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Search papers from 30+ top conferences, including NeurIPS, ICLR, ICML, CVPR, ACL and AAAI. Filter by keyword, then open any paper for its abstract, authors and links.

ECCV 2026

ClearText-Video: A Large-Scale Text-Centric Video Dataset Bridging Video Restoration and Scene-Text Enhancement

Jinlong Li, Jiaming Ding, Dingfu Lu, Malcolm Hsiu, Chuang Ke, Kangning Yang, Bochen Guan, Lan Fu, Jie Cai, Huiming Sun, Zibo Meng

Multimodal Large Language Models (MLLMs) have recentlydemonstrated strong progress in visual–linguistic understanding, yet theirperformance on text-centric video reasoning remains highly sensitive toinput quality. Real-world user-provided videos frequently contain motionblur, compression artifacts, noise, and low-resolution text, substantiallyimpairing reliable text reading and downstream reasoning. WhetherMLLMs can robustly read and reason over in-the-wild text under di-verse quality conditions remains an unanswered fundamental question.We introduce ClearText-Video (CTVid), a large-scale, scene-text-awarebenchmark for studying text-centric video understanding under controlledquality variation. CTVid contains 4,639 real-world text-rich egocentricvideos, 550K+ frames, 1.6M human-verified scene-text annotations, and220K+ spatial/temporal question–answer pairs in Chinese and English.For each high-quality video, CTVid provides content-matched Degraded-Quality and Restored-Quality variants, supporting two task families:Text-Centric Video Restoration and Multi-Video Quality VideoQA. Weevaluate 18 representative restoration methods and 16 state-of-the-artMLLMs on CTVid. The results show that visual enhancement does notguarantee textual fidelity or downstream reasoning gains: blur is moredamaging than low resolution, restored videos can alter the textual evi-dence used by MLLMs, and OCR-only pipelines remain far below directmultimodal reasoning. CTVid exposes the gap between video restorationand text-grounded understanding, providing a rigorous foundation forrestoration-aware, quality-robust text-centric video systems. Benchmark:https://github.com/jinlong17/CTVid-Bench.

ECCV 2026

CasaMaestro: Multi-View Panoramas for House-Scale 3D Reconstruction

Yuzhou Ji, Xiaotian Yang, Zhipeng Zhang

The rise of home-deployed embodied AI systems is driving agrowing need for fast, metric 3D reconstruction of residential spaces tosupport navigation, interaction, and long-horizon task execution. How-ever, the commonly used pinhole-camera 3D reconstruction pipelinesstruggle to model large indoor residences efficiently due to their limitedfield of view, to which achieving full coverage across multiple rooms oftenrequires thousands of images and incurs drift from long chains of incre-mental alignment. In this work, we present CasaMaestro (Spanish wordsmeaning “house” and “master”), a feedforward model that can take onlytwenty to fifty sparse multi-view indoor panoramas as input and directlypredicts metric depth along with camera poses, allowing fast point-cloudDA3-AUC30 Pi3-AUC30 VGGT-AUC30Yaw Step 90° Yaw Step 45° Yaw Step 15° DA3-time Pi3-time VGGT-timeMulti Viewpoints Single Viewpoint0.9 8000.8 7000.7 6000.65000.54000.43000.30.2 2000.1 1000 0 (s)AUC Yaw step 90 Yaw step 45 Yaw step 15 TimesFig. 2: Illustration of existing problems. Left visualization shows pinhole modelseither face limited FoV in sparse capture or accumulative error in dense sequence. Rightfigure shows pose accuracy and processing time under different input density.reconstruction of the entire house with full coverage. CasaMaestro isthe first model that supports house-scale reconstruction with multi-viewpanoramas. Experiments show that CasaMaestro can robustly providehigh quality results in both real-world and synthetic scenes, which canserve as a strong foundation for acquiring house-scale 3D indoor assetsto be applied in close-loop simulation.

