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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

PanoRec: Spatially-Structured Sequence Modeling for Multi-Granularity Panoramic Retrieval

Zidong Cao, Ding Zhou, Wenyao Gao, Lutao Jiang, Hui Xiong

Panoramic images capture holistic environments, yet retriev-ing them using fine-grained, localized textual descriptions remains a fun-damental challenge. Through empirical analysis, we reveal that modernVision-Language Models (VLMs) suffer from severe semantic dilutionwhen processing panoramic inputs. By compressing an information-densepanorama into a single global embedding, VLMs inevitably submerge lo-cal details within vast backgrounds, restricting retrieval to coarse scene-level matching. To overcome this bottleneck, we propose PanoRec, amulti-granularity panoramic retrieval framework built on a spatially-structured sequence modeling paradigm. Specifically, PanoRec serializesdistortion-free cubemap faces and a downsampled global panorama along-side spatial anchor tokens into a unified sequence. This enables the ef-ficient extraction of decoupled local and global representations within asingle forward pass. To effectively supervise this multi-granularity featurespace, we formulate a joint spatial InfoNCE objective. For local match-ing, we adopt a MaxSim routing strategy that dynamically aligns eachquery with its most relevant cubemap face. Crucially, this strategy notonly suppresses background noise during inference but also inherentlyintroduces a powerful hard spatial negative mining mechanism duringtraining. Extensive experiments demonstrate that PanoRec achieves im-pressive performance across multiple scenarios, effectively unifying holis-tic scene-level retrieval with fine-grained spatial discrimination.

ECCV 2026

OrthoTrack: Continuous 6-DoF UAV Trajectory Estimation Anchored in Public Orthophotos

Oussema Dhaouadi, Zuria Bauer, Johannes Meier, Olaf Wysocki, Marc Pollefeys, Daniel Cremers

Continuous 6-DoF pose estimation is essential for au-tonomous UAV operations. Yet, existing visual odometry and SLAMmethods accumulate drift and yield only relative, up-to-scale trajecto-ries. Single-frame geo-localization, in turn, discards temporal continu-ity and remains too slow for real-time use. We present OrthoTrack, atraining-free system that estimates continuous 6-DoF UAV trajectoriesusing only publicly available orthophotos and surface models as a mapprior. OrthoTrack matches keyframes against the orthophoto and liftscorrespondences to metric 3D via the surface model. It then propagatesthese map-anchored correspondences to intermediate frames with opticalflow, producing absolute, metrically scaled poses at every frame with-out GPS or post-hoc alignment. We also introduce the MovingDroneDataset, a large-scale benchmark pairing photorealistic UAV sequenceswith dense 6-DoF ground truth and co-registered multi-modal geodataincluding multi-temporal orthophotos. On MovingDrone and real-worldbenchmarks, OrthoTrack runs in real time on a single GPU. It outper-forms all baselines by a large margin, even those receiving oracle scaleand alignment. By relying on publicly available geodata, OrthoTrackenables deployment to new regions without site-specific adaptation.

ECCV 2026

OpenCVL: An Open, Diverse, and Large-Scale Dataset for Fine-Grained Cross-View Localization

Zimin Xia, Mubariz Zaffar, Junsheng Fu, Alexandre Alahi, Julian Kooij

Fine-grained Cross-View Localization (CVL) estimates theprecise position and orientation of a ground-level image by aligningit with geo-referenced aerial imagery, offering a scalable alternative toGlobal Navigation Satellite Systems (GNSS) in challenging urban envi-ronments. Existing datasets rely on data collected with high-end sensorsuites, which inherently limit image diversity and scalability. While in-the-wild images are abundant, their noisy geo-tags make them unsuit-able for reliable evaluation. To bridge this gap, we introduce OpenCVL,a large-scale, diverse, and open dataset containing 617,388 ground-aerialimage pairs spanning 41 cities across four European countries. All imagesare sourced from permissive platforms, ensuring long-term accessibilityand supporting open and reproducible research. The training set com-bines images captured with high-end sensors with diverse in-the-wild im-agery. We further develop a data curation framework that filters and cor-rects pose annotations to construct reliable in-the-wild evaluation data.In addition, OpenCVL includes dedicated cross-area and snowy test setsto assess generalization and robustness. Experiments with a state-of-the-art CVL model on OpenCVL show that incorporating noisy in-the-wilddata consistently improves performance on clean test sets, suggesting apromising direction for scaling CVL with diverse real-world imagery.

