ECCV 2026 Papers

Creating 3D visual illusions, a single 3D mesh that revealsentirely different semantics from various viewing angles, is a fascinat-ing but tough challenge. Existing optimization-based methods are slowand can produce oversaturated colors. In contrast, naive stitching ap-proaches fail to produce geometrically coherent objects. This results invisible unnatural seams and semantic leaks. In this paper, we presenta fast and training-free framework for generating text-driven 3D visualillusions. Our approach decouples the generation into two stages. First,we propose a cross-space dual-branch denoising process. This processdynamically decodes 3D latents into voxel space for CLIP-guided orien-tation alignment and Signed Distance Field (SDF) blending, which en-sures seamless geometric fusion. Second, we introduce a view-conditionedtexture synthesis module that projects and aggregates view-specific 2Ddiffusion priors onto the fused geometry. Extensive experiments demon-strate that our method generates highly realistic, dual-semantic 3D illu-sions in just 3–5 minutes. It significantly outperforms existing methodsin geometric integrity, semantic recognizability, and efficiency. Projectpage: https://siang1105.github.io/JanusMesh.github.io/Target Junction view Target Junction view Target Junction viewview view view“A parrot”𝜃" 𝜃" 𝜃"Target Target Targetview view view𝜃! 𝜃! 𝜃!“A boat”❌ Severe over-saturation ❌ Visible backside leakage ✅ Dual-semantic coherency❌ Slow optimization (~40 min) ❌ Unnatural geometric seams ✅ Fast & Training-free (~3-5 min)(a) SDS-Based Method (b) Direct Concatenation (c) OursFig. 2: Comparison of 3D visual illusion generation methods. (a) SDS-BasedMethods suffer from severe over-saturation and slow optimization. (b) Direct Con-catenation exposes unnatural geometric seams and semantic leakage at target views(red arrows). (c) Our method creates a seamless, dual-semantic coherent 3D mesh.Unlike previous approaches, our method does not require training. It generates high-quality 3D visual illusions in just 3–5 minutes while completely preventing geometricinterference between the two semantics.

Whole slide image (WSI) analysis is central to computationalpathology, with multiple instance learning (MIL) emerging as the stan-dard pipeline for slide-level diagnosis. However, conventional approachesformulate WSI diagnosis as a flat classification task over discrete labels,contradicting the inherently hierarchical, coarse-to-fine nature of clinicalreasoning. Although recent hierarchical classifiers and vision–languagemodels (VLMs) have sought to address this structural gap, they eitherfail to capture semantic continuity between related diagnoses or sufferfrom unconstrained text generation that produces taxonomic hallucina-tions and parent–child label violations. To address these limitations, wepropose TaxoMIL, a taxonomy-constrained framework that reformulatesWSI diagnosis as a multi-granularity text generation task. TaxoMIL uti-lizes a dual-head Transformer decoder to generate coarse- and fine-leveldiagnostic text, and introduces taxonomy-guided objectives that explic-itly structure the label embedding space and strictly ground slide-level vi-sual representations within the clinical taxonomy. Extensive experimentsacross three diverse WSI datasets demonstrate that TaxoMIL consis-tently outperforms state-of-the-art MIL classifiers and VLM-based gen-erative methods, yielding accurate and hierarchy-aware diagnostic pre-dictions. The code is released at https://github.com/QuIIL/TaxoMIL..

We introduce a novel neural representation for maps between3D shapes based on flow-matching models, which is computationally ef-ficient and supports cross-representation shape matching without large-scale training or data-driven procedures. 3D shapes are represented asthe probability distribution induced by a continuous and invertible flowmapping from a fixed anchor distribution. Given a source and a targetshape, the composition of the inverse flow (source to anchor) with theforward flow (anchor to target), we map points between the two surfaces.By encoding the shapes with a pointwise task-tailored embedding, thisconstruction provides an invertible and modality-agnostic representationof maps between shapes across point clouds, meshes, signed distancefields (SDFs), and volumetric data. The resulting representation con-sistently achieves high coverage and accuracy across diverse benchmarksand challenging settings in shape matching. Beyond shape matching, ourframework shows promising results in other tasks, including UV mappingand registration of raw point cloud scans of human bodies.