ECCV 2026

Bridging Theory and Practice in Source-Free Domain Adaptation via Adversarial Proxy Perturbation

Dexuan Zhang, Qianyu Zhou, Thomas Westfechtel, YUSUKE Mukuta, Tatsuya Harada

Domain adaptation addresses the distributional shifts be-tween source and target domains. Given increasing data privacy con-straints, Source-Free Domain Adaptation (SFDA) has attracted growinginterest, as it precludes access to raw source data during adaptation.While existing SFDA methods predominantly rely on source-like datageneration or heuristic pseudo-labeling, their connection to target errorgeneralization is often unclear. In this work, we first present a theoreticalanalysis by establishing an upper bound on the target error that over-comes explicit dependence on the classical joint-error term. Guided bythis analysis, we propose an optimization principle based on AdversarialProxy Perturbation (APP), serving as a practical bridge between thederived bound and empirical training. We further extend the frameworkto multi-source scenarios and enable the integration of zero-shot priorsfrom vision-language models (e.g., CLIP) through mutual distillation.Extensive experiments on standard benchmarks demonstrate consistentimprovements over state-of-the-art methods in both unimodal and multi-modal settings, validating the proposed theoretical framework in practice.

ECCV 2026

Boba: Batched Simulation for Physics-Based Gaussian Digital Twins

Yihan Pang, Hanxiao Jiang, Sushant Kondguli, Sarita Adve, Shenlong Wang

Digital twins replicate the appearance and physical behav-ior of real-world objects for interactive simulation in XR, robotics, andgaming. While neural representations such as 3D Gaussians enable high-fidelity twins reconstructed from images, existing systems simulate onlyone instance at a time and barely reach real-time performance, makinglarge-scale rollout evaluation for robot learning, model predictive control,and multi-user XR impractical. We present Boba, the first batched simula-tor for physics-based Gaussian digital twins. Our key idea is to separate thestatic twin template from the dynamic simulation state and co-design thephysics, deformation, and rendering/visualization pipelines for batchedexecution. This structure enables shared template computation acrossinstances while minimizing memory traffic and synchronization overhead.Guided by this principle, we introduce compact surrogate spring–massmodels and a set of system-level optimizations—including mixed-precisionGaussian skinning, memory-efficient execution, and shared-memory-awarebatching—that significantly reduce compute and memory costs. On anXR-configured Jetson Orin, Boba’s local configuration achieves over 10×single-instance speedup over the prior state-of-the-art PhysTwin pipeline.Its distributed configuration, split between the Orin and a server, reaches25.1 ms end-to-end latency, 13.2× lower than the same PhysTwin base-line on Orin, while lowering incremental power by 22.2% relative toBoba’s local configuration. For batched execution, Boba scales to 3,310FPS on RTX 4090, a 16.2× system-level aggregate-throughput improve-ment over Boba’s optimized single-instance configuration on the sameGPU, and accelerates predictive control for deformable object manipula-tion, achieving speedups from 26× to an estimated > 2,000× relative toPhysTwin-based planning. These results show that Boba can supportpower-constrained XR applications and scalable robot planning.

ECCV 2026

Beyond Atomic Layouts: Compositional Design Understanding with Vision-Language Models

Yiyang Huang, Zhaowen Wang, Simon Jenni, Jing Shi, Yitian Zhang, Yizhou Wang, Yun Fu

Layout understanding, or the interpretation of element orga-nization, is essential for document analysis, user interface (UI) creation,and graphic design. While recent vision-language models (VLMs) ex-cel at interpreting atomic layouts composed of independent elements,they struggle with compositional layouts that require reasoning over vi-sually entangled elements within hierarchical multi-layer structures. Inthis paper, we introduce a new task, compositional layout understanding,and present CoDeLayout, a VQA dataset of ∼20K real-world multi-layerlayouts annotated with compositional element pairs and design intent.Through empirical analysis on CoDeLayout, we identify two key chal-lenges for existing VLMs: semantic drift between textual metadata andvisual content, and structural ambiguity in hierarchical inter-elementrelationships. To address these challenges, we propose MASON, a post-training paradigm that integrates multimodal alignment (MA) and struc-tural perception (SP). MA enhances element interpretation by ground-ing metadata-defined elements to their visual counterparts, mitigatingsemantic drift, while SP models layer-aware inter-element spatial rela-tionships to improve hierarchical understanding and reduce structuralambiguity. Experiments reveal substantial gaps in existing VLMs: eventhe strongest baseline, GPT-o3, achieves only 79.68% accuracy, whereasQwen2.5-VL 7B with MASON reaches 91.66%. Notably, MASON sur-passes full-data Direct Finetune using only 30% of the training data andscales better with additional data.

