ECCV 2026 论文列表

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/

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.

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.

Learning-based stereo matching models struggle in under-water environments due to scarce in-domain data and the difficulty ofextracting discriminative correspondences from degraded imagery. In thiswork, we present AquaStereo, a perception-enhanced framework witha data simulation pipeline and a self-distillation strategy that jointly ad-dress data scarcity and feature degradation in underwater stereo match-ing. First, a depth-conditioned diffusion pipeline renders underwaterstereo pairs while preserving binocular geometry, with a lightweight left-right consistency module ensuring geometric alignment. Training on thissynthetic corpus effectively narrows the terrestrial–underwater gap andimproves zero-shot robustness. Second, a frozen binocular teacher trainedon clean terrestrial pairs guides a student exposed to rendered underwa-ter pairs with perturbations. A stage-weighted sequence loss is performedto align the student’s disparities with the teacher’s geometry, while aclean-branch supervision with shared pseudo targets prevents scale drift.To further enhance feature stability under turbidity and low texture,we introduce learnable perception frames, a perception-enhanced featureformulation that constructs robust matching descriptors by fusing tem-poral cues from two auxiliary views encoded by a video backbone withsemantic features extracted by a strong image encoder. Extensive experi-ments demonstrate that AquaStereo substantially improves robustnessand zero-shot generalization in challenging underwater scenarios.

Embodied visual navigation requires agents with real-time perception, logical reasoning and efficient decision-making capabilities. Existing collaborative modular frameworks struggle to balance accurate decision-making and efficient online execution, leading to error propagation and inference blocking. To tackle this limitation, we propose AMCoNav, an Asynchronous Multi-module Collaborative Framework that unites real-time lightweight backbone networks with on-demand zeroshot large model modules (ZLMM) for robust and real-time embodied navigation. The framework has four tightly coupled components: (1) a Real-Time Multimodal Decision Module (RMDM) for continuous decision making; (2) Zero-shot Large Model Modules (ZLMM) for on-demand asynchronous reasoning; (3) a latency-tolerant Multimodal Shared Context (MSC) for cross-module coordination; and (4) a Bayesian Probabilistic Decision Fusion Module (BPDFM) for robust score fusion and mode switching. Experiments on three representative embodied navigation benchmarks (HM3D-OVON, SG3D, and GOAT-Bench) demonstrate that AMCoNav consistently improves success rates while maintaining real-time execution. The most significant gain appears on HM3DOVON, where AMCoNav achieves an average 8.7% gain over prior stateof-the-art results, with 60.0%/60.0%/53.3% success rate (SR) on Val Seen/Val Seen Synonyms/Val Unseen. Ablation results show that asynchronous guidance from MSC and ZLMM and the BPDFM both contribute to performance improvement, and their combination yields the best overall results. These results show that asynchronous multi-module collaboration can improve navigation capability without sacrificing realtime responsiveness.

The ability to remotely measure an animal’s size and shape(i.e. its morphology) in its natural habitat is of key importance for ap-plications such as conservation, re-identification, and biomechanical un-derstanding. Monocular depth estimation (MDE) and 3D reconstructionapproaches can be used. This is useful to generate accurate deformablemesh models or to characterize biologically important parameters such assexual dimorphism or the size distribution of populations. Whilst depthestimation and 3D reconstruction have been extensively studied as coretopics in computer vision, ranging from early work on simple, rigid ob-jects, to more recent work on human and animal reconstruction, themajority of existing animal models are trained on video/image baseddatasets that lack metric scale and ground truth e.g. from camera trapimages or from public videos. In addition, they do not fully reflect thechallenges of accurately estimating the focal animal’s scale when it is dis-tant from a camera. To address this limitation, we present WildDepth,a multimodal dataset and benchmark suite for depth estimation, behav-ior detection, and 3D reconstruction from diverse categories of animalsranging from domestic to wild environments with synchronized RGB andLiDAR. We provide three focused benchmarks: (1) monocular depth es-timation with per-distance and temporal stability analysis, (2) behav-ior detection, (3) 3D reconstruction and densification. Our results showthat large-scale MDE models degrade significantly at long range, whileLiDAR-anchored fusion reduces metric error by up to 25% – 30% RMSEin mid-range scenarios. Our aim is to enable camera trap data to bemore accurately ‘lifted’ to 3D morphometrics, unlocking a new era ofscale-aware animal modelling.⋆ M. Aamir, N. Muramatsu, and S. Shin contributed equally and are listed alpha-betically by surname.

The ability to remotely measure an animal’s size and shape(i.e. its morphology) in its natural habitat is of key importance for ap-plications such as conservation, re-identification, and biomechanical un-derstanding. Monocular depth estimation (MDE) and 3D reconstructionapproaches can be used. This is useful to generate accurate deformablemesh models or to characterize biologically important parameters such assexual dimorphism or the size distribution of populations. Whilst depthestimation and 3D reconstruction have been extensively studied as coretopics in computer vision, ranging from early work on simple, rigid ob-jects, to more recent work on human and animal reconstruction, themajority of existing animal models are trained on video/image baseddatasets that lack metric scale and ground truth e.g. from camera trapimages or from public videos. In addition, they do not fully reflect thechallenges of accurately estimating the focal animal’s scale when it is dis-tant from a camera. To address this limitation, we present WildDepth,a multimodal dataset and benchmark suite for depth estimation, behav-ior detection, and 3D reconstruction from diverse categories of animalsranging from domestic to wild environments with synchronized RGB andLiDAR. We provide three focused benchmarks: (1) monocular depth es-timation with per-distance and temporal stability analysis, (2) behav-ior detection, (3) 3D reconstruction and densification. Our results showthat large-scale MDE models degrade significantly at long range, whileLiDAR-anchored fusion reduces metric error by up to 25% – 30% RMSEin mid-range scenarios. Our aim is to enable camera trap data to bemore accurately ‘lifted’ to 3D morphometrics, unlocking a new era ofscale-aware animal modelling.⋆ M. Aamir, N. Muramatsu, and S. Shin contributed equally and are listed alpha-betically by surname.

