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ECCV 2026

HER-Count: Learning Hyper-Exemplar Representation for Generalized Zero-Shot Object Counting

Jianing Li, Xiaobin Liu, Ruihan Xu

Zero-shot object counting aims to count objects in images from givenclass names. Existing methods typically select representative image patches orextract text embeddings as exemplars to match image regions for counting. How-ever, a limited number of selected patches is insufficient to cover intra-class di-versity, and the accumulated errors in exemplar detection and selection substan-tially degrade counting accuracy. While the text embeddings remain at a genericsemantic level, blind to the specific instance diversity of target objects in animage. To handle these issues, this paper proposes the Hyper-Exemplar Repre-sentation (HER) counting framework (HER-Count) to represent the objects ofa specific category in a given image. Rather than image patches or text embed-dings, HERs are synthesized from holistic images and textual class names viaan multi-modal large-language model. By jointly integrating all target objects ineach image, HERs avoid exemplar generation errors and are more customizedrepresentative, thus yielding more accurate counting results. To effectively ex-ploit HERs in counting, HER-Count introduces a hierarchical fusion strategy thatinjects HERs into multiple intermediate layers of the vision encoder when pre-dicting density maps. This enables the model to progressively refine countingcues across layers, significantly enhancing the performance. A global discrimi-nation enhancement constraint at both image sample and semantic class levels isdesigned to further improve the capacities for both representation and discrimi-nation of HERs. Extensive experiments on widely used benchmarks demonstrateboth the state-of-the-art performance and generalization ability of the proposedHER-Count. Benefiting from the compact end-to-end design, HER-Count alsoachieves promising inference speed.

ECCV 2026

Generation Models Know Space: Unleashing Implicit 3D Priors for Scene Understanding

Xianjin Wu, Dingkang Liang, Tianrui Feng, Kui Xia, Yumeng Zhang, Xiaofan Li, Xiao Tan, Xiang Bai

While Multimodal Large Language Models demonstrate im-pressive semantic capabilities, they often suffer from spatial blindness,struggling with fine-grained geometric reasoning and physical dynamics.Existing solutions typically rely on explicit 3D modalities or complexgeometric scaffolding, which are limited by data scarcity and generaliza-tion challenges. In this work, we propose a paradigm shift by leveragingthe implicit spatial prior within large-scale video generation models. Weposit that to synthesize temporally coherent videos, these models inher-ently learn robust 3D structural priors and physical laws. We introduceVEGA-3D (Video Extracted Generative Awareness), a plug-and-playframework that repurposes a pre-trained video diffusion model as a La-tent World Simulator. By extracting spatiotemporal features from inter-mediate noise levels and integrating them with semantic representationsvia a token-level adaptive gated fusion mechanism, we enrich MLLMswith dense geometric cues without explicit 3D supervision. Extensiveexperiments across 3D scene understanding, spatial reasoning, and em-bodied manipulation benchmarks demonstrate that our method outper-forms state-of-the-art baselines, validating that generative priors providea scalable foundation for physical-world understanding. Code is publiclyavailable at https://github.com/H-EmbodVis/VEGA-3D.

ECCV 2026

Learning to Corrupt for Better Restoration

JoonKyu Park, Wooseok Lee, Jaeha Kim, Sehoon Kim, Bokyeung Lee, Kyoung Mu Lee

Recalling that diffusion models map noisy images to cleanones, the choice of noise addition (i.e., corruption) critically influencesthe denoising process. However, prior diffusion-based image restoration (IR)methods often employ naïve corruption strategies—such as randomlysampling noise, fixing the timestep, or even omitting corruption alto-gether—which may not accurately reflect the actual degradation of eachimage. To handle this, we propose Input-Aware Corruption for IR (IAC-IR) framework, which maps each low-quality (LQ) input into an optimalnoisy sample that lies on the pretrained diffusion trajectory. Instead ofchoosing corruption heuristically, we predict the timestep and noise us-ing supervision derived from the properties of the pretrained diffusionmodel. Specifically, the predicted timestep aligns the corrupted samplewith the Gaussian noise corrupted distribution, while the predicted noisepreserves the recoverable content of the input. Moreover, we use theseinput-aware corruption factors to improve conventional score-based dis-tillation. Rather than relying on random corruption, which often produceunreliable target scores and weak gradients, we perform distillation withinput-aware corruption, yielding more reliable score estimates and morestable distillation. By modeling input-aware corruption and integratingit into distillation, our method better leverages the pretrained diffusionprior, achieving superior perceptual quality in image restoration.