ECCV 2026

ARC-Loc: Leveraging Azimuthal Ray Convergence as a Geometric Cue for Direct Cross-View Localization

HyeongSik Kim, Mincheol Kim, Heejoon Moon, Je Hyeong Hong

Cross-view localization (CVL) estimates the pose of a groundimage by matching it to a geo-referenced satellite image. To bridge theextreme viewpoint gap, mainstream pipelines rely on Bird’s-Eye-View(BEV) transformations or 2D-to-3D lifting. However, deriving 3D struc-tures from a single ground image is fundamentally ill-posed, causing thesemethods to endure geometric distortions and computational costs dur-ing 3D lifting or BEV projection. Furthermore, relying on external depthfoundation models to resolve this introduces latency and remains suscep-tible to noisy predictions. In this work, we present a different approachinspired by a human navigation technique called resection, that can per-form direct ground-to-satellite image matching and localization withoutrelying on external depth foundation models. The key insights of ourmethod are that (i) ground keypoints can be translated into azimuthalrays on the satellite map, and (ii) these rays ideally converge at theuser location. Exploiting this geometric constraint through direct line-to-point correspondences, we introduce a minimal Azimuthal Ray Con-vergence (ARC) solver to identify the intersection alongside an ARC lossto optimize the matching network. By eliminating dependencies on com-putationally heavy BEV transformations and external depth foundationmodels, our approach achieves faster, memory-efficient inference, whileits explicit feature matching ensures straightforward compatibility withexisting frameworks. Experiments on VIGOR and KITTI demonstratethat ARC-Loc maintains competitive localization accuracy compared torecent approaches, highlighting its practicality.

ECCV 2026

AnyMatch: Supercharging Universal Multi-Modal Image Matching with Large-Scale Single-View Images

Meng Yang, Zizhuo Li, Linfeng Tang, Fan Fan, Jiayi Ma

Multi-modal image matching is essential for visual localiza-tion and multi-sensor fusion, but it is hindered by the scarcity of large-scale training data with precise geometric annotations. Existing real-world datasets suffer from prohibitive costs, limited scene diversity, anderrors in SfM-MVS pipelines, while synthetic methods struggle to main-tain 3D geometric consistency or achieve photorealistic appearance. Toaddress this, we propose AnyMatch, a novel framework that leveragesabundant, easily accessible single-view images at minimal cost to gen-erate rich multi-modal training data. AnyMatch integrates monoculardepth estimation, 3D reprojection, diffusion-based inpainting, and cross-modal image translation to synthesize multi-view, multi-modal imagepairs with 3D geometric fidelity. Crucially, our method provides annota-tions that strictly adhere to 3D geometric consistency through explicit3D reprojection, avoiding SfM-MVS error accumulation. Furthermore,AnyMatch offers strong scalability, enabling controllable scene diversityand annotation difficulty via adjustable input and camera parameters.We construct Any-syn, a large-scale synthetic multi-modal dataset us-ing AnyMatch. Experimental results show that matching networks (e.g.,LoFTR, EDM, RoMa) fine-tuned on Any-syn achieve substantial perfor-mance gains on multi-modal benchmarks, exhibiting superior generaliza-tion and robustness compared to models trained on existing data.

