Action Model with Latent World Model

Jingwen Sun, Wenyao Zhang, Zekun Qi, Shaojie Ren, Zezhi Liu, Hanxin Zhu, Guangzhong Sun, Xin Jin, Zhibo Chen

Pretraining Vision-Language-Action (VLA) policies on internet-scale video is appealing, yet current latent-action objectives often learnthe wrong thing: they remain anchored to pixel variation rather thanaction-relevant state transitions, making them vulnerable to appearancebias, nuisance motion, and information leakage. We introduce VLA-JEPA,a JEPA-style pretraining framework that sidesteps these pitfalls by design.The key idea is leakage-free state prediction: a target encoder produceslatent representations from future frames, while the student pathwaysees only the current observation—future information is used solely assupervision targets, never as input. By predicting in latent space ratherthan pixel space, VLA-JEPA learns dynamics abstractions that are ro-bust to camera motion and irrelevant background changes. This yieldsa simple two-stage recipe—JEPA pretraining followed by action-headfine-tuning—without the multi-stage complexity of prior latent-actionpipelines. Experiments on LIBERO, LIBERO-Plus, SimplerEnv and real-world manipulation tasks show that VLA-JEPA achieves consistent gainsin generalization and robustness over existing methods. Code is availableat https://github.com/ginwind/VLA-JEPA.

ECCV 2026

VarProtoAD: Variational Prototype-Conditioned Prompting for Zero-Shot Anomaly Detection

Mengyang Zhao, Zhuolin He, Haiyang Yu, Teng Fu, Ke Niu, Xiangyang Xue

Zero-shot anomaly detection (ZSAD) requires detecting andlocalizing diverse defects in unseen categories. Recent vision-languagemodel (VLM) based approaches improve ZSAD via prompt learning, yettheir prompts are often weakly grounded to transferable visual evidence,making them prone to semantic drift under domain shifts and limit-ing the reuse of normal/abnormal primitives. We propose a VariationalPrototype-conditioned prompting framework (VarProtoAD) that ex-plicitly conditions prompt learning on visual prototypes discovered in thefeature space. Specifically, multi-layer patch tokens are ℓ2 -normalized andmodeled with a variational von Mises–Fisher mixture to learn layer-wisenormal and abnormal prototype banks. These prototypes form multi-cluster semantic anchors on the hypersphere and are naturally alignedwith cosine-based VLM matching, where anomalies correspond to direc-tional deviations. Conditioned on these anchors, learnable prompt con-text tokens interact with the prototype banks via confidence-gated cross-attention, producing explicit prototype-conditioned prompts as well asimplicit deviation prompts derived from normal prototypes to bettercover unseen anomaly semantics. Finally, multi-layer image–text simi-larities are fused within a unified framework to jointly produce image-level anomaly scores and pixel-level anomaly maps. Experiments on 15industrial and medical datasets demonstrate strong cross-domain per-formance, indicating that VarProtoAD yields more stable and robustprompt learning.

ECCV 2026

Human-Level Accuracy, Non-Human Strategies: Revealing Model-Human Divergence in Video Physical Reasoning

Fanhong Li, Shurui Zheng, Yinzi Yinzi, Junbo Cui, Lei Ji, Jia Liu

Video foundation models now reach human-level accuracy onphysical-reasoning benchmarks, yet such tasks require predicting unob-served physical outcomes. Do these models perform human-like forwardsimulation, or do they exploit statistical regularities in visible scenes?Accuracy alone cannot distinguish these strategies. We introduce a distri-butional evaluation framework that treats model seeds and human ratersas populations, enabling comparison of consensus, uncertainty, and strat-egy. On the Physion benchmark, we evaluate three ViT-L architectures(V-JEPA2, VideoMAEv2, DINOv2). V-JEPA2 narrows the accuracy gapto ∼1 percentage point (73.2% vs. 74.2%), yet model–human disagree-ment reaches 26.4%, far exceeding human–human disagreement (4.8%),with substantially lower agreement (κ ≈ 0.48 vs. 0.91). The divergenceis scenario-structured: models outperform humans on geometric reason-ing (linking, +11.8 pp), while human advantages appear on causal-chainand other future-dynamics scenarios in the released split (e.g., dominoes,−10.5 pp). Strategy fingerprinting confirms all three architectures sharenon-human strategies while none aligns with humans. Attribution analy-sis suggests that unobservable outcome features, rather than visible sceneproperties, predict this divergence, consistent with models relying moreon scene-level statistical regularities than on explicit forward simulation,a systematic divergence that accuracy alone cannot reveal. Code is avail-able at https://github.com/fanhong-li/model-human-divergence.

