ECCV 2026 Papers

Surface geometry and multispectral reflectance estimation are essential tools for reliable physical scene understanding in applications such as industrial inspection, cultural heritage analysis, and remote sensing. To obtain dependable spectral and geometric information, conventional pipelines often require multiple multispectral images captured across viewpoints, or multispectral photometric stereo with repeated, sequential illuminations. These approaches are frequently bandwidthintensive and slow to acquire due to their staged capture protocols. We propose EventSpecPS, an event-based multispectral photometric stereo system. It comprises a Spectrally-Angularly Multiplexed Encoding Device that encodes geometric and spectral cues into the event stream, which are then decoded by an Event-based Spectral-Angular Decoder to jointly recover surface normals and absolute multispectral reflectance. This enables single-stage, high-speed, and low-bandwidth acquisition and reconstruction, while remaining robust to cast shadows and specularities in practice. Extensive experiments on both synthetic and real data demonstrate state-of-the-art performance over existing methods. Code and data are available at https://github.com/Wujingqian/EventSpecPS.

While image stylization has been studied extensively, video stylization remains a critical and largely unsolved challenge in the field of intelligent content creation. Existing methods, usually utilizing a reference image as the style prior, suffer from content leakage, data scarcity and limited adaptability to long videos, leading to suboptimal results with severe style drift and motion distortion. For these issues, we present EchoStyle, a scalable text-driven framework to achieve high-quality stylization of videos with arbitrary lengths. To start with, we construct a video-to-video architecture to appropriately re-fuse the video content and the text style. To address data scarcity, we pioneer an automatic reverse-synthesis pipeline to establish V-Style20k, a large-scale stylization dataset of 20k high-quality video pairs. To facilitate long video stylization, we devise an init-follow-mode mechanism along with a slidingwindow inference strategy. Extensive experiments demonstrate EchoStyle’s excellent performance across a wide range of artistic styles, even comparable to leading closed-source solutions.

In this paper, we tackle the problem of performing consistent,unified modifications to a multi-shot video sequence. This task is par-ticularly challenging because multi-shot videos consist of discontinuoustemporal segments that vary significantly in viewpoint, camera scale, andsubject pose, leading to severe identity drift and cumulative error propa-gation. Achieving coherent edits requires establishing reliable cross-shotsemantic awareness to maintain stable subject appearance and visualcontinuity across these disjointed boundaries. To address this, we proposeMSEditor, the first framework designed specifically for consistent multi-shot video editing. To overcome the scarcity of high-quality multi-shottraining data, we repurpose existing multi-view video datasets to providerobust cross-shot supervision. Architecturally, we introduce a Supervi-sory Adapter that injects this cross-shot information into the diffusionbackbone, enabling the model to learn identity-consistent representa-tions. Furthermore, to effectively mitigate cumulative errors and ensurelong-range temporal coherence, we design a Cross-Shot Packing strat-egy that dynamically aggregates information from semantically relatedshots within the self-attention window. Extensive experiments demon-strate that MSEditor significantly outperforms existing methods on ourcurated multi-shot video editing benchmark in terms of identity preser-vation, temporal stability, and overall visual quality.

Previous works based on Segment Anything Model (SAM)have achieved promising performance in unified scene text detection andlayout analysis. However, the typical reliance on pixel-level text segmen-tation for sampling thousands of foreground points as prompts leadsto unsatisfied inference latency and limited data utilization. To addressabove issues, we propose ET-SAM, an Efficient framework with twodecoders for unified scene Text detection and layout analysis based onSAM. Technically, we customize a lightweight point decoder that pro-duces word heatmaps for achieving a few foreground points, thereby elim-inating excessive point prompts and accelerating inference. Without thedependence on pixel-level segmentation, we further design a joint train-ing strategy to leverage existing data with heterogeneous text-level an-notations. Specifically, the datasets with multi-level, word-level only, andline-level only annotations are combined in parallel as a unified trainingset. For these datasets, we introduce three corresponding sets of learn-able task prompts in both the point decoder and hierarchical mask de-coder to mitigate discrepancies across datasets. Extensive experimentsdemonstrate that, compared to the previous SAM-based architecture,ET-SAM achieves about 3× inference acceleration while obtaining com-petitive performance on HierText, and improves an average of 11.0% F-score on Total-Text, CTW1500, and ICDAR2015. The code and modelsare available at ET-SAM.