ECCV 2026

AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories

Zun Wang, Han Lin, Jaehong Yoon, Jaemin Cho, Yue Zhang, Mohit Bansal

Maintaining spatial world consistency over long horizons remains a central challenge for camera-controllable video generation. Existing memory-based approaches often condition generation on globally reconstructed 3D scenes by rendering anchor videos from the reconstructed geometry in the history. However, reconstructing a global 3D scene from multiple views inevitably introduces cross-view misalignment, as pose and depth estimation errors cause the same surfaces to be reconstructed at slightly different 3D locations across views. When fused, these inconsistencies accumulate into noisy geometry that contaminates the conditioning signals and degrades generation quality. We introduce AnchorWeave, a memory-augmented video generation framework that replaces a single misaligned global memory with multiple clean local geometric memories and learns to reconcile their cross-view inconsistencies. To this end, AnchorWeave performs coverage-driven local memory retrieval aligned with the target trajectory and integrates the selected local memories through a multi-anchor weaving controller during generation. Extensive experiments demonstrate that AnchorWeave significantly improves long-term scene consistency while maintaining strong visual quality, with ablation and analysis studies further validating the effectiveness of local geometric conditioning, multi-anchor control, and coveragedriven retrieval. We provide more qualitative demonstration videos in the supplementary material via an anonymized local webpage.

ECCV 2026

A Benchmark for Heterogeneous Stereo Deblurring with Physically- and Epipolar-constrained Cross Attention

Jiah Kim, Hoju Shin, Seung Wook Kim, Seowon Ji

Modern stereo-capable smartphones enable immersive XRcontent capture. However, hardware heterogeneity across camera mod-ules often causes severe asymmetric blur artifacts. Existing methods andbenchmarks largely assume homogeneous stereo setups and therefore donot explicitly address such asymmetric degradation. To bridge this gap,we present a dedicated framework for heterogeneous stereo deblurring.First, we introduce the heterogeneous stereo deblurring (HSD) dataset,constructed from real smartphone stereo captures via multi-frame inte-gration. Second, we propose physically- and epipolar-constrained crossattention (PECA), a lightweight module that restricts cross-view match-ing to an epipolar search window bounded by an optics-derived dis-parity upper bound. By enforcing physically valid disparity constraints,PECA enables efficient and reliable cross-view feature fusion. Moreover,our confidence-weighted attention with residual fusion emphasizes cross-guided deblurring when correspondences are reliable, while naturallyfalling back to self-deblurring in occluded or unreliable regions. PECAis architecture-agnostic and consistently improves CNN-, Transformer-,and NAFNet-based baselines. Extensive experiments on HSD show thatPECA-enhanced models achieve improved restoration performance withfavorable efficiency. The dataset and source code are publicly released athttps://github.com/shinhoju/PECA.

ECCV 2026

What Matters in RL-Based Methods for Object-Goal Navigation? An Empirical Study and A Unified Framework

Hongze Wang, Boyang Sun, Jiaxu Xing, Fan Yang, Marco Hutter, Dhruv Shah, Davide Scaramuzza, Marc Pollefeys

Object-Goal Navigation (ObjectNav) is a key capability fordeploying mobile robots in everyday environments such as homes, schools,and workplaces. In this task, an agent must locate an instance of a targetobject category in previously unseen environments using only onboardperception, requiring the integration of semantic understanding, spatialreasoning, and long-horizon planning. Reinforcement learning (RL) hasbecome a dominant paradigm for ObjectNav, yet modern systems involvenumerous design choices across perception modules, policy architectures,and inference-time strategies. The relative impact of these components,however, remains poorly understood. In this work, we present a large-scale empirical study of modular RL-based ObjectNav systems. We de-compose the navigation pipeline into three key components: perception,policy, and test-time enhancement, and conduct extensive controlled ex-periments to analyze their individual contributions. Our results suggestthat improvements in perception quality and test-time strategies oftenyield larger performance gains than policy improvements alone, high-lighting the importance of understanding how different components inter-act within modular navigation systems. Motivated by these findings, weintroduce a unified framework for systematically studying modular Ob-jectNav systems. Guided by our analysis, we build an enhanced systemthat achieves state-of-the-art performance on the Gibson benchmark, im-proving SPL by 6.6% and success rate by 2.7% over prior methods. Wealso introduce a human expert baseline, achieving 98% success, high-lighting the significant gap between current RL agents and human-levelnavigation. Finally, we provide practical insights and design recommen-dations for each module to help guide future research. Project page:https://honwang0054.github.io/What-matters-in-RL-ObjNav-web/.