Multimodal Large Language Models (MLLMs) have demon-strated remarkable abilities when analyzing images, yet translating thesecapabilities to few-shot image classification remains challenging. To bridgethis gap, we present DeCoDe, a simple yet effective technique that en-ables off-the-shelf MLLMs to act as strong few-shot classifiers withoutany additional training. Our approach builds on the idea of few-shotclassification as a set of pairwise image comparisons, decomposing thetask into a set of binary decisions. Given a query image and a supportimage from a candidate class, the MLLM is prompted to decide whetherthe two images depict the same class. The logit corresponding to an af-firmative response is then used as a similarity score to assign the queryimage to the most likely class. While this already yields good results,we show that providing additional high-level information, such as thedata domain, to the model further improves performance. Our evalu-ation provides an extensive analysis of various inference variants on asuite of twelve datasets, six established and six newly curated few-shotbenchmarks spanning across diverse domains. The results show that theproposed simple decomposition technique can turn off-the-shelf MLLMsinto powerful few-shot learners, significantly outperforming current state-of-the-art few-shot methods on both standard and novel domains. Codeis available at https://github.com/yunhanwang1105/DeCoDe.

Vision Language Models (VLMs) have shown promising plan-ning capabilities, yet their success remains confined to the text domain,leaving visual decision-making relatively underexplored. Addressing thisgap, we introduce Corrective Sequence Planning (CoSPlan) bench-mark, where VLMs must plan a sequence of visual actions from an initialscene to a target scene. CoSPlan evaluates models on their ability to imag-ine and execute a coherent set of visual steps required to reach the goal(Step Completion). To prevent any shortcuts that simply describethe final scene, we introduce an erroneous action in decision making,which must be detected (Error Detection) and corrected to reach thegoal, enabling a deeper understanding of the task. CoSPlan spans across4 tasks: maze navigation, block re-arrangement, image reconstruction,and object re-organization. Despite using advanced reasoning strategiessuch as Chain-of-Thought and Scene Graphs, VLMs struggle on CoSPlan,while still showing promising performance in the text domain. Addressingthis, we propose Scene Graph Incremental updates (SGI), a noveltraining-free method to transform images into ‘textual’ scene graphs, en-abling step-by-step reasoning through iterative scene graph refinement.SGI yields an average of ≃ 4.4% ↑ on CoSPlan w/ generalization onPlanBench and VQA. Link for solving puzzles on the project page.

Sparse autoencoders (SAEs) are increasingly used to extract interpretable concepts from vision and vision language models, yet existing evaluation methods largely rely on proxy metrics or qualitative inspection rather than measuring semantic correspondence. We present a human-grounded evaluation framework that quantifies alignment between SAE latents and human-annotated concepts, without requiring user studies, and validate this matching through targeted attribute perturbations. To enable this intervention-style evaluation in vision, we construct synCUB and synCOCO, synthetic benchmarks of paired images that differ in exactly one attribute. We introduce Fully-Binary Matching Pursuit (FBMP), a coalition-based matching procedure that supports many-to-one mappings between SAE latents and annotated concepts, and consistently outperforms one-to-one baselines. For functional validation, we propose a Targeted Attribute Perturbation Alignment Score (TAPAScore), which tests whether matched concepts respond selectively and in the expected direction under targeted image-level attribute perturbations. Under sanity checks, our matching and TAPAScore are the only evaluated metrics that reliably distinguish trained SAEs from untrained ones. Across SAEs trained on CLIP and DINOv2 embeddings, we find that increased overcompleteness can reduce perturbation alignment, indicating a reduction in interpretability. Our evaluation framework suggests that moderate dictionary sizes provide the best trade-off, yielding the most interpretable SAEs. Code and datasets are available at https://github.com/JonasKlotz/sae-concept-eval.