ECCV 2026

On-Orbit Real-Time Wildfire Detection Under On-Board Constraints

Matthias Rötzer, Veronika Pörtge, Martin Ickerott, Jayendra Chorapalli, Dimitri Scheftelowitsch, Max Bereczky, Dmitry Rashkovetsky, Sai Appalla, Julia Gottfriedsen

We present a deployed system for on-orbit wildfire detec-tion aboard OroraTech’s thermal infrared Constellation Plane 1 (OTC-P1), operating under a demanding set of joint constraints: sub-megabytemodel footprint, sub-150 ms per-batch TensorRT FP16 inference on anNVIDIA Jetson Xavier NX, and an end-to-end alert pipeline targetingunder 10 minutes from satellite overpass to fire event communication.The system operates on uncalibrated mid-wave infrared (MWIR) single-band imagery at 200 m ground sampling distance, where fires frequentlyappear as sub-pixel or single-pixel thermal anomalies under extremeclass imbalance—challenges not addressed by the contextual thermal-thresholding pipelines (MODIS, VIIRS) that currently dominate opera-tional fire monitoring.We present an empirical study of lightweight dense representation learn-ing for this regime using a proprietary nine-satellite MWIR dataset. Wecompare dense masked autoencoding (DenseMAE) and a hybrid Dense-MAE+EMA (exponential moving average) distillation variant, and eval-uate representations via linear probing and full-distribution pixel-levelaverage precision (AP) under extreme class imbalance. DenseMAE pre-training enables compact downstream models that sit on the latency–accuracy Pareto frontier: our fastest SSL-pretrained model achieves 0.640test AP and 0.69 event-level Fire-F1 with 65.34 ms TensorRT FP16 la-tency per batch (224 × 224, B=8) and a 0.52 MB engine, achieved with-out pruning or compression. The best-performing configuration reaches0.699 test AP and 0.744 Fire-F1 while remaining below 1 MB. Bothoutperform a supervised baseline (up to 0.650 AP) under comparableconstraints.

ECCV 2026

MPO: Single-Stream Policy Optimization for Efficient Text-to-Image Alignment

Lishuai Gao, Jie Hu, Cong Wei, Yujie Zhong, Yibo Zhao, Zan Gao, Xiaoming Wei

Online reinforcement learning (RL) has become an effectivetool for aligning text-to-image models with human preferences, but itsefficiency is often limited by group-relative variance reduction. Exist-ing methods generate multiple trajectories per prompt to estimate localbaselines, which introduces synchronization overhead and can weaken thelearning signal when group rewards become homogeneous. We proposeMonolithic Policy Optimization (MPO), a group-free online RL frame-work based on a single-stream training principle: one prompt, one trajec-tory, and one policy update. MPO retains stochastic differential equation(SDE) exploration within each trajectory, while replacing group-wisebaselines with a persistent Bayesian value tracker. The tracker main-tains a history-aware reward estimate for each prompt and adapts itsuncertainty using a Girsanov-inspired policy-drift proxy, providing stableglobal advantage estimation under non-stationary policy updates. Acrosscompositional generation, visual text rendering, and human-preferencealignment benchmarks, MPO consistently improves alignment qualityover group-based baselines. It also delivers a 26× wall-clock speedupand a 5× sample-efficiency gain under matched training settings.

ECCV 2026

Leveraging Phase Information to Boost Unrolled Network Learning for Image Deblurring

Samira Malek, Haichuan Zhang, Chul Lee, Vishal Monga

While most image deblurring techniques directly restore thespatial image variable, we propose an amplitude and phase decomposi-tion recognizing the importance of accurate phase estimation in recover-ing sharp image details. To that end, we first develop novel linear min-imum mean squared (LMMSE) estimators of the amplitude and phaseof the blurred, noisy image observation. An iterative optimization algo-rithm follows that recovers the sharp image using the aforementionedLMMSE estimators. Finally, matrix parameters that are statistically de-termined and fixed in the iterative algorithm are now learned using atraining dataset of clean and degraded observations. Our deblurring en-gine is dubbed UPADNet – Unrolled Phase and Amplitude Decompo-sition Network, such that each iteration of the underlying phase andamplitude recovery algorithm is parameterized and trained end-to-end.Experiments over benchmark evaluation datasets such as GoPro, Real-Blur and COCO datasets confirm that UPADNet outperforms state ofthe art deep networks including those based on algorithm unrolling inthe image domain. The benefits of UPADNet are even more pronouncedin high noise and limited training data regimes.