ECCV 2026

URHead: A Unified UV-Space Representation for Joint Mesh–3DGS Optimization in Head Avatars

Seonghak Lee, Junhee Cho, Jisoo Park, Min-Gyu Park, Jongmin Lee, Ju Yoon, Junseok Kwon

We present URHead, a unified representation for high-fidelityand animatable head avatars that fundamentally redefines mesh-Gaussianintegration. While mesh-based methods offer precise geometric controlbut lack photorealistic detail, and Gaussian-based approaches achievephotorealism but suffer from poor structural consistency, existing hybridsolutions fail to fully leverage their complementary strengths. Our keycontribution is a UV-space unification where both representations sharea common UV parameterization. Through joint optimization with adap-tive gaussian sampling, our method automatically learns to disentangleand allocate appropriate roles to each component. URHead maintainsfull parametric controllability while preserving subject-specific details,outperforms existing state-of-the-art methods in reconstruction qualityand animation consistency.Our code is available at https://github.com/Lseonghak/URHead

ECCV 2026

Unsupervised Point Cloud Registration via Training-Time Semantic Guidance

KeZheng Xiong, Shiyun Xu, Sheng Ao, Siqi Shen, Cheng Wang, Chenglu Wen

Unsupervised registration of large-scale LiDAR point cloudsremains challenging due to the geometric ambiguity inherent in out-door scenes, which degrades pseudo-label quality and leads to subop-timal convergence, particularly for sparse, low-resolution scans such asthose from nuScenes. We reveal that registration models intrinsicallyencode semantic awareness that strongly correlates with registration ac-curacy, albeit without explicit semantic supervision. However, this nativeawareness is fragile: noisy supervision arising from geometric ambigu-ity in unsupervised settings rapidly erodes the learned semantic struc-ture, causing performance collapse. To this end, we propose CAESAR,a teacher-student framework guided by an off-the-shelf 3D segmenta-tion model exclusively during training. We observe that potential in-lier matches are often buried just beneath a few spurious neighbors inthe noisy feature space, motivating Dual-Cue Guided Re-Matching torecover them through reselection rather than simply rejecting. Build-ing on this, a train-only Semantic-Geometric Label Mining performsCorresponding author.lightweight, batch-specific teacher refinement and mines reliable pseudo-labels under semantic guidance. We further introduce Semantic Predic-tive Distillation to consolidate the student’s semantic awareness in thefeature space. Extensive experiments on KITTI and nuScenes demon-strate state-of-the-art performance, with pronounced gains on the chal-lenging nuScenes benchmark. Crucially, CAESAR incurs zero inferenceoverhead and requires no semantic annotations on the registra-tion data.

ECCV 2026

Unified Video Dense Prediction from Disjoint Data

Yihong Sun, Seoung Wug Oh, Jiahui Huang, Bharath Hariharan, Joon-Young Lee

Scene understanding requires simultaneous prediction aboutgeometry, appearance, and semantics. However, existing task-specific an-notations are fragmented across incompatible, domain-specific datasets.Current unified systems circumvent this by restricting training to fullyco-annotated data, or by incurring the large computational cost of pseudo-labeling. To mitigate this, we introduce UniD, a unified video model thatjointly predicts eight dense scene properties—depth, surface normals, se-mantic segmentation, boundaries, human parts, albedo, shading, and ma-terials—all learned from disjoint, domain-specific datasets. We propose asimple yet effective distillation step in which per-task experts supervisea unified backbone through lightweight task projectors, eliminating theneed for annotation overlap or pseudo-labeling. Our key insight is thatthe strong visual priors of a pretrained diffusion model are sufficientto bridge the domain gaps introduced by disjoint training sources, en-abling robust generalization to scene-task combinations never seen dur-ing training. UniD achieves competitive performance against per-taskspecialists and multi-task baselines, with strong generalization to out-of-distribution scenarios and enhanced temporal and cross-task consistency.Code and video results are available at https://unid-video.github.io.