Deep neural networks remain vulnerable to backdoor at-tacks, in which adversaries embed hidden triggers during training tocause misclassification of any trigger-carrying input into an attacker-chosen target class, while maintaining normal behavior on clean inputs.Though recent fine-tuning-based defenses have shown promise in miti-gating backdoor attacks and are particularly practical in continual learn-ing and MLaaS scenarios, where backdoors are removed from pretrainedmodels using small clean datasets, we demonstrate that this defensiveparadigm remains fundamentally exploitable by an adaptive adversary.We study a threat model in which attackers anticipate fine-tuning-baseddefenses and craft backdoors whose resulting optimization signals areentangled with those of the clean task. Our approach leverages FisherInformation to identify task-critical neurons and optimizes a trigger gen-erator such that backdoor gradients align with benign gradients on theseneurons, ensuring that any attempt to suppress the backdoor inevitablyinterferes with clean task preservation. As a result, fine-tuning defensesfails to effectively reduce attack success rate without incurring substan-tial degradation in clean accuracy. Through extensive evaluation againstseven attacks and eight defenses, we show our method consistently showsa stronger effect than existing backdoor attacks across all settings. Ourwork reveals a critical vulnerability in fine-tuning-based defenses andhighlights the need for more robust defense mechanisms against adap-tive adversaries.

Video diffusion models have recently been applied with success to problems in content generation, novel view synthesis, and, more broadly, world simulation. Many applications in generation and transfer rely on conditioning these models, typically through perceptual, geometric, or simple semantic signals, fundamentally using them as generative renderers. At the same time, high-dimensional features obtained from large-scale self-supervised learning on images or point clouds are increasingly used as a general-purpose interface for vision models. The connection between the two has been explored for subject specific editing, aligning and training video diffusion models, but not in the role of a dense conditioning signal for pretrained video diffusion models. Features obtained through self-supervised learning like DINOv3, contain a lot of entangled information about style, lighting and semantics of the scene. This makes them great at reconstruction tasks but limits their generative capabilities. In this paper, we show how we can use the features for tasks such as video domain transfer and video-from-3D generation. We introduce a lightweight control architecture and training strategy that decouples appearance from other features that we wish to preserve, enabling robust control for appearance changes such as stylization and relighting. Furthermore, we show that low spatial resolution can be compensated by higher feature dimensionality, improving controllability in generative rendering from explicit spatial representations.

Advances in neural rendering have enabled high-fidelity multi-view reconstruction of 3D scenes. However, free-form non-rigid shapeediting remains a significant challenge. Point-based neural representationsare highly desirable for multi-view reconstruction because they lack fixedconnectivity, which does not constrain the learned surface topology tothat of the initialization. Yet this same property causes point-based rep-resentations to struggle with holes and surface discontinuities under largedeformations. To address this, we propose a novel self-supervised methodto enable point-based representations to adapt to large deformations with-out requiring ground truth multi-view images of deformed geometry. Thekey idea is to generate random deformations and to ensure consistencyin the predicted surface before and after deformation. In particular, thesurface prediction from the deformed point cloud should be the sameas the deformation applied to the surface prediction from the originalpoint cloud. We incorporate our approach into attention-based pointrepresentations, which differ from splatting-based point representationsin their use of a learned interpolation kernel between points as opposedto a Gaussian kernel around each point. This learned interpolation kernelcan learn to adapt to large deformations, without requiring addition orremoval of points. We show that our framework significantly enhancesits robustness to large deformations. Experiments on synthetic geometryediting benchmarks (Neural Editor, Objaverse) demonstrate that ourapproach outperforms existing point-based methods in zero-shot editingand significantly reduces artifacts. Furthermore, qualitative results on theDTU and Mip-NeRF 360 datasets demonstrate our method’s effectivenesson real-world scenes.