ECCV 2026

VLZip: Unified Visual and Textual Compression for Interleaved Long-Context Modeling

Yuqi Zhang, Cheng Chen, Yuyu Guo, Wenjie Yang, Lingchen Meng, Peng Di, Hang Yu, Zuxuan Wu, Yu-Gang Jiang

Vision Language Models (VLMs) face signi_x001C_cant challengeswith ultra-long, interleaved image-text sequences due to the quadraticcomplexity of self-attention. Current solutions either resort to aggres-sive token pruning, risking irreversible information loss, or adopt e_x001E_-cient but less precise architectures, while largely ignoring the equallyvital textual component. We introduce VLZip, a framework that uni-_x001C_es visual and textual compression for high-_x001C_delity reasoning within apure Transformer. At its core, VLZip hierarchically distills visual andtextual segments into compact, layer-speci_x001C_c "soft pre_x001C_xes" and injectsthem into each decoder layer's hidden states, drastically shortening theattention sequence while preserving _x001C_ne-grained global context. To ad-dress de_x001C_cient evaluations in the _x001C_eld, we also introduce LongVLBench,a new benchmark derived from video narratives that demands holistic,narrative-level reasoning. Extensive experiments show VLZip achievesleading performance on long-context multimodal reasoning, enabling train-ing up to 120K tokens_x0016_a 6× increase over the baseline_x0016_and inferencebeyond 280K tokens with signi_x001C_cantly reduced memory, while demon-strating the memory scalability to handle up to 2M tokens. By excellingat extreme context lengths where existing methods collapse, VLZip es-tablishes an e_x001E_cient and powerful new standard for long-context mul-timodal AI. Code is available at https://github.com/ShareLab-SII/VLZip.

ECCV 2026

VisCoP: Visual Probing for Video Domain Adaptation of Vision Language Models

Dominick Reilly, Manish Govind, Le Xue, Srijan Das

Large Vision Language Models (VLMs) excel at general vi-sual reasoning tasks, but their performance degrades sharply when de-ployed in novel domains with substantial distribution shifts compared towhat was seen during pretraining. Existing approaches to adapt VLMsto novel target domains rely on finetuning standard VLM components.Depending on which components are finetuned, these approaches eitherlimit the VLMs ability to learn domain-specific features, or lead to catas-trophic forgetting of pre-existing capabilities. To address this, we intro-duce Vision Contextualized Probing (VisCoP), which augments theVLM’s vision encoder with a compact set of learnable visual probes,enabling domain-specific features to be learned with only minimal up-dates to the pretrained VLM components. We evaluate VisCoP acrossthree challenging domain adaptation scenarios: cross-view (exocentric →egocentric), cross-modal (RGB → depth), and cross-task (human under-standing → robot control). Our experiments demonstrate that VisCoPconsistently outperforms existing domain adaptation strategies, achiev-ing superior performance on the target domain, while better retainingcapabilities from the source domain. We will release all code, models,and evaluation protocols to facilitate future research in this direction.