Transformer has recently garnered significant interest in un-supervised domain adaptation tasks due to its superior generalizationability. State-of-the-art methods leverage masked image modeling withconsistency regularization to improve target domain performance. How-ever, such strategy becomes less effective when the domain gap becomeslarge, as the domain gap between masked domain and original domainis not minimized. How to address the adaptation problem when domaingap becomes large is an important research problem in domain adap-tation. In this paper, we propose Dual Masked Generative AdversarialTransformer (Dual-MGAT), which simultaneously aligns the maskedtarget and masked source domain towards source and target domainto address the large domain gap problem. Specifically, we formulate ageneral framework for masked domain learning. To reduce the domaingap between the masked domain (e.g. masked target) and the originaldomain (e.g. source), we note that there exists two types of discrepan-cies, namely the information gap due to masked image modeling and thedomain distribution discrepancy. To this end, we propose the maskedgenerative adversarial adaptation technique, which introduces a trans-former decoder to bridge the masked feature space towards the originalfeature space via [CLS] token feature generation and utilizes a class con-ditional domain discriminator for adversarial distribution alignment toreduce the domain distribution discrepancy. We further investigate thedual branch of masked source to target domain adaptation, which boostsperformance. We provide a theoretical analysis of our framework from amasked domain adaptation perspective. Extensive experimental studiesdemonstrate the superiority of our framework.

Document layout understanding has long relied on bound-ing boxes with non-hierarchical and fixed label spaces. This abstractionfails on irregular regions, discards structural hierarchy, and falls shortof what real-world document tasks demand. We present DocLayout-VL, a foundation model that reframes document layout understandingas promptable open-set hierarchical segmentation. DocLayout-VL pro-duces a hierarchical layout tree, a pixel-accurate mask for each region,and a natural language explanation. Our novel Hierarchical GroundingContextualizer introduces Hierarchical Contextual Attention which mod-els inter-region relationships across hierarchical levels. DocLayout-VLis instruction-tuned on diverse layout tasks and domain-specific ontolo-gies. This enables highly customizable layout understanding via natu-ral language prompts, without any task-specific fine-tuning. To supportfoundation-scale training, we introduce HOPLay-Docs-80M, a large-scale dataset of 2.5M pages, 80M pixel-level masks, 400k open-set labelsand 5M promptable annotations across 12 domains and 58 documenttypes. We also release HOPLay-Docs-Bench, a human-verified evalu-ation suite covering open-set, hierarchical, and promptable layout seg-mentation. DocLayout-VL achieves state-of-the-art results across allbenchmarks, outperforming both specialized layout models and strongmultimodal baselines. Our work establishes a new paradigm for control-lable, semantically grounded layout understanding.

Open-vocabulary semantic segmentation (OVSS) leveragestextual semantics to segment objects beyond predefined categories. Whilethe self-supervised model DINOv3 provides strong structured visual rep-resentations, its lack of native textual alignment hinders direct applica-tion to OVSS. To bridge this gap, we propose DINOde, an ODE-basedframework that continuously aligns CLIP text embeddings to the DINOvisual manifold. Our approach employs two complementary components:(i) Semantic Text Flow (STF), which evolves text embeddings toward theDINO manifold through a continuous ODE trajectory, and (ii) GlobalContext Flow (GCF), which progressively refines the holistic image rep-resentation carried by DINO’s CLS token. To preserve the hypersphericalgeometry of the feature space during this evolution, we further intro-duce Velocity Tangent Projection, which constrains the learned veloc-ity field to the tangent space through projection. By modeling align-ment as a continuous trajectory, DINOde avoids the manifold entan-glement inherent in discrete MLP projections and yields more robustcross-modal alignment. Extensive experiments demonstrate that DIN-Ode consistently outperforms existing methods and achieves state-of-the-art performance across multiple OVSS benchmarks. The code is availableat https://github.com/yoon307/DINOde.