ECCV 2026

The Map Is Not the Territory: Embedding-Coverage Blacklists for Safe Diffusion Steering

Juyang Bai, Tong Zhou, Shaolei Ren, Xiaolin Xu

Ensuring safe content generation in text-to-image diffusionmodels remains a critical challenge. Existing safety mechanisms focuson model editing or trajectory steering, yet the design of the blacklist,the set of unsafe concepts these methods steer away from, is an equallypivotal but largely under-explored dimension. Through controlled exper-iments on blacklist-guided concept erasure, we uncover three findingsthat overturn common assumptions: longer keyword phrases do not nec-essarily improve safety, enlarging the blacklist yields diminishing returns,and mixing in irrelevant concepts degrades performance. Together theypoint to a single principle: blacklist effectiveness is governed by how wellits keywords cover unsafe regions of the embedding space, not by individ-ual keyword quality or list size. Building on this principle, we introduceMAPS, a framework for automated blacklist optimization. MAPS usesreinforcement learning to discover a prompt token that, when insertedinto an LLM prompt, elicits semantically diverse safety keywords, andthen assembles category-specific blacklists for distinct unsafe concepts.At inference, it selects the most relevant blacklist for each prompt viaembedding similarity, maximizing coverage of the threat while avoidinginterference from unrelated concepts. MAPS acts as a drop-in enhance-ment for existing trajectory-steering methods, reducing attack successrates by 25–52% (relative) across five red-teaming benchmarks on thenudity concept while preserving generation quality, all without modelediting or modifications to the steering procedure. Our code is availableat https://github.com/juyangbai/MAPS.

ECCV 2026

STARLINC: Satellite Trail Artifact Removal using Inter-Frame Correlation

Shingeon Kim, Hyeyoon Lee, Dain Kwon, Kanghyun Choi, SunJong Park, Mi-Ryang Kim, Jeong-Eun Lee, Jinho Lee

The rapid expansion of low Earth orbit satellites such asStarlink is increasingly contaminating astronomical surveys. In practice,contaminated images are often identified through inspection. However,modern surveys generate terabytes of data each night, making manualscreening infeasible and necessitating reliable automated methods forsatellite trail removal. Unfortunately, existing general-domain line de-tection methods fail to generalize to astronomical images due to do-main mismatch, which are mostly grayscale with sparse bright stars andhave a low signal-to-noise ratio. Moreover, training new models fromscratch is impractical due to the lack of large-scale annotated astro-nomical datasets. To address these challenges, we introduce STARLINC,the first ML-based framework for satellite trail removal without requir-ing tedious pixel-level annotation of astronomical images. STARLINCcombines synthetic satellite trail generation for training, inter-frame dif-ferential maps from temporally adjacent exposures to highlight transienttrails, and heatmaps to provide additional localization cues for pixel-level segmentation. Extensive experiments on real-world data demon-strate substantial improvements over baselines, establishing STARLINCas a scalable solution for next-generation astronomical surveys. Code isavailable at https://github.com/starioKim/STARLINC.

ECCV 2026

Spectral Gating via Damped Oscillations for Adaptive Implicit Neural Representations

Alex Costanzino, Pierluigi Zama Ramirez, Giuseppe Lisanti, Luigi di Stefano

Implicit Neural Representations (INRs) have been provensuccessful in encoding continuous signals through coordinate-based net-works, yet facing a spectral dilemma: periodic activations capture finedetails but act as all-pass filters that memorise noise, while spatiallycompact activations regularise effectively but suffer from low-frequencybias. Existing attempts to resolve this trade-off introduce computationaloverhead or tuning frailty. We propose to model each neuron’s activationas the steady-state response of a sinusoidally-forced damped harmonicoscillator, whose amplitude naturally governs the network’s spectral se-lectivity during training. By jointly optimising the oscillator parametersalongside the network weights, our method adapts to the target signal’sspectral content without explicit regularisation. Initialised in the stop-band, the network exhibits a coarse-to-fine learning curriculum that pro-gressively expands its spectral gate, capturing low-frequency structuresfirst and high-frequency details only when justified by the reconstructionobjective. Comprehensive experiments show that our approach consis-tently achieves state-of-the-art or competitive results against establishedINRs, while requiring no task-specific tuning of any hyperparameters.Project Page available at https://alex-costanzino.github.io/fdho/.