ECCV 2026

URoPE: Universal Relative Position Embedding across Geometric Spaces

Yichen Xie, Depu Meng, Yihan Hu, Chensheng Peng, Quentin Herau, Masayoshi Tomizuka, Wei Zhan

Relative position embedding has become a standard mech-anism for encoding positional information in Transformers. However,existing formulations are typically limited to a fixed geometric space,namely 1D sequences or regular 2D/3D grids, which restricts their ap-plicability to many computer vision tasks that require geometric rea-soning across camera views or between 2D and 3D spaces. To addressthis limitation, we propose URoPE, a universal extension of RotaryPosition Embedding (RoPE) to cross-view or cross-dimensional geomet-ric spaces. For each key/value image patch, URoPE samples 3D pointsalong the corresponding camera ray at predefined depth anchors andprojects them into the query image plane. Standard 2D RoPE can thenbe applied using the projected pixel coordinates. URoPE is a parameter-free and intrinsics-aware relative position embedding that is invariant tothe choice of global coordinate systems, while remaining fully compatiblewith existing RoPE-optimized attention kernels. We evaluate URoPE asa plug-in positional encoding for transformer architectures across a di-verse set of tasks, including novel view synthesis, 3D object detection,object tracking, and depth estimation, covering 2D–2D, 2D–3D, and tem-poral scenarios. Experiments show that URoPE consistently improvesthe performance of transformer-based models across all tasks, demon-strating its effectiveness and generality for geometric reasoning. Our codeis available on our project website: https://urope-pe.github.io/.

ECCV 2026

Guide, Think, Act: Interactive Embodied Reasoning for Vision-Language-Action Model

Yiran Ling, Qing Lian, Jinghang Li, Qing Jiang, Tianming Zhang, Xiaoke Jiang, Chuanxiu Liu, Jie Liu, Lei Zhang

In this paper, we propose GTA-VLA (Guide, Think,Act), an interactive Vision-Language-Action (VLA) framework thatenables spatially steerable embodied reasoning by allowing users to guiderobot policies with explicit visual cues. Existing VLA models learn adirect "Sense-to-Act" mapping from multimodal observations to robotactions. While effective within the training distribution, such tightly cou-pled policies are brittle under out-of-domain (OOD) shifts and difficultto correct when failures occur. Although recent embodied Chain-of-Thought (CoT) approaches expose intermediate reasoning, they stilllack a mechanism for incorporating human spatial guidance, limitingtheir ability to resolve visual ambiguities or recover from mistakes. Toaddress this gap, our framework allows users to optionally guide thepolicy with spatial priors, such as affordance points, boxes, and traces,which the subsequent reasoning process can directly condition on. Basedon these inputs, the model generates a unified spatial-visual Chain-of-Thought that integrates external guidance with internal task planning,aligning human visual intent with autonomous decision-making. Forpractical deployment, we further couple the reasoning module with alightweight reactive action head for efficient action execution. Extensiveexperiments demonstrate the effectiveness of our approach. On thein-domain SimplerEnv WidowX benchmark, our framework achievesa state-of-the-art 81.2% success rate. Under OOD visual shifts andspatial ambiguities, a single visual interaction substantially improvestask success over existing methods, highlighting the value of interactivereasoning for failure recovery in embodied control.

ECCV 2026

TrustCLIP: Learning Private Visual Features via Adversarial Reconstruction

Nikos Athanasiou, Ilya A. Petrov, Angela Yao, Shugao Ma, Eric Sauser, Edoardo Remelli, Shreyas Hampali, Johannes Schonberger, Fadime Sener, Bugra Tekin

Vision and vision–language models rely on high-level visualrepresentations that are increasingly used across recognition, retrieval,and multimodal reasoning pipelines. However, recent advances in gen-erative modeling have shown that such features can often be inverted,enabling realistic reconstructions of the underlying image and raisingsignificant privacy risks. We revisit this problem through the lens ofreconstruction and propose TrustCLIP, a reconstruction-driven frame-work that treats a feature-conditioned generator as an explicit privacyadversary. TrustCLIP learns a projection between encoder features anddownstream modules that is explicitly optimized to degrade the recon-structions produced by generative attackers while retaining the necessarysignals for downstream tasks. Unlike prior defenses that rely on discrimi-native privacy metrics, TrustCLIP directly optimizes against a generativereconstruction attacker, targeting a threat not captured by standard eval-uation protocols. We demonstrate its effectiveness in both conventionalclassification and multimodal large language model pipelines. Acrossthese settings, TrustCLIP consistently reduces the fidelity of genera-tive inversions while maintaining downstream task performance. Projectpage: atnikos.github.io/trustclip.