Recent point-based intrinsic decomposition and inverse ren-dering methods have advanced the modelling of the shading and albedoof 3D scenes. However, we identify a fundamental limitation: these meth-ods suffer from a misattribution issue, where individual primitives learnincorrect appearance features despite producing correct aggregated ren-derings. We show that the root cause lies in volume rendering, whichaggregates translucent primitives along each ray and only supervises thefinal colour, preventing direct supervision of individual primitive fea-tures. To address this, we propose Intrinsic PAPR, a robust intrinsic de-composition framework which leverages Proximity Attention Point Ren-dering (PAPR) to enable direct per-point supervision. Unlike volumerendering approaches, PAPR eliminates translucent primitives and di-rectly predicts appearance at ray-surface intersections, enabling accuratesupervision to the feature of each individual point. Our method incor-porates a 2D albedo prior adapted with conditional Implicit MaximumLikelihood Estimation (cIMLE) to handle monocular ambiguities, andemploys a space carving loss to ensure multi-view consistency. Exten-sive evaluations on synthetic and real-world datasets demonstrate thatIntrinsic PAPR outperforms point-based inverse rendering, NeRF-basedintrinsic decomposition, and diffusion-based PBR methods in novel viewsynthesis and albedo estimation while resolving the misattribution issue.

Recent advances in Image-to-Video (I2V) generation allow asingle image to be animated into a convincing video under text guidance,raising serious copyright and privacy risks. We propose Anti-Prompt,an image protection approach that injects imperceptible perturbationsinto an image, inducing visible inconsistencies and structural failures intext-guided I2V generation. Our method is motivated by a simple em-pirical observation: when text guidance is removed from modern I2Vmodels, generation quality degrades markedly, not only in motion re-alism but also in subject preservation, structural coherence, and tem-poral consistency. Building on this insight, Anti-Prompt exploits themodel’s reliance on textual guidance by attenuating text-conditioned in-teractions during denoising while strengthening visual-only pathways.To further systematically evaluate protection effectiveness, we introducea Video-LLM–assisted evaluation protocol that provides interpretable,frame-grounded analyses of generation artifacts and inconsistencies. Ex-periments on two representative I2V architectures demonstrate that ourmethod achieves strong protection performance while improving effi-ciency and cross-model transferability.

Workflow generation in visual creation systems such as Com-fyUI demands not only syntactic accuracy but also expert-level reason-ing over modular compositions. Existing large language model (LLM)approaches often treat this as a direct text-to-JSON generation task,struggling with structural brittleness and lacking the experiential knowl-edge required for effective design. We argue that successful workflowgeneration requires modeling knowledge itself, including its structure,hierarchy, and reasoning dynamics. To this end, we propose a knowledge-centric framework that learns to invert, inject, and infer with knowl-edge across multiple abstraction levels. We first perform knowledge in-version to distill hierarchical representations, ranging from full pseudo-codes and skeletons to high-level strategies, from large collections of real-world workflows. We then conduct knowledge injection through super-vised fine-tuning, teaching the model to reason from task descriptionsto strategies and from strategies to executable structures. During infer-ence, the model performs reversible reasoning to synthesize executableworkflows, augmented by self-refinement for structural coherence. Ex-tensive experiments demonstrate that our method produces workflowswith richer node diversity, more coherent structures, and higher execu-tion success rates than existing systems, establishing a new foundationfor knowledge-driven, agentic workflow generation.

Measuring structured object understanding in vision founda-tion models remains challenging due to inconsistent evaluation protocolsand limited part-level supervision. Semantic correspondence (SC) eval-uates this capability by testing whether object parts can be matchedacross instances and categories under large variations in appearance,viewpoint, and geometry. To enable a systematic SC evaluation, we in-troduce SOCO, a new benchmark for Semantic Object Correspondencethat introduces a taxonomy of correspondence types and provides con-sistent, functionally meaningful keypoint annotations across 100 cate-gories and over 1M correspondence pairs. In addition, SOCO includeskeypoint language descriptions, enabling the evaluation of large vision-language models (LVLMs) and their fine-grained part-level understand-ing. Comprehensive experiments reveal that (i) vision foundation back-bones encode strong semantic structure but transfer correspondencespoorly across related categories and only partially capture object-partposition, (ii) LVLMs are stronger at text-prompted part localizationthan at visual-reference cross-image matching, exposing a gap betweenlanguage-grounded localization and fine-grained visual correspondence,and (iii) correspondence performance predicts dense downstream tasks—segmentation, tracking, 3D pose estimation, and 3D detection—morestrongly than ImageNet classification. Together, these findings positionSOCO as a benchmark for structured, part-level representation qualityin vision and multimodal foundation models.