While large language models (LLMs) have advanced chestCT report generation, existing methods typically encode 3D volumesholistically, failing to distinguish informative cues from redundant anatom-ical background. Inspired by radiological cognitive subtraction, we pro-pose Differential Visual Prompting (DiffVP), which conditions reportgeneration on explicit, high-level semantic scan-to-reference differencesrather than solely on absolute visual features. DiffVP employs a hier-archical difference extractor to capture complementary global and localsemantic discrepancies into a shared latent space, along with a difference-to-prompt generator that transforms these signals into learnable visualprefix tokens for LLM conditioning. These difference prompts serve asstructured conditioning signals that implicitly suppress invariant anatomywhile amplifying diagnostically relevant visual evidence, thereby facil-itating accurate report generation without explicit lesion localization.On two large-scale benchmarks, DiffVP consistently outperforms priormethods, improving the average BLEU-1-4 by +10.98 and +4.36, respec-tively, and further boosts clinical efficacy on RadGenome-ChestCT (F1score 0.421). Code will be available at https://github.com/ArielTYH/DiffVP/.

Recent advances in large-scale image generative models en-able photorealistic scene synthesis with controllable attributes. Beyonddata augmentation, their potential as diagnostic tools for trained visionsystems remains unexplored in the aerial and remote sensing domains.We introduce a synthetic diagnostic framework for aerial-view vehicle de-tection that combines text-guided generation, attribute-controlled edit-ing, and automated attribute verification to construct a controllable syn-thetic testbed. This enables fine-grained evaluation of pretrained detec-tors under diverse scene types and environmental conditions that aredifficult to isolate in real datasets. Across three detection architecturesand three real aerial datasets, synthetic scene-wise performance trendsclosely match real-world weaknesses. Guided by these diagnostics, tar-geted supplementation with small real datasets from the identified weakcategories yields improvements of up to 13% AP50 while requiring sub-stantially fewer additional samples than non-targeted augmentation. Ourresults show that controlled synthetic probing can predict real-domainperformance gaps and guide efficient data collection. The proposed diag-nostic framework is modular and can incorporate alternative generativeor vision-language models as capabilities evolve. Our code and datasetsare available here: humansensinglab.github.io/AVODDiag/

Video understanding benchmarks have long centered on sin-gle-camera settings, where modern multi-modal language models achievestrong performance across image and video tasks. Yet, the real world runson multi-camera networks: autonomous vehicles, security systems, androbots all gather data across many simultaneous views. We argue thatthis is not simply “more” of the single-camera problem; it is fundamen-tally different. Multi-camera reasoning requires handling context thatscales with the number of views, resolving occlusions visible from only asubset of cameras, judging which views matter, and integrating evidenceacross perspectives that may overlap or diverge. Current models strugglewith exactly these challenges, yet no benchmark systematically targetsthem. We introduce CrossView, a multi-camera video question-answeringbenchmark spanning autonomous driving, security surveillance, egocen-tric/exocentric video, and robotics. Evaluation of proprietary models,such as GPT-5.2, and open-source models, like Qwen3-VL, reveals con-sistently low accuracy, with open-source models trailing by a wide mar-gin. Performance scales strongly with a model’s ability to jointly pro-cess multiple viewpoints, positioning CrossView as a rigorous bench-mark for multi-camera video. We open-source our code and dataset athttps://utaustin-swarmlab.github.io/CrossView.

Modern one-step diffusion models achieve impressive qual-ity through distribution-based timestep distillation. Yet, they rely on acritical assumption: Teacher and Student must inhabit the same latentspace. This Shared-Space constraint prevents knowledge transfer frommodern high-capacity Teachers (e.g., SD 3.5 and Flux) into compact,deployment-friendly Students such as SD 1.5, whose latent resolutionand VAE parameterization differ from the Teacher. We formalize thisoverlooked regime as Cross-Space Distillation, where Teacher andStudent differ in both latent resolution and VAE space. To enable distil-lation under this mismatch, we introduce the Bridge (Bϕ ), a lightweightlatent interface that maps Student latents into the Teacher space with-out modifying the Student backbone. Bridge combines a frozen StudentVAE decoder as a spatial prior with a compact learnable projector, andis trained with latent reconstruction and attention fidelity objectives forstable Teacher-space alignment. Across diverse modern Teachers, Bridgeenables substantial gains for compact one-step Students; for example, itimproves SD 1.5 from 5.4 to 9.4 HPSv3 while preserving one-step in-ference, low latency, and broad ecosystem compatibility. These resultsshow that heterogeneous large Teachers can be distilled into efficient,deployable backbones through a lightweight latent-space interface.