ECCV 2026

QualiTeacher: Quality-Conditioned Pseudo-Labeling for Real-World Image Restoration

Fengyang Xiao, Jingjia Feng, Peng Hu, Yuhan Chen, Dingming Zhang, Lei Xu, Guanyi Qin, Lu Li, Chunming He, Sina Farsiu

Real-world image restoration (RWIR) is a highly challeng-ing task due to the absence of clean ground-truth images. Many recentmethods resort to pseudo-label (PL) supervision, often within a Mean-Teacher (MT) framework, where a teacher network generates targets fora student network. However, these methods face a critical paradox: un-conditionally trusting the often imperfect, low-quality PLs forces the stu-dent model to learn undesirable artifacts, while discarding them severelylimits data diversity and impairs model generalization. In this paper, wepropose QualiTeacher, a novel framework that transforms pseudo-labelquality from a noisy liability into a conditional supervisory signal. In-stead of filtering, QualiTeacher explicitly conditions the student modelon the quality of the PLs, estimated by an ensemble of complementarynon-reference image quality assessment (NR-IQA) models spanning low-level distortion and semantic-level assessment. This strategy teaches thestudent network to learn a quality-graded restoration manifold, enablingit to understand what constitutes different quality levels. Consequently,it can not only avoid mimicking artifacts from low-quality labels but alsoextrapolate to generate results of higher quality than the teacher itself.To ensure the robustness and accuracy of this quality-driven learning,we further enhance the process with a multi-augmentation scheme to di-versify the PL quality spectrum, a score-based preference optimizationstrategy inspired by Direct Preference Optimization (DPO) to enforcea monotonically ordered quality separation, and a cropped consistencyloss to prevent adversarial over-optimization (reward hacking) of theIQA models. Experiments on standard RWIR benchmarks demonstratethat QualiTeacher can serve as a plug-and-play strategy to improve thequality of the existing pseudo-labeling framework, establishing a newparadigm for learning from imperfect supervision. Code will be releasedat https://github.com/fengyang1399-pixel/QualiTeacher.git .6.87.52 F. Xiao et al.(a) Unconditional Trust (b) Aggressive Filtering (c) QualiTeacher5.2 6.8 7.5 5.2 6.8 7.5Aug AugTeacher Teacher NR-IQA Teacher NR-IQAInput PL Input Optimal PL Input Quality-Conditioned PLsArtifacts are replicated rather than removed Limited data diversity, over-smoothed and blurry Better generalization via quality-conditioned pseudo-labelsDegraded Unconditional Trust Aggressive Filtering QualiTeacherFig. 1: Comparison of pseudo-label (PL) utilization strategies in the mean-teacherframework. (a) Unconditional Trust: PLs are used without filtering, causing degra-dation artifacts to be replicated by the student. (b) Aggressive Filtering: Low-quality PLs are discarded via NR-IQA filtering, yet over-smoothed outputs that receivedeceptively high scores survive, introducing blurriness. (c) QualiTeacher (Ours):Nearly all PLs are retained (with only extreme outliers discarded), with their qualityscores injected as continuous conditioning signals, enabling the student to leverage fulldata diversity while remaining aware of each sample’s reliability.

ECCV 2026

Forecasting Animal Motion

Neerja Thakkar, Shiry Ginosar, Jacob Walker, Jitendra Malik, Joao Carreira, Carl Doersch

Visual intelligence requires anticipating the future behaviorof agents, yet vision systems lack a general representation for motion andbehavior. We propose dense point trajectories as visual tokens for behavior,a structured mid-level representation that disentangles motion fromappearance and generalizes across diverse non-rigid agents, such as animalsin-the-wild. Building on this abstraction, we design a diffusion transformerthat models unordered sets of trajectories and explicitly reasons aboutocclusion, enabling coherent forecasts of complex motion patterns. Toevaluate at scale, we curate 300 hours of unconstrained animal motion fromvideo through robust shot detection and camera-motion compensation.Experiments show that forecasting trajectory tokens achieves category-agnostic, data-efficient prediction, outperforms state-of-the-art baselines,and generalizes to rare species and morphologies, providing a foundationfor predictive visual intelligence in the wild.