Whole-slide images (WSIs) are widely used for computa-tional cancer prognosis. However, current research primarily focuses onin-domain performance and fails to generalize across clinical centers. Thislimitation stems from their reliance on pixel-derived representations thatare highly susceptible to domain-specific artifacts caused by stainingprotocols and scanner hardware. We hypothesize that high-level pathol-ogy semantics, such as tumor grade and micro-environmental architec-ture, provide a domain-invariant semantic representation that mirrorsthe robust diagnostic logic of human pathologists. Therefore, we proposea Semantic-Anchored Evidential Fusion Survival (SAEFS) framework,where SAEFS derives semantic anchors from WSIs via Visual QuestionAnswering (VQA), employs a dual-stream WSI evidence extraction archi-tecture, uses Dirichlet-based Subjective Logic to model uncertainty, andfuses semantic and visual evidence through a cautious conjunction rule toavoid overconfident fusion from correlated sources. Trained exclusively onone source domain and evaluated zero-shot across four unseen domains,SAEFS consistently outperforms state-of-the-art models both in predic-tion accuracy and reliability, improving the average C-index by 10.2%.Quantitative analyses further show that VQA-derived semantic featuresexhibit significantly lower cross-center divergence than pixel-derived fea-tures, highlighting their robustness for cross-center clinical applications.The code is available at https://github.com/YuchengXing99/SAEFS.

While Vision-Language Models (VLMs) have significantly advanced remote sensing interpretation, enabling them to perform complex, step-by-step reasoning remains highly challenging. Recent efforts to introduce Chain-of-Thought (CoT) reasoning to this domain have shown promise, yet ensuring the visual faithfulness of these intermediate steps remains a critical bottleneck. To address this, we introduce GeoSolver, a novel framework that transitions remote sensing reasoning toward verifiable, process-supervised reinforcement learning. We first construct GeoPRM-2M, a large-scale, token-level process supervision dataset synthesized via entropy-guided Monte Carlo Tree Search (MCTS) and targeted visual hallucination injection. Building upon this dataset, we train GeoPRM, a token-level process reward model (PRM) that provides granular faithfulness feedback. To effectively leverage these verification signals, we propose Process-Aware Tree-GRPO, a reinforcement learning algorithm that integrates tree-structured exploration with a faithfulness-weighted reward mechanism to precisely assign credit to intermediate steps. Extensive experiments demonstrate that our resulting model, GeoSolver- 9B, achieves state-of-the-art performance across diverse remote sensing benchmarks. Crucially, GeoPRM unlocks robust Test-Time Scaling (TTS). Serving as a universal geospatial verifier, it seamlessly scales the performance of GeoSolver-9B and directly enhances general-purpose VLMs, highlighting its remarkable cross-model generalization. The code will be available at https://github.com/minglangL/GeoSolver.

Parsing images with high precision for matting and segmentation is sig-nificantly more challenging than conventional dense prediction, as it requires accurateestimation of fine-grained details. Existing methods either lack semantic awareness orproduce suboptimal predictions; even interactive matting approaches rely on manualverification and repetitive checking, making fully automatic matting still unattainable.In this work, we propose Kiroshi , an agentic perception system for high-accuracyimage parsing. We train an Action Model with iterative refinement and mine pairedtrajectories by sampling grid prompts from residual maps, where each step yields apositive and a negative transition under the same intermediate prediction based onquantitative quality gains. These within-context preference pairs form a reliable su-pervision signal for post-training to align the MLLM policy toward more effectivegrid decisions. We also contribute a new High-Fidelity Referring Matting and Seg-mentation (HiFiRefMS ) benchmark to evaluate the performance of different mod-els. Experimental results demonstrate that our method surpasses state-of-the-art ap-proaches both quantitatively and qualitatively, and extensive ablation studies furthervalidate the effectiveness and superiority of our agentic design. Project will be releasedat https://github.com/haipengzhou856/Kiroshi.