ECCV 2026

Leveraging Dark Knowledge for Intrinsic Multimodal Out-of-Distribution Detection

Nimeshika Udayangani Hewa Dehigahawattage, Sarah Erfani, Christopher Leckie

Out-of-distribution (OOD) detection is crucial for the safedeployment of deep neural models in applications such as autonomousdriving. With the emerging multimodal nature of modern applications,recent attention has shifted toward OOD detection in multimodal set-tings. However, current multimodal OOD detection methods fail to fullyexploit the synergy among modalities: they treat all modalities equally,disregarding their varying detection performance, and they are unableto capture the diverse uncertainty information encoded at the logit level.In this paper, we propose to exploit the dark knowledge within unimodalexperts as the key to revealing their synergy. To this end, we introducea self multimodal OOD distillation framework, which leverages logits asuncertainty-aware soft targets to train a holistic model that operates inthe joint embedding space of all modalities. Specifically, the proposedframework accounts for the negative effects of underperforming modal-ities and effectively fuses both the rich feature-level knowledge and thelogit-level knowledge of modalities. As a result, our method improvesthe performance of current state-of-the-art multimodal OOD detectionmethods, achieving gains of up to 30% across diverse OOD detectionbenchmarks, spanning two tasks and five multimodal OOD datasets.

ECCV 2026

InsertAnywhere: Geometrically Grounded and Optics-Aware Video Object Insertion

Hoiyeong Jin, Hyojin Jang, Junha Hyung, Jeongho Kim, Kinam Kim, Dongjin Kim, Huijin choi, Hyeonji Kim, Choo Jaegul

Recent advances in diffusion models have enabled impressivevideo editing capabilities, yet production-grade Video Object Insertion(VOI) remains challenging due to inadequate 4D scene understandingand a lack of proper optical interactions, such as shadows and reflec-tions. To address these limitations, we present InsertAnywhere, a com-prehensive VOI framework that achieves geometrically grounded objectplacement and optics-aware video synthesis. Our approach first lever-ages a 4D-aware mask generation module that allows users to anchor anobject’s 3D pose in a single frame. The framework automatically prop-agates this placement across the video, accurately handling local scenedynamics and occlusions. To synthesize realistic physical lighting inter-actions, we introduce Optics-Aware Representation Alignment, a novelstrategy that utilizes an extended mask to guide feature extraction, en-abling optical effects to seamlessly extend beyond the inserted object’sboundary. Finally, to overcome the lack of training data for such phenom-ena, we construct and open-source ROSE++, a specialized quadrupletdataset tailored for the supervised learning of optical effects. Extensiveexperiments demonstrate that InsertAnywhere produces geometricallyplausible and photometrically realistic insertions in complex real-worldscenarios, significantly outperforming existing research and commercialgenerative tools.

ECCV 2026

GuideMe: Benchmarking Multi-Domain Task Guidance and Intervention in Streaming Video

Fang Liu, Jinpeng Chen, Ke Xu, Yuhao Liu, Huankang Guan, Xudong Lu, Yang Bo, Gerhard P. Hancke, Rui Liu, Rynson Lau