With the recent rapid progress in generative models, im-age editing has made remarkable advances, yet achieving faithful editsthat precisely modify only the target regions while strictly preservingall other regions remains challenging. Since externally provided regionannotations are often difficult to obtain in practice, a growing body ofwork seeks to improve preservation by automatically inferring edit andnon-edit regions, and then enforcing consistency on the latter. However,these approaches still suffer from inaccurate region estimation and heuris-tic correction strategies that distort the native inference process, makingmethods designed for fidelity themselves a new source of artifacts. Wepropose SR-Edit, an image editing framework that overcomes these is-sues via iterative self-refinement. Specifically, at each iteration, SR-Editfirst (i) extracts progressively precise and self-consistent region separa-tion from the model’s own predictions by lightweight post-processing,and then (ii) enforces preservation in non-edit areas through correctionupdates that remain aligned with the original sampling dynamics. Ex-tensive experiments demonstrate that SR-Edit achieves superior preser-vation and overall image quality compared to existing editing techniques.

3D Gaussian Splatting (3DGS) enables photorealistic real-time novel view synthesis, yet placing a virtual camera to capture a de-sired frame remains largely manual. Existing language-guided approachesin 3D scenes mainly focus on object-centric grounding, determining whatto observe but rarely controlling how it should appear in a single frame,such as subject orientation or frame layout. To address this limitation,we introduce a new task, Text-Instructed Viewpoint Grounding (TIVG),which aims to identify a 6-DoF camera pose in a 3D Gaussian scenewhose rendered frame aligns with a text instruction. To solve this task,we propose CapFrame, a partially differentiable framework that con-verts language into geometric pseudo labels for camera pose optimization.CapFrame follows a Retrieve–Translate–Refine pipeline: it retrieves rele-vant views and ranks them through a Question-Evaluation process withMLLMs, translates the instruction into orientation and layout pseudo la-bels, and refines the camera pose via differentiable optimization with lay-out and orientation losses in 3DGS. Experiments on 38 real-world sceneswith 135 instructions indicate that CapFrame produces viewpoints betteraligned with texts than heuristic viewpoint search and adapted trajectorygeneration baselines, validated by VLM metrics, MLLM judges, and userstudies. Code is available at: https://github.com/jirongli/CapFrame

Autonomous driving operates in partially observable envi-ronments where actors may become fully occluded by other vehicles orinfrastructure. Most end-to-end driving systems implicitly couple actorexistence to instantaneous observations, causing actor hypotheses to de-grade or disappear during prolonged occlusion and removing potentiallycritical agents from downstream prediction and planning. We introduceBeyondSight, a permanence-aware end-to-end driving framework thatdecouples actor existence from observability by maintaining persistentactor hypotheses over time. BeyondSight propagates actor queries tem-porally and updates them with observation-conditioned evidence, en-abling joint perception, prediction, and planning to reason about ac-tors even when they are temporarily unobservable. To enable principledtraining and evaluation of persistence-aware models, we further introducenuScenes-Permanence, an extension of nuScenes that provides super-vision and observability-conditioned evaluation for unobservable actors.Experiments show that BeyondSight substantially improves reasoningunder occlusion, increasing detection performance for unobservable ac-tors from 0 to 0.249 mAP while reducing planning error from 0.61 to0.54 L2avg . These results highlight object permanence as an importantmodeling principle for robust end-to-end autonomous driving.

Video emotion analysis is typically framed as a static clas-sification problem, treating each clip as an independent labeled unit.However, such a formulation overlooks a key psychological fact: emotionschange as a result of cumulative reactions to consecutive causal events. Tobridge this gap, we introduce DAR (Dynamic Affective Reasoning), thefirst large-scale benchmark for viewer-centric affect transitions and causalreasoning over consecutive video events. DAR contains 15,087 videos and36,908 event-aligned affective segments annotated with 27 emotion cat-egories. Unlike existing video-based emotion datasets, DAR presents anew viewer-centric perspective on fine-grained emotional expressions andtransitions, and provides dense, temporally grounded, and causally ex-plicit reasoning chains. Based on DAR, we formally define three challeng-ing tasks: affective segmentation, fine-grained emotion classification, andaffective reasoning. Complementing this benchmark, we propose DAR-R1, a two-stage framework that combines supervised fine-tuning withGroup Relative Policy Optimization. Experiments across 10+ MLLMsshow that DAR-R1 sets a new state-of-the-art for dynamic affective rea-soning, in terms of both emotional localization and affective reasoning.Project page: https://github.com/Zhang-Zhiyan/DAR.

