ECCV 2026 论文列表

3D Gaussian Splatting (3DGS) has emerged as a promis-ing paradigm for reconstructing streamable free-viewpoint video (FVV)from multi-view videos. However, 3DGS-based FVVs typically lack userinteraction and editing capabilities, which diminishes the immersive ex-perience. Recent research has integrated language features from CLIPinto 3DGS via distillation, enabling open-vocabulary queries and sup-porting many downstream applications. Nevertheless, the stringent re-quirements of FVV, low frame size and high FPS, make current languageGaussian representations unsuitable for language-embedded FVV. In thispaper, we propose DLGStream, a novel language-embedded FVV repre-sentation that streams time-varying language features alongside Gaus-sian attributes to support 4D environment interaction, scene editing, andspatial intelligence. Specifically, we propose a dual-opacity dynamic lan-guage Gaussian representation, which maintains two opacity attributesfor color and language features to deal with performance degradationthat occurs when colors and features are jointly optimized. Further-more, we introduce an interpolation-based deformation field to reducetemporal redundancy. This deformation field can also be used for 4Dframe interpolation, boosting FVV sequences from low to high FPS. Ex-perimental results demonstrate that DLGStream achieves superior per-formance in both on open-vocabulary segmentation and reconstructionquality with an average frame size of merely 43 KB. The code is availableon https://github.com/kkkzh/DLGStream.

We propose Discrete Diffusion Bridges (DDB), a novel frame-work designed to resolve the fundamental spatiotemporal misalignmentof standard discrete diffusion in image translation and generation. Bycorrupting data into a pure mask state via a random schedule, the con-ventional forward process induces a twofold misalignment: spatially, thispure-mask destination entirely discards the rich structural priors of thesource image; temporally, the random masking order inherently contra-dicts the “easy-first, hard-last” decoding mechanism used during infer-ence. To address this, DDB constructs a direct and efficient trajectorybetween domains. Spatially, we introduce a hybrid absorption mechanismthat redefines the absorbing state to a stochastic mixture of mask andsource tokens, effectively injecting source prior as spatial anchors into thelatent space. Temporally, we design an information-guided noise schedulethat quantifies semantic variation to prioritize the corruption of high-information regions at earlier timesteps. This ensures the model learnsto resolve difficult semantic changes using robust context from invariantregions. Extensive experiments validate the versatility and robustnessof our framework across diverse generative paradigms. DDB effectivelybalances edit alignment with structural fidelity across both text-guidedsemantic manipulation and pure structural image translation, while in-herently complementing text-to-image generation and guaranteeing ro-bust high-quality decoding under extremely low sampling steps. Codeand models are available at https://github.com/HKU-HealthAI/DDB.

Recent progress in 3D Gaussian Splatting (3DGS) has en-abled dense visual SLAM with pinhole cameras, yet most pipelines arenot designed for panoramic imagery. We present Cube-Splat, the firstpanoramic GS-SLAM framework that factorizes each 360◦ frame into acubemap of four fixed-orientation virtual pinhole views sharing a singleoptical center. By designating the front face as the primary pose state, weaccumulate gradients from all faces via an adjoint mapping, thereby en-abling multi-face observations to coherently update a single state whilestrictly preserving cross-view geometric consistency. Concurrently, ourmapping module densifies and optimizes anisotropic Gaussians using ag-gregated cubemap rays for high-fidelity, dense reconstruction. Further-more, to rigorously evaluate panoramic SLAM under diverse and chal-lenging conditions, we introduce SynPano, a highly scalable, photoreal-istic synthetic dataset featuring parameterized complex trajectories andmulti-modal ground truth. Extensive evaluations on two public bench-marks (PALVIO and OmniBlender) and our SynPano dataset, collec-tively encompassing both indoor and outdoor scenes, demonstrate thatCube-Splat achieves state-of-the-art (SOTA) performance in tracking ac-curacy and reconstruction fidelity. Both the source code and the SynPanodataset are available at https://github.com/guoxf304/CubeSplat.

Existing image forgery detection (IFD) methods either ex-ploit low-level, semantics-agnostic artifacts or rely on multimodal largelanguage models (MLLMs) with high-level semantic knowledge. Althoughnaturally complementary, these two information streams are highly hetero-geneous in both paradigm and reasoning, making it difficult for existingmethods to unify them or effectively model their cross-level interactions.To address this gap, we propose ForenAgent, a multi-round interactiveIFD framework that enables MLLMs to autonomously invoke, execute,and iteratively refine Python-based low-level tools around the detectionobjective, thereby achieving more flexible and interpretable forgery analy-sis. ForenAgent adopts a two-stage training pipeline with Cold Start andReinforcement Fine-Tuning to progressively improve tool interaction andreasoning adaptability. We design a human-inspired dynamic reasoningloop with global perception, local focusing, iterative probing, and holisticadjudication, and implement it as both a data-sampling strategy anda task-aligned process reward. To support training and evaluation, webuilt FABench, a heterogeneous agent-forensics dataset with 100k imagesand about 200k agent-interaction question-answer pairs. Experimentsshow that ForenAgent exhibits emergent tool-use competence and re-flective reasoning on challenging IFD tasks when assisted by low-leveltools, charting a promising route toward general-purpose IFD. The codeis available at https://github.com/zfr00/ForenAgent.

Multi-modal Large Language Models (MLLMs) have signifi-cantly advanced video reasoning, yet Video Question Answering (VideoQA)remains challenging due to its demand for temporal causal reasoningand evidence-grounded answer generation. Prevailing end-to-end MLLMframeworks lack explicit structured reasoning between visual percep-tion and answer derivation, causing severe hallucinations and poor in-terpretability. Existing methods also fail to address three core gaps:faithful visual clue extraction, utility-aware clue filtering, and end-to-end clue-answer alignment. Inspired by hierarchical human visual cogni-tion, we propose ClueNet, a clue-aware video reasoning framework with atwo-stage supervised fine-tuning paradigm without extensive base modelmodifications. Decoupled supervision aligns clue extraction and chain-based reasoning, while inference supervision with an adaptive clue filterrefines high-order reasoning, alongside lightweight modules for efficientinference. Experiments on NExT-QA, STAR, and MVBench show thatClueNet outperforms state-of-the-art methods by ≥ 1.1%, with strongbenchmark generalization, hallucination mitigation, and improved infer-ence efficiency. This work bridges the perception-to-generation gap inMLLM video understanding, providing an interpretable, faithful reason-ing paradigm for real-world VideoQA applications.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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/.

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.

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.

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/.

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.