While multimodal Large Language Models (MLLMs) excel at offline video understanding, an interesting question of how far they are from serving as a real-time procedural coach remains unknown. Such a role typically requires an MLLM to continuously monitor the execution, detect mistakes, and provide corrective guidance in a closed-loop interaction. In this paper, we construct GuideMe, the first multi-domain benchmark for streaming video that supports training and evaluation of MLLMs for closed-loop interactive task guidance. It comprises 2,458 videos spanning 223.7 hours across diverse domains (e.g., cooking, object manipulation, daily-life guidance, and fitness), with 47,775 interaction samples covering next-step instructions, completion feedback, error detection, and corrective guidance. To evaluate existing models on GuideMe, we design a three-component assessment framework to measure the capabilities of representative MLLMs, which consists of temporal-semantic bipartite matching for sequence-level alignment, behavioral classification for intervention timing, and LLM-as-a-Judge for content quality. Extensive experiments highlight a critical performance asymmetry: despite excelling at providing instructions, existing MLLMs consistently fail to identify execution errors and respond with corrective feedback. Code and data are released at (cid:140)homepage.

ECCV 2026

FlowerDance: MeanFlow for Efficient and Refined 3D Dance Generation

Kaixing Yang, Xulong Tang, Ziqiao Peng, Xiangyue Zhang, Chubin Chen, xukun zhou, Puwei Wang, Hongyan Liu, Jun He

Music-to-dance generation aims to translate auditory sig-nals into expressive human motion. Despite promising progress, existingmethods remain underexplored in achieving high-quality generation un-der a strict efficiency budget, due to (i) generative strategies that sup-port high-fidelity few-step sampling, and (ii) per-step model architecturesthat are optimized for efficient yet refined long-horizon motion synthesis,thereby degrading downstream user experience in terms of responsive-ness and visual fidelity. Thus, we propose FlowerDance, which not onlygenerates refined motion with physical plausibility and artistic expres-siveness, but also achieves significant generation efficiency on inferencespeed and memory utilization. Specifically, FlowerDance combines Mean-Flow with Physical Consistency Constraints, which enables high-qualitymotion generation with only a few sampling steps. Moreover, Flower-Dance leverages a simple but efficient model architecture with BiMamba-based backbone and Channel-Level Cross-Modal Fusion, which generateslong-horizon dance with efficient non-autoregressive manner. Meanwhile,FlowerDance supports motion editing, enabling users to interactively re-fine dance sequences. Extensive experiments on AIST++ and FineDanceshow that FlowerDance achieves state-of-the-art results in both motionquality and generation efficiency. Code is available at https://sun-happy-ykx.github.io/FlowerDance/.

ECCV 2026

FixAnything: 3D-Consistent Rendering Refinement via Video Generative Priors

Khiem Vuong, Deva Ramanan, Srinivasa G. Narasimhan

Rendering views using 3D scene representations such as Gaus-sian Splatting (3DGS), Neural Radiance Fields (NeRF), meshes, or evenpoint clouds produces artifacts when input views are sparse or targetviews lie far from the input. Recent work mitigates these artifacts usingdiffusion-based generative priors, but is specialized to individual repre-sentations and require custom architectures or extensive retraining. Wepresent FixAnything, a single model for fixing a wide range of render-ing artifacts. It does so by repurposing a pretrained video generativemodel, leveraging its implicit multi-view priors with only minimal mod-ification and lightweight finetuning. Our key insight is that even noisily-rendered sequences preserve camera motion and coarse scene structure,allowing cleanup to be formulated as video-to-video translation. To con-trol what scene structure should be preserved, we introduce a binarymask denoting the clean pixels, enabling the model to anchor its outputto high-quality inputs (e.g. training views) while refining the rest. To en-courage FixAnything to produce 3D-consistent renderings that supportdownstream reconstruction, we use camera pose accuracy (recovered viastructure-from-motion) as a reward signal for direct preference optimiza-tion (DPO). Across four distinct 3D representations, FixAnything con-sistently improves rendering quality with lightweight finetuning, demon-strating that a single generalist video prior can replace multiple specialistrefinement pipelines. The simplicity of the framework enables immediateadoption of stronger future video models without architectural redesign.