Understanding a painting is never a single act. Art histori-ans may analyze the same work through concepts of style, iconography,or historical context, dimensions that are not interchangeable, and eachcarries distinct semantic relationships between the visual and the tex-tual. Vision-Language Models (VLMs) like CLIP, which learn a singleshared embedding space, collapse this richness into a single homogeneousalignment, thereby losing the multi-relational structure that defines art-historical reasoning. We introduce CANVAS (Contrastive Art-awareNetwork for Vision-Language Alignment with Sheaves), a frameworkfor learning relation-aware multimodal representations inspired by sheaftheory. Each artwork is projected into multiple embeddings conditionedon the type of relation (i.e., the context), and a novel contrastive lossencodes contextual information during training, with no dependency onexternal data at inference. We evaluate on three newly introduced bench-marks of artworks for multi-relational art understanding: WikiArt+,derived from WikiArt and Wikipedia, HertzianaDP, from the Biblio-theca Hertziana collection, and SemArt+, refined from the SemArtdataset. In multimodal retrieval and art understanding, CANVAS out-performs the baselines, supporting the view that multi-relational align-ment is not just theoretically motivated but also practically essential.

Zero-shot anomaly detection aims to identify defects in ar-bitrary novel domains; however, existing models assume that the auxil-iary data contains a rich diversity of anomalies, neglecting the far morecomplex and unpredictable variations in real-world target domains. Thisstudy introduces DIVE, the first approach to investigate the scenario oflimited auxiliary anomaly priors and resolve the resulting substantial per-formance degradation. Through a shallow-and-deep text embedding in-jection strategy during visual encoding, DIVE learns to abstract genericanomaly concepts shared across the auxiliary training domain and di-verse target domains. Moreover, we propose a disentanglement mech-anism to tackle the suboptimal alignment between visual embeddingsentangled with object semantics and object-agnostic textual prompts.Experiments demonstrate that, under the setting of limited anomalypatterns in auxiliary data, DIVE outperforms SOTA baselines by up to16.2% and 28.5% on two classification metrics, and 23.4%, 24.1%, and47.0% on three segmentation metrics, in terms of average performanceacross twelve datasets. Furthermore, it maintains highly competitive per-formance when auxiliary data exhibits sufficient anomaly diversity.

Existing video object removal methods excel at inpaintingcontent “behind” the object and correcting appearance-level artifactssuch as shadows and reflections. However, when the removed object hasmore significant interactions, such as collisions with other objects, cur-rent models fail to correct them and produce implausible results. Wepresent VOID, a video object removal framework designed to performphysically-plausible inpainting in these complex scenarios. To train themodel, we generate a new paired dataset of counterfactual object re-movals using Kubric and HUMOTO, where removing an object requiresaltering downstream physical interactions. During inference, a vision-language model identifies regions of the scene affected by the removedobject. These regions are then used to guide a video diffusion model thatgenerates physically consistent counterfactual outcomes. Experiments onboth synthetic and real data show that our approach better preservesconsistent scene dynamics after object removal compared to prior videoobject removal methods. We hope this framework sheds light on howto make video editing models better simulators of the world throughhigh-level causal reasoning.

Video diffusion models lack explicit geometric supervisionduring training, leading to inconsistency artifacts such as object defor-mation, spatial drift, and depth violations in generated videos. To addressthis limitation, we propose a geometry-based reward model that leveragespretrained geometric foundation models to evaluate multi-view consis-tency through cross-frame reprojection error. Unlike previous geometricmetrics that measure inconsistency in pixel space, where pixel intensitymay introduce additional noise, our approach conducts error computa-tion in a pointwise fashion, yielding a more physically grounded and ro-bust error metric. Furthermore, we introduce a geometry-aware samplingstrategy that filters out low-texture and non-semantic regions, focusingevaluation on geometrically meaningful areas with reliable correspon-dences to improve robustness. We apply this reward model to align videodiffusion models through two complementary pathways: post-training ofa bidirectional model via SFT or Reinforcement Learning and inference-time optimization of a Causal Video Model (e.g., Streaming video genera-tor) via test-time scaling with our reward as a path verifier. Experimentalresults validate the effectiveness of our design, demonstrating that ourgeometry-based reward provides superior robustness compared to othervariants. By enabling efficient inference-time scaling, our method offersa practical solution for enhancing open-source video models without re-quiring extensive computational resources for retraining.