ECCV 2026

SLER-IR: Spherical Layer-wise Expert Routing for All-in-One Image Restoration

Shurui Peng, Xin Lin, Shi Luo, Jincen Ou, Dizhe Zhang, Lu Qi, Truong Nguyen, Chao Ren

Image restoration under diverse degradations remains chal-lenging for unified all-in-one frameworks due to feature interference andinsufficient expert specialization. We propose SLER-IR, a sphericallayer-wise expert routing framework that dynamically activates special-ized experts across network layers. To ensure reliable routing, we in-troduce a Spherical Uniform Degradation Embedding with contrastivelearning, which maps degradation representations onto a hypersphereto eliminate geometry bias in linear embedding spaces. In addition, aGlobal–Local Granularity Fusion (GLGF) module integrates global se-mantics and local degradation cues to address spatially non-uniformdegradations and the train–test granularity gap. Experiments on three-task and five-task benchmarks demonstrate that SLER-IR achievesconsistent improvements over state-of-the-art methods in both PSNRand SSIM. Code and models will be available at https://github.com/PSR666/SLER-IR.

ECCV 2026

SemLight: Distilled Semantic–Geometric Fusion for Efficient Local Feature Matching

Guanglu Shi, Xiangzeng Liu, Yunan Li, Tuo Pang, Qiguang Miao

Robust and efficient local feature matching remains a bot-tleneck for real-time visual localization, especially in environments withlow texture, repetitive structures, or illumination changes. Most priorapproaches model either appearance or geometry in isolation, failing toexploit the semantic–geometric interactions crucial for disambiguatingchallenging matches. To address this limitation, we propose SemLight,built on the insight that compact semantic cues can serve as a lightweightprior for resolving matching ambiguity under challenging conditions.Specifically, high-level semantic priors are first distilled into compact,matching-oriented descriptors via a task-aware student network. Thesedescriptors then conditionally re-weight appearance and geometric chan-nels locally, enabling efficient disambiguation with minimal computa-tional overhead. Extensive experiments on relative pose estimation, ho-mography estimation, and visual localization demonstrate state-of-the-art performance. Under comparable computational cost to XFeat, ourmethod achieves a 17.8% relative improvement in AUC@5 over XFeat(50.2 vs. 42.6).

ECCV 2026

RoboTALES: Learning Reasoning-Guided Robot Policies via Task-Aligned Simulated Futures

Hanan Gani, Tejal Kulkarni, Madhoolika Chodavarapu, Nicklas Hansen, Manmohan Chandraker

Pretrained video generative models are promising backbonesfor visuomotor control, but their imagined futures often drift from taskintent and are not reliably action-conditional. As a result, these mod-els can be difficult to use for planning or policy extraction. To addressthese limitations, we propose RoboTALES, a single-stage framework thatlearns task-aligned simulated futures and uses them to train robot poli-cies. Our approach introduces two key innovations: (1) a hierarchicalLLM-based planner that breaks complex tasks into a sequence of sub-goals to guide the model’s imagination; and (2) a VLM-based critic thatevaluates these “imagined” futures and uses reward-based feedback tokeep the model’s internal representations focused on the goal. By an-choring the video generator in abstract reasoning, we produce temporallyconsistent rollouts and more coherent actions. We evaluate RoboTALESon diverse manipulation tasks from RoboCasa and LIBERO10, and showthat our method consistently outperforms existing methods, especiallyin long-horizon tasks. Our code and models are publicly available athttps://github.com/hananshafi/RoboTALES.

ECCV 2026

RoboMirror: Understand Before You Imitate for Video to Humanoid Locomotion

Zhe Li, Boan Zhu, Yangyang Wei, Shuanghao Bai, Yuheng Ji, Tao Huang, Pengwei Wang, Zhongyuan Wang, Gary Chan, Chang Xu, Cheng Chi, Jianfei Yang, Shanghang Zhang

Humans learn locomotion through visual observation, inter-preting visual content first before imitating actions. However, state-of-the-art humanoid locomotion systems rely on either curated motion capturetrajectories or sparse text commands, leaving a critical gap between visualunderstanding and control. Text-to-motion methods suffer from semanticsparsity and staged pipeline errors, while video-based approaches onlyperform mechanical pose mimicry without genuine visual understanding.We propose RoboMirror, the first retargeting-free video-to-locomotionframework embodying “understand before you imitate”. Leveraging VLMs,it distills raw egocentric/third-person videos into visual motion intents,which directly condition a diffusion-based policy to generate physicallyplausible, semantically aligned locomotion without explicit pose recon-struction or retargeting. Extensive experiments validate RoboMirror’seffectiveness, it enables telepresence via egocentric videos, drasticallyreduces third-person control latency by 80%, and achieves a 3.7% highertask success rate than baselines. By reframing humanoid control aroundvideo understanding, we bridge the visual understanding and action gap.