Image restoration is fundamentally constrained by the tradeoff between distortion and perception: minimizing pixel-wise error yields over-smoothed results, whereas optimizing for perceptual realism often introduces structural deviations. Recent approaches attempt to balance this tradeoff via posterior sampling or multi-stage generative pipelines, yet remain computationally expensive and architecturally complex. To overcome these limitations, we propose PCFlow (Perceptually Consistent Flow Matching), a unified framework that directly parameterizes a continuous transport from degraded observations to clean targets, jointly optimizing distortion and perceptual quality. While its latent consistency flow objective drives stable and efficient few-step inference, a Latent Consistency Perceptual Loss (LCPL) imposes semantic constraints directly on the guiding velocity field, steering the dynamics toward visually sharp data manifolds. Furthermore, recognizing the inherent conflict between structural and perceptual consistencies, we integrate a conflict-free gradient projection strategy to stabilize the multi-objective optimization landscape. Combined with lightweight, convolution-only backbone, PCFlow achieves competitive performance across diverse restoration tasks at a fraction of traditional computational costs.

Face anti-spoofing (FAS) is critical for secure deploymentof face recognition in high-stakes applications. However, existing multi-modal FAS methods still generalize poorly to unseen domains. We arguethat a key reason is that they directly align noisy multimodal featuresin which spoof-relevant cues are entangled with modality- and domain-specific variations, so both useful and nuisance patterns are forced tomatch across domains. Motivated by this, we design Multimodal Denoisingand Alignment (MMDA), a CLIP-based framework that (i) purifiesmultimodal features, (ii) softly guides them into a semantic space, and(iii) preserves the pre-trained geometry during task adaptation. To de-couple spoof cues from these variations before alignment, the Modality-Domain Joint Differential Attention (MD2A) module contrasts same-domain cross-sample features to down-weight patterns shared withineach domain/modality, which helps reduce domain- and modality-relatedbiases while better preserving discriminative spoof information. To avoiddestructive, over-rigid alignment, the Representation Space Soft (RS2)strategy aligns visual features to real/spoof subspaces spanned by mul-tiple prompts in the CLIP space, providing a soft semantic pull insteadof collapsing features onto a single text embedding. To prevent task-specific fine-tuning from destroying CLIP’s generalizable structure, theU-shaped Dual Space Adaptation (U-DSA) module introduces deepyet parameter-efficient adaptation and remaps adapted features backto shallower, more domain-invariant layers. Extensive experiments onWMCA, CeFA, PADISI, and SURF under complete-modality, missing-modality, and limited-source protocols show that MMDA consistentlysurpasses state-of-the-art methods, reducing average HTER by up to9.63% and improving AUC by up to 5.98% in cross-domain evaluation.

Vision-language models can produce confident answers on visually ambiguous inputs, resulting in biased predictions. Common entropybased methods, such as Semantic Entropy (SE), rely on output diversity. Yet our analysis shows that overconfident visual embeddings suppress output diversity under stochastic decoding, causing SE to underestimate uncertainty in such cases. Recent methods instead probe output diversity through input perturbations, including textual paraphrasing or joint text-image perturbations, and show improved performance. We study these approaches and reveals that the resulting variability is often dominated by textual changes rather than visual evidence, causing uncertainty estimates to reflect prompt sensitivity rather than visual ambiguity. We therefore propose Visual Semantic Entropy (VSE), which perturbs only the image to probe nearby visual variations while keeping the text query fixed. VSE measures uncertainty by clustering generated answers into semantic prototypes and computing the massweighted dispersion among them. Extensive evaluation across five modern vision-language models and five diverse VQA benchmarks demonstrates that VSE effectively captures visual ambiguity, establishing a new state-of-the-art for VLM uncertainty estimation. Code is available: https://github.com/tadeephuy/visual-semantic-entropy