ECCV 2026

Estimating Individual Tree Height and Species from UAV Imagery

Jannik Endres, Etienne Laliberté, David Rolnick, Arthur Ouaknine

Accurate estimation of forest biomass, a major carbon sink, relies heavily on tree-level traits such as height and species. Unoccupied Aerial Vehicles (UAVs) capturing high-resolution imagery from a single RGB camera offer a cost-effective and scalable approach for mapping and measuring individual trees. We introduce BIRCH-Trees, the first benchmark for individual tree height and species estimation from tree-centered UAV images, spanning three datasets: temperate forests, tropical forests, and boreal plantations. We also present DINOvTree, a unified approach using a Vision Foundation Model (VFM) backbone with task-specific heads for simultaneous height and species prediction. Through extensive evaluations on BIRCH-Trees, we compare DINOvTree against commonly used vision methods, including VFMs, as well as biological allometric equations. We find that DINOvTree achieves top overall results with accurate height predictions and competitive classification accuracy while using only 54% to 58% of the parameters of the second-best approach.

ECCV 2026

E3VS-Bench: A Benchmark for Viewpoint-Dependent Active Perception in 3D Gaussian Splatting Scenes

Koya Sakamoto, Taiki Miyanishi, Daichi Azuma, Shuhei Kurita, Shu Morikuni, Naoya Chiba, Motoaki Kawanabe, Yusuke Iwasawa, Yutaka Matsuo

Visual search in 3D environments requires embodied agentsto actively explore their surroundings and acquire task-relevant evidence.However, existing visual search and embodied AI benchmarks, includ-ing EQA, typically rely on static observations or constrained egocen-tric motion, and thus do not explicitly evaluate fine-grained viewpoint-dependent phenomena that arise under unrestricted 5-DoF viewpointcontrol, such as disambiguating object attributes observable only fromspecific angles. To address this limitation, we introduce E3VS-Bench,a benchmark for embodied 3D visual search where agents must controltheir viewpoints in 5-DoF to gather viewpoint-dependent evidence forquestion answering. E3VS-Bench consists of 99 high-fidelity 3D scenesreconstructed using 3D Gaussian Splatting and 2,014 question-drivenepisodes. 3D Gaussian Splatting enables photorealistic free-viewpointrendering that preserves fine-grained visual details (e.g., small text andsubtle attributes) often degraded in mesh-based simulators, thereby al-lowing the construction of questions that cannot be answered from asingle view and instead require active inspection across viewpoints in5-DoF. We evaluate multiple state-of-the-art VLMs and compare theirperformance with humans. Despite strong 2D reasoning ability, all mod-els exhibit a substantial gap from humans, highlighting limitations inactive perception and coherent viewpoint planning specifically under full5-DoF viewpoint changes. The benchmark, code, and dataset are publiclyavailable at https://k0uya.github.io/e3vs-proj/.

ECCV 2026

DriftScope: Measuring The Hidden Effects of Diffusion Model Fine-Tuning

Héctor Laria, Yiping Han, Julian Santamaria, Kai Wang, Bogdan Raducanu, Joost van de weijer, Alex Gomez-Villa*

Adapting pre-trained text-to-image diffusion models, whether to learn new visual concepts or erase unwanted ones, is routinely evaluated on its intended effects alone. We argue this framing is incomplete. Through sparse autoencoder analysis and zero-shot classification, we demonstrate that adaptation systematically damages semantically unrelated concepts in ways that aggregate metrics structurally cannot surface: when damage is severe enough for FID and KID to respond, the model is already nearly unusable; when the model remains functional, FID and KID stay flat while specific classes silently suffer worst-case zero-shot accuracy drops of up to 18.9 points and concept-level distributions shift dramatically. This pattern appears at both ends of the adaptation spectrum (concept customization and concept unlearning), suggesting it is a systematic consequence of weight-level modification rather than an artifact of any particular method. To surface this hidden drift before deployment, we introduce DriftScope, a prompt-level diagnostic tool that takes any two model checkpoints and returns a ranked list of tokens whose visual concepts have shifted most between them. DriftScope optimizes a soft prompt to attribute drift at the token level without requiring access to real data or model internals. The result is an interpretable, concept-level audit that aggregate evaluation cannot provide.