ECCV 2026

RiO-DETR: DETR for Real-time Oriented Object Detection

Zhangchi Hu, Yifan Zhao, Yansong Peng, Wenzhang SUN, Xiangchen Yin, Jie Chen, Peixi Wu, Hebei Li, Xinghao Wang, Dongsheng Jiang, Xiaoyan Sun

We present RiO-DETR: DETR for Real-time OrientedObject Detection, the first real-time oriented detection transformer tothe best of our knowledge. Adapting DETR to oriented bounding boxes(OBBs) poses three challenges: semantics-dependent orientation, angleperiodicity that breaks standard Euclidean refinement, and an enlargedsearch space that slows convergence. RiO-DETR resolves these issueswith task-native designs while preserving real-time efficiency. First, wepropose Content-Driven Angle Estimation by decoupling angle from po-sitional queries, together with Rotation-Rectified Orthogonal Attentionto capture complementary cues for reliable orientation. Second, Decou-pled Periodic Refinement combines bounded coarse-to-fine updates witha Shortest-Path Periodic Loss for stable learning across angular seams.Third, Oriented Dense O2O injects angular diversity into dense super-vision to speed up angle convergence at no extra cost. Extensive ex-periments on DOTA-1.0, DIOR-R, and FAIR-1M-2.0 demonstrate RiO-DETR establishes a new speed–accuracy trade-off for real-time orienteddetection. Code is available at https://github.com/RicePasteM/RiO-DETR.

ECCV 2026

ReViV: Reconstructing the Viewer and the View in 4D from Monocular Egocentric Video

Xiaozhong Lyu, Gen Li, Zhiyin Qian, Xucong Zhang, Marc Pollefeys, Siyu Tang

Egocentric devices, such as wearable front-facing cameras,provide a unique perspective for capturing the continuous interactionbetween a human viewer and the surrounding environment. A holisticand efficient multimodal model capable of reconstructing this 4D rep-resentation is therefore highly desirable. However, existing approachesoften rely on auxiliary inputs such as pre-computed camera trajecto-ries, treat scene perception and human ego-motion modeling as separateproblems despite their strong interdependency, and suffer from slow in-ference time. To address these limitations, we present ReViV, the firstunified framework for holistic egocentric 4D reconstruction that extractsboth viewer and view dynamics from a single monocular RGB video.We formulate the task as learning the full joint probability distribu-tion over multimodal signals, including RGB video, camera trajectory,gaze direction, full-body motion, hand motion, and depth. Powered bya Masked Generative Egocentric Transformer, ReViV operates within asingle feed-forward architecture to simultaneously reconstruct the tem-porally consistent 4D reconstruction across the viewer and the view withfast inference speed. Extensive experiments on diverse benchmarks, in-cluding HoloAssist, HOT3D, ARCTIC, Aria Digital Twin, and TACO,demonstrate that ReViV achieves state-of-the-art accuracy and efficiencyacross holistic ego-body, hand, and gaze reconstruction, camera track-ing, while maintaining highly competitive egocentric depth estimation,without relying on heavy task-specific priors. Code and models are fullyopen-sourced: https://reviv4d.github.io/.

ECCV 2026

The 3D Mirage: Probing and Taming 3D Hallucinations

Hoang Nguyen, Xiaohao Xu, Xiaonan Huang

Monocular depth foundation models achieve remarkable gen-eralization by learning large-scale semantic priors, but this creates a criti-cal vulnerability: they hallucinate illusory 3D structures from planar/low-curvature but perceptually ambiguous inputs. We term this failure the3D Mirage. This paper introduces a novel end-to-end framework toprobe, score, and tame this under-quantified safety risk in monoculardepth under context variation. To probe, we present 3D-Mirage, thefirst benchmark to combine context variation and precise annotation forreal-world illusions with real object exclusions, multi-surface support;purpose-built to stress-test monocular depth on real-world illusions. Toscore, we propose a second-order magnitude-based evaluation with twometrics: the Deviation Composite Score (DCS) for high second-order 3D structure and the Confusion Composite Score (CCS) forcontextual instability. To tame this failure, we introduce GroundedSelf-Distillation, a parameter-efficient strategy on Depth-Anything-V2baseline that surgically targets and resolves hallucination on illusion ROIswhile preserving background knowledge, avoiding catastrophic forgetting.Our work provides an innovative pipeline for diagnosing and addressingthis phenomenon, urging a necessary shift in the evaluation of MDE frompixel-wise accuracy to structural and contextual robustness.Code: https://github.com/hdnndh/The- 3D- Mirage- Probing- and-Taming-3D-HallucinationsDataset: https://huggingface.co/datasets/3dmirage/3D-Mirage