ECCV 2026

Track4World: Feedforward World-centric Dense 3D Tracking of All Pixels

Jiahao Lu, Jiayi Xu, Wenbo Hu, Ruijie Zhu, Chengfeng Zhao, Sai-Kit Yeung, Ying Shan, Yuan Liu

Estimating the 3D trajectory of every pixel from a monocularvideo is crucial and promising for a comprehensive understanding of the3D dynamics of videos. Recent monocular 3D tracking works demon-strate impressive performance, but are limited to either tracking sparsepoints on the first frame or a slow optimization-based framework fordense tracking. In this paper, we propose a feedforward model, calledTrack4World, enabling an efficient holistic 3D tracking of every pixel inthe world-centric coordinate system. Built on the global 3D scene repre-sentation encoded by a VGGT-style ViT, Track4World applies a novel3D correlation scheme to simultaneously estimate the pixel-wise 2D and3D dense flow between arbitrary frame pairs. The estimated scene flow,along with the reconstructed 3D geometry, enables subsequent efficient3D tracking of every pixel of this video. Extensive experiments on multiplebenchmarks demonstrate that our approach consistently outperforms ex-isting methods in 2D/3D flow estimation and 3D tracking, highlighting itsrobustness and scalability for real-world 4D reconstruction tasks. Projectpage: https://jiah-cloud.github.io/Track4World.github.io/.

ECCV 2026

Towards In-Context Tone Style Transfer with A Large-Scale Triplet Dataset

Yuhai Deng, Huimin She, Wei Shen, Meng Li, Ruoxi Wu, Lunxi Yuan, Xiang Li

Tone style transfer for photo retouching aims to adapt thestylistic tone of the reference image to a given content image. However,the lack of high-quality large-scale triplet datasets with stylized groundtruth forces existing methods to rely on self-supervised or proxy objec-tives, which limits model capability. To mitigate this gap, we design adata construction pipeline to build TST100K, a large-scale dataset of100,000 content-reference-stylized triplets. At the core of this pipeline,we train a tone style scorer to ensure strict stylistic consistency for eachtriplet. In addition, existing methods typically extract content and ref-erence features independently and then fuse them in a decoder, whichmay cause semantic loss and lead to inappropriate color transfer and de-graded visual aesthetics. Instead, we propose ICTone, a diffusion-basedframework that performs tone transfer in an in-context manner by jointlyconditioning on both images, leveraging the semantic priors of generativemodels for semantic-aware transfer. Reward feedback learning using thetone style scorer is further incorporated to improve stylistic fidelity andvisual quality. Experiments demonstrate the effectiveness of TST100K,and ICTone achieves state-of-the-art performance on both quantitativemetrics and human evaluations. The project page is available online.

ECCV 2026

Thinking from the Robot’s View: The CoT-HRC Benchmark for Human Intent Reasoning in Embodied Collaboration

Chenxi Deng, Chao Xu, JianmingLiu JianmingLiu, Shaofei Chen

Effective Human-Robot Collaboration (HRC) requires robotsto accurately infer human intents under partial observations. However,existing benchmarks mostly rely on idealized views, overlooking the spa-tiotemporal incompleteness such as low-angle truncation and dynamicocclusions inherent in real-world quadruped robot perception. Further-more, current evaluations lack diagnostic transparency, failing to distin-guish whether Vision-Language Models (VLMs) perform reliable com-pensatory reasoning or merely hallucinate based on statistical biaseswhen visual features are degraded. To bridge these dual gaps, we in-troduce CoT-HRC, a large-scale benchmark built in Habitat 3.0 thatcouples simulated robot-centric views with explicit hierarchical Chain-of-Thought (CoT) annotations. We propose a diagnostic protocol featur-ing the Step-wise Consistency Assessment (SCAR ) to penalize spuriousaccuracy by enforcing intermediate logical consistency, and the Condi-tional Reasoning Accuracy (CRA) to explicitly decouple models’ un-derlying reasoning capabilities from visual perception failures. Exten-sive experiments across state-of-the-art VLMs reveal that while currentmodels inherently possess strong physical commonsense, their reason-ing chains are severely disrupted by the dynamic geometric constraintsof egocentric views. Our fine-grained ablations further highlight criti-cal bottlenecks in temporal aggregation and the compensatory role ofsemantic priors, establishing CoT-HRC as a vital stepping stone for ro-bust embodied intent inference.The full benchmark is publicly availableat https://github.com/Thus-cx/CoT-HRC.