ECCV 2026

Scaling Verification Can Be More Effective than Scaling Policy Learning for Vision-Language-Action Alignment

Jacky Kwok, Xilun Zhang, Mengdi Xu, Yuejiang Liu, Azalia Mirhoseini, Chelsea Finn, Marco Pavone

The long-standing vision of general-purpose robots hinges on theirability to understand and act upon natural language instructions. Vision-Language-Action (VLA) models have made remarkable progress toward thisgoal, yet their generated actions can still misalign with the given instructions.In this paper, we investigate test-time verification as a means to shrinkthe “intention-action gap.” We first characterize the test-time scaling lawsfor embodied instruction following and demonstrate that jointly scaling thenumber of rephrased instructions and generated actions greatly increasestest-time sample diversity, often recovering correct actions more efficientlythan scaling each dimension independently. To capitalize on these scalinglaws, we present CoVer, a contrastive verifier for vision–language–actionalignment, and show that our architecture scales gracefully with additionalcomputational resources and data. We then introduce CoVer-VLA, a hierar-chical test-time verification pipeline using the trained verifier. At deployment,our framework precomputes a diverse set of rephrased instructions from aVision-Language-Model (VLM), repeatedly generates action candidates foreach instruction, and then uses the verifier to select the optimal high-levelprompt and low-level action chunks. Compared to scaling policy pre-training⋆ †Equal contribution. § Equal advising.on the same data, our verification approach yields 22% gains in-distributionand 13% out-of-distribution on the SIMPLER benchmark, with a further45% improvement in real-world experiments. On the PolaRiS benchmark,CoVer-VLA achieves 14% gains in task progress and 9% in success rate.

ECCV 2026

Don't Let the Video Speak: Audio-Contrastive Preference Optimization for Audio-visual Language Models

Ami Baid, Zihui Xue, Kristen Grauman

While Audio-Visual Language Models (AVLMs) have achievedremarkable progress over recent years, their reliability is bottleneckedby cross-modal hallucination. A particularly pervasive manifestation isvideo-driven audio hallucination: models routinely exploit visual short-cuts to hallucinate expected sounds, discarding true auditory evidence.To counteract this deeply ingrained visual dominance, we propose Audio-Contrastive Preference Optimization (ACPO). This dual-axis preferencelearning framework introduces an output-contrastive objective to penal-ize visual descriptions masquerading as audio facts, alongside an input-contrastive objective that swaps audio tracks to explicitly penalize gener-ation invariant to the true auditory signal. Extensive experiments demon-strate that ACPO establishes highly faithful audio grounding and mit-igates audio hallucination. Project page: https://vision.cs.utexas.edu/projects/acpo/

ECCV 2026

ConceptWeaver: Weaving Disentangled Concepts with Flow

Jintao Chen, Aiming Hao, Xiaoqing Chen, Chengyu Bai, Chubin Chen, Yanxun Li, Jiahong Wu, Xiangxiang Chu, Shanghang Zhang

Pre-trained flow-based models excel at synthesizing complexscenes, yet lack a direct mechanism for disentangling and customizingtheir underlying concepts from one-shot real-world sources. To demystifythis process, we first introduce a novel differential probing technique toisolate and analyze the influence of individual concept tokens on the ve-locity field over time. This investigation yields a critical insight: the gen-erative process is not monolithic but unfolds in three distinct stages. Aninitial Blueprint Stage establishes low-frequency structure, followed bya pivotal Instantiation Stage where content concepts emerge with peakintensity and become naturally disentangled, creating an optimal windowfor manipulation. A final concept-insensitive Refinement Stage thensynthesizes fine-grained details. Guided by this discovery, we proposeConceptWeaver, a framework for one-shot concept disentanglement.ConceptWeaver learns concept-specific semantic offsets from a single ref-erence image using a stage-aware optimization strategy that aligns withthe three-stage framework. These learned offsets are then deployed dur-ing inference via our novel ConceptWeaver Guidance (CWG) mechanism,1 ∗Equal contribution; † Project Lead; ‡ Co-corresponding author.which strategically injects them at the appropriate generative stage. Ex-tensive experiments validate that ConceptWeaver enables high-fidelity,compositional synthesis and editing, demonstrating that understandingand leveraging the intrinsic, staged nature of flow models is key to un-locking precise, multi-granularity content manipulation. We will releaseour code at https://github.com/JasperChennn/ConceptWeaver.