ECCV 2026

RAE-NWM: Navigation World Model in Dense Visual Representation Space

Mingkun Zhang, wangtian shen, Fan Zhang, Haijian Qin, Zihao Pei, Ziyang Meng

Visual navigation requires agents to reach goals in complexenvironments through perception and planning. World models addressthis task by simulating action-conditioned state transitions to predictfuture observations. Current navigation world models typically learnstate evolution under actions within the compressed latent space of aVariational Autoencoder, where spatial compression often discards fine-grained structural information and makes precise action-conditioned pre-diction more difficult. To better understand the propagation character-istics of different representations, we conduct a linear dynamics probeand observe that dense DINOv2 features exhibit stronger linear pre-dictability for action-conditioned transitions. Motivated by this obser-vation, we propose the Representation Autoencoder-based NavigationWorld Model (RAE-NWM), which generatively models navigation dy-namics in a dense visual representation space. We employ a Condi-tional Diffusion Transformer with a Decoupled Diffusion Transformerhead (CDiT-DH) to model continuous transitions, and introduce a sep-arate time-driven gating module for dynamics conditioning to regulateaction injection strength during generation. Extensive evaluations showthat modeling sequential rollouts in this space improves structural stabil-ity and action accuracy, benefiting downstream planning and navigation.Code is available at https://github.com/20robo/raenwm.

ECCV 2026

ReTarget: Representation Transformation via Adversarial Regularization for Geometric Misalignment

Jia You Chen, Shang-Tse Chen

Split inference (SI) offloads computation from edge devicesand is often considered privacy-friendly since raw inputs remain local.However, recent data reconstruction attacks (DRA) show that interme-diate features can be inverted to recover sensitive content. Existing de-fenses suppress feature information through pruning, noise injection, ordecorrelation, yet strong reconstructions persist. In this paper, we ar-gue that reconstruction vulnerability is not solely determined by mutualinformation between inputs and representations, but also by the localorganization of representations that facilitates reliable inversion in prac-tice. Therefore, we propose ReTarget, a lightweight transformation thatpreserves task-discriminative representations while perturbing inversion-consistent features. ReTarget combines adversarial reconstruction, taskpreservation, and semantic regularization training to substantially de-grade inversion performance in practice without sacrificing utility. Ex-periments on CLIP-ViTB/16, CLIP-RN50 and DINOv2 across multiplesplit points and datasets show substantial degradation in reconstructionquality. Under both state-of-the-art and adaptive attacks, particularlyat shallow split points where prior defenses fail, our method remainseffective, where achieve up to 35% improvement while maintaining com-petitive task accuracy.

ECCV 2026

ScrollScape: Unlocking 32K Image Generation With Video Diffusion Priors

Haodong Yu, Yabo Zhang, Donglin Di, Ruyi Zhang, Wangmeng Zuo

While diffusion models excel at generating images with con-ventional dimensions, pushing them to synthesize ultra-high-resolutionimagery at extreme aspect ratios (EAR) often triggers catastrophic struc-tural failures, such as object repetition and spatial fragmentation. Thislimitation fundamentally stems from a lack of robust spatial priors, asstatic text-to-image models are primarily trained on image distributionswith conventional dimensions. To overcome this bottleneck, we presentScrollScape, a novel framework that reformulates EAR image synthe-sis into a continuous video generation process through two core inno-vations. By mapping the spatial expansion of a massive canvas to thetemporal evolution of video frames, ScrollScape leverages the inherenttemporal consistency of video models as a powerful global constraint toensure long-range structural integrity. Specifically, Scanning PositionalEncoding (ScanPE) distributes global coordinates across frames to actas a flexible moving camera, while Scrolling Super-Resolution (ScrollSR)leverages video super-resolution priors to circumvent memory bottle-necks, efficiently scaling outputs to an unprecedented 32K resolution.Fine-tuned on a curated 3K multi-ratio image dataset, ScrollScape ef-fectively aligns pre-trained video priors with the EAR generation task.Extensive evaluations demonstrate that it significantly outperforms ex-isting image-diffusion baselines by eliminating severe localized artifacts.Consequently, our method overcomes inherent structural bottlenecks toensure exceptional global coherence and visual fidelity across diverse do-mains at extreme scales.

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