ECCV 2026

LensStyle: Learning the Optical Aesthetics for Controllable Stylized Lens Effect Rendering

Yachuan Huang, Liwen Xiao, Liao Shen, QiWen Wang, Huiqiang Sun, Zhiyu Pan, Zhiguo Cao

The visual aesthetics of photographs are deeply influencedby lens characteristics such as aperture shape, optical vignetting and op-tical diffraction, which together define a camera’s unique optical style.Existing lens effect rendering methods primarily focus on accurately sim-ulating the blur transition from small to large apertures but overlook thestylistic aspects of lens effects. As a result, they fail to produce diversebokeh effects under large apertures or capture distinctive photographicphenomena such as starbursts that emerge under small apertures. In thiswork, we introduce LensStyle, a unified framework for controllable styl-ized lens effect rendering that explicitly models lens aesthetics throughjoint continuous–discrete control. Our model incorporates a Dual-PathController that disentangles continuous optical parameter modulation(e.g., focus distance and blur strength) from discrete lens-style condi-tioning (e.g., circular, polygonal, donut, cat-eye, and starburst effects),enabling fine-grained, interpretable, and physically grounded lens manip-ulation within a single unified framework. To support model training, wecurate a comprehensive MultiLens dataset containing multi-lens imagepairs synthesized under real optical constraints. Extensive experimentsdemonstrate that LensStyle achieves superior realism, controllability, andaesthetic quality compared with existing lens effect rendering approachesand diffusion-based image editing models, advancing computational pho-tography toward multiple-lens-style simulation.

ECCV 2026

Test-time Counterfactual Calibration for Hallucination-Resistant Temporal Grounding

Chufan YI, Hongyu Qu, Shiyu Xuan, Rui Yan, Xiangbo Shu, Fang Zhao, Guosen Xie

Existing video temporal grounding methods typically oper-ate under a closed-world assumption, relying on the implicit premise thatevery natural language query must have a corresponding event some-where in the video. This always-answer paradigm severely compressesthe decision boundary during training, as models are seldom exposed tothe alternative hypothesis that the queried event may be absent. Con-sequently, when users ask about specific events that cannot be matchedin the video within an open-world environment, these models exhibitsystematic temporal hallucinations. This may stem from incorrect at-tribution of textual, visual, or multimodal information, leading them toconfidently generate plausible time frames for events that do not actuallyexist. To address this limitation, we propose HRVTG, a test-time adapta-tion framework that dynamically calibrates the decision boundary of themodel during inference. Built upon a frozen grounding backbone, the pro-posed framework constructs three types of counterfactual hallucinationprobes in a self-supervised manner, targeting the aforementioned halluci-nations. We dynamically reshape the decision boundary through absten-tion signals from counterfactual probes and consistency rewards fromgenuine queries. This is executed by introducing the GRPO algorithm,effectively reformulating the task into an online policy optimization prob-lem in reinforcement learning. Furthermore, we introduce decoupled met-rics to independently evaluate hallucination resistance and real-eventgrounding. Experiments indicate that our method suppresses halluci-nated responses to fabricated queries. It achieves accurate selective pre-diction while maintaining strong grounding performance on real events.The code will be released at https://github.com/CVL-hub/HRVTG.

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