ECCV 2026

Face Anything: 4D Face Reconstruction from Any Image Sequence

Umut Kocasarı, Simon Giebenhain, Richard Shaw, Matthias Niessner

Accurate reconstruction and tracking of dynamic human facesfrom image sequences is challenging because non-rigid deformations, ex-pression changes, and viewpoint variations occur simultaneously, creat-ing significant ambiguity in geometry and correspondence estimation. Wepresent a unified method for high-fidelity 4D facial reconstruction basedon canonical facial point prediction, a representation that assigns eachpixel a normalized facial coordinate in a shared canonical space. Thisformulation transforms dense tracking and dynamic reconstruction intoa canonical reconstruction problem, enabling temporally consistent ge-ometry and reliable correspondences within a single feed-forward model.By jointly predicting depth and canonical coordinates, our method en-ables accurate depth estimation, temporally stable reconstruction, dense3D geometry, and robust facial point tracking within a single architec-ture. We implement this formulation using a transformer-based modelthat jointly predicts depth and canonical facial coordinates, trained us-ing multi-view geometry data that non-rigidly warps into the canonicalspace. Extensive experiments on image and video benchmarks demon-strate state-of-the-art performance across reconstruction and trackingtasks, achieving approximately 3× lower correspondence error and fasterinference than prior dynamic reconstruction methods, while improvingdepth accuracy by 16%. These results highlight canonical facial point pre-diction as an effective foundation for unified feed-forward 4D facial recon-struction. Website: https://kocasariumut.github.io/FaceAnything/

ECCV 2026

EventSpecPS: Photometric Stereo with Multispectral Reflectance Using an Event Camera

Jingqian Wu, Bohan Yu, Jun Hoong Chan, Edmund Lam, Boxin Shi

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.

ECCV 2026

EchoStyle: Unlocking High-Fidelity Video Stylization with Reverse Data Synthesis

Huaqiu Li, Jiahao Wang, Sijia Cai, Hualian Sheng, Bing Deng, Jieping Ye, Wenhan Luo

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.

ECCV 2026

MSEditor: Toward Consistent Multi-Shot Video Editing

Kunyu Feng, Yue Ma, Bingyuan Wang, Yuefeng Wang, Zhiyuan Qin, Hao Cheng, Hao Li, Qifeng Chen, Zeyu Wang

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.

ECCV 2026

ET-SAM: Efficient Point Prompt Prediction in SAM for Unified Scene Text Detection and Layout Analysis

Xike Zhang, Maoyuan Ye, Juhua Liu, Bo Du

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.

ECCV 2026

Dual Masked Generative Adversarial Transformer for Unsupervised Domain Adaptation

Lei Zhu, Xinxing Xu, JUN ZHOU, Qiegen Liu, Rick Siow Mong Goh, Yong Liu

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.

ECCV 2026

DocLayout-VL: A Foundational Model for Hierarchical, Open-set, and Promptable Document Layout Segmentation

Venkata Venna, Srihari Bandarupalli, Anirudh Srinivasan, R Raghuveer, Sai Madhusudan Gunda, SANTOSH RAVI KIRAN SARVADEVABHATLA

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.

ECCV 2026

DINOde: Continuous Vision-Text Alignment for Open-Vocabulary Semantic Segmentation

Sung-Hoon Yoon, Hoyong Kwon, Changgyoon Oh, Kuk-Jin Yoon

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.

ECCV 2026

DiffVP:Differential Visual Semantic Prompting for LLM-Based CT Report Generation

Yuhe Tian, Kun Zhang, Haoran Ma, Rui Yan, Yingtai Li, Rongsheng Wang, S. Kevin Zhou

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/.

ECCV 2026

Diagnosing Aerial-View Object Detectors with Foundational Image Generative Models

Stanislav Panev, Minhyek Jeon, Vaishnavi Khindkar, Ahish Deshpande, Celso de Melo, Shuowen Hu, Shayok Chakraborty, Fernando De la Torre

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/

ECCV 2026

CrossView: Can Vision-Language Models Reason Across Cameras?

Sahil Shah, S P Sharan, Harsh Goel, Manvik Pasula, Adithya Hebbalae, Minkyu Choi, Sandeep Chinchali

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.

ECCV 2026

Cross-Space Distillation: Teaching One-Step Students with Modern Diffusion Teachers

Anh Nguyen, Ngan Nguyen, Hong Duc Vu, Trung Dao, Viet Nguyen, Minh-Quan Dao, Kien Nguyen, Tran Bao Chi Tran, Phong Nguyen, Khoi Nguyen, Cuong Pham, Dimitris N. Metaxas, Vishal Patel, Anh Tran

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.

ECCV 2026

SR-Edit: Region-Aware Image Editing via Self-Refinement

Andong Wang, Zehua Chen, Yuxuan Jiang, Jun Zhu

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.

ECCV 2026

CapFrame: Text-Instructed Viewpoint Grounding in 3D Gaussian Scenes via Geometric Pseudo Labels

Jirong Li, Satoshi Ikehata, Shuhei Kurita, Ikuro Sato

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

ECCV 2026

BeyondSight: Object Permanence for End-to-End Autonomous Driving

Sandro Papais, Letian Wang, Mudit jain, Behnaz Rezaei, Steven Waslander

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.

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