ECCV 2026

Color Pass-Through via Camera-Display Coupling

Ruikang Li, Molin Li, Jiarui Wu, Zhe Wei, Pengpeng Liu, Tianfan Xue

When a real-world scene is captured by a smartphone cam-era and viewed on its screen, the displayed image often differs noticeablyfrom the original scene in color, brightness, and contrast. This gap per-sists despite substantial advances in both modern cameras and displays.A key reason is that most pipelines factor the high-dimensional capture-to-display process into two separately calibrated camera and displaystages, and then connect them through low-dimensional color transforms,leading to information bottlenecks and inevitable error accumulation. Toaddress this systemic challenge, we propose Color Pass-Through, anend-to-end learned framework that operates directly on captured images.Our key insight is to treat the camera and display as a coupled systemrather than calibrating them in isolation. Coupling the camera and dis-play yields two practical advantages: (1) it brings the entire real-worldscenes to the display via end-to-end optimization, and (2) it allows effi-cient one-step calibration for each distinct observer via complete capture-to-display path. We validate Color Pass-Through using both digi-tal and human observers. Compared with representative baselines, ourmethod achieves an average gain of +2.0 points on a 5-point user-studyand more than 2× improvement on quantitative metrics, demonstratingimproved reproduction of the perceived color of the original scene. Seeproject page: https://lyricccco.github.io/color-pass-through/

ECCV 2026

CameraAnything: Refilming Videos with Arbitrary Camera Control

Yixuan Li, Yanhong Zeng, Ka Leong Cheng, Jiayi Zhu, Hanlin Wang, Wen Wang, Yihao Meng, Hao OUYANG, Qiuyu Wang, Yue Yu, Zidong Wang, Yiyuan Zhang, Yujun Shen, Dahua Lin

We introduce CameraAnything, the first unified frame-work for camera controlled video editing that enables joint control ofboth intrinsic and extrinsic camera parameters. Existing approaches ei-ther rely on expensive 3D reconstruction to achieve full camera function-ality or restrict editing to extrinsic parameter manipulation. Moreover,the coupled influence of intrinsic and extrinsic parameters on video ap-pearance makes disentangled modeling particularly challenging. To ad-dress this, we adopt per-pixel Plücker ray injection alongside resolution-aware 3D RoPE in self-attention, building both camera conditioning andspatial positional encoding on the target latent to jointly control cam-era position, focal length, and native resolution editing without croppingor outpainting. To overcome the scarcity of paired training data, wefurther develop a scalable synthetic pipeline that constructs diverse dy-namic scenes through structured multi-camera recording and generatessynchronized videos with varied camera configurations. With a tailoredorthogonal training strategy, CameraAnything enables expressive videoreshooting with arbitrary viewpoint control, focal length adjustment, res-olution adaptation, and multi-shot transitions within a single generationprocess, offering strong practical value for cinematic video editing andcross-platform content adaptation in video production.

ECCV 2026

Articulat3D: Reconstructing Articulated Digital Twins From Monocular Videos with Geometric and Motion Constraints

Lijun Guo, Haoyu Zhao, Xingyue Zhao, Rong Fu, Linghao Zhuang, Siteng Huang, Zhongyu Li, Hua Zou

Building high-fidelity digital twins of articulated objects fromvisual data remains a central challenge. Existing approaches depend onmulti-view captures of the object in discrete, static states, which severelyconstrains their real-world scalability. In this paper, we introduce Articu-lat3D, a novel framework that constructs such digital twins from casuallycaptured monocular videos by jointly enforcing explicit 3D geometric andmotion constraints. We first propose Motion Prior–Driven Initialization,which leverages 3D point tracks to exploit the low-dimensional structureof articulated motion. By modeling scene dynamics with a compact set ofmotion bases, we facilitate soft decomposition of the scene into multiplerigidly-moving groups. Building on this initialization, we introduce Ge-ometric and Motion Constraints Refinement, which enforces physicallyplausible articulation through learnable kinematic primitives parameter-ized by a joint axis, a pivot point, and per-frame motion scalars, yieldingreconstructions that are both geometrically accurate and temporally co-herent. Extensive experiments demonstrate that Articulat3D achievesstate-of-the-art performance on synthetic benchmarks and real-worldcasually captured monocular videos, significantly advancing the feasibilityof digital twin creation under uncontrolled real-world conditions. Here isour project page.

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