Foundation model pseudo-labeling—labeling data strictly viazero-shot inference—enables massive scale, but performance is under-mined by hallucinations that evade standard thresholds. To eliminatethese errors, we introduce the Turing-inspired Label Imitation Game(LIG), a framework that formalizes pseudo-label pruning as an adversar-ial interrogation. Rather than filtering labels via isolated thresholds, weuse the LIG to train a Turing Test Network (TTN), a task-agnostic“judge” that evaluates candidate pseudo-labels within a dataset-wide con-text. Experiments across four diverse datasets demonstrate the TTN’srobustness, consistently enhancing label accuracy for three state-of-the-art vision-language models without costly supervision or retraining. Cru-cially, we demonstrate that learned semantic-contextual logic is a robustalternative to spatial-geometric verification, enabling a unique zero-shottask transfer capability—a TTN trained strictly on image classificationdatasets can effectively prune complex object detection pseudo-labels.This pruning yields F1 -score gains of 28% for the worst-performing base-line categories and 44% with task-specific fine-tuning. Significantly, wealso observe Category Revival, where the TTN pruning “detoxifies” thetraining signal for downstream models and enables them to recover fromzero recall on transfer-vulnerable classes. The pre-trained TTN modelsand code are available at https://github.com/voxel51/ttn.

Recent advances in multimodal large language models (MLLMs)have shown impressive reasoning capabilities. However, further enhanc-ing existing MLLMs necessitates high-quality vision-language datasetswith carefully curated task complexities, which are both costly and chal-lenging to scale. Although recent self-improving models that iterativelyrefine themselves offer a feasible solution, they still suffer from two corechallenges: (i) most existing methods augment visual or textual data sep-arately, resulting in discrepancies in data complexity (e.g., over-simplifieddiagrams paired with redundant textual descriptions); and (ii) the evo-lution of data and models is also separated, leading to scenarios wheremodels are exposed to tasks with mismatched difficulty levels. To ad-dress these issues, we propose SyncLoop, an automatic, closed-loopself-improving framework that jointly evolves both training data andmodel capabilities. Specifically, given a base dataset and a base model,SyncLoop enhances them by a cross-modal data evolution loop and adata-model evolution loop. The former loop expands the base datasetby generating complex multimodal problems that combine structuredtextual sub-problems with iteratively specified geometric diagrams ormathematical functions, while the latter loop adaptively selects the gen-erated problems based on the performance of the base model, to conductsupervised fine-tuning and reinforcement learning alternately. Conse-quently, our method continuously refines its model and training data,and consistently obtains considerable performance gains across multiplemathematical reasoning benchmarks.

Recent progress in video large language models (Video-LLMs) has enabled strong offline reasoning over long and complex videos. However, real-world deployments increasingly require streaming perception and proactive interaction, where video frames arrive online and the system must decide not only what to respond, but also when to respond. In this work, we revisit proactive activation in streaming video as a structured sequence modeling problem, motivated by the observation that temporal transitions in streaming video naturally form span-structured activation patterns. To capture this span-level structure, we model activation signals jointly over a sliding temporal window and update them iteratively as new frames arrive. We propose STRIDE (Structured Temporal Refinement with Iterative DEnoising), which employs a lightweight masked diffusion module at the activation interface to jointly predict and progressively refine activation signals across the window. Extensive experiments on diverse streaming benchmarks and downstream models demonstrate that STRIDE shows more reliable and temporally coherent proactive responses, significantly improving “when-to-speak” decision quality in online streaming scenarios. The STRIDE codebase and demonstrations are available on our project webpage.

Weight-only post-training quantization (PTQ) has proven highly effective for deploying large language models under memory-bound inference constraints. However, extending PTQ to Multimodal LLMs at ultra-low bit-widths (2–3 bits) presents a significant challenge. Existing uniform-precision methods suffer severe accuracy collapse due to their implicit assumption that all output channels within a layer are equally sensitive to quantization noise. This uniform bit-width assignment fails to account for the heterogeneous sensitivity of different channels. By lifting the analysis to the Transformer block level, we reveal a two-level anisotropy—inter-layer and intra-layer inter-channel. This anisotropy is further amplified by an outlier–sensitivity resonance mechanism, causing different output channels to contribute vastly different amounts of quantization error to the block output. Together, these findings provide the rigorous foundation for per-output-channel mixed-precision quantization. Building on the derived optimality condition, we propose GloBitQ, a framework that unfolds the anisotropic loss into principled bit allocation. It integrates three intermediate proxies: power-law balanced layer weighting, rank–value hybrid channel scoring, and global top-τ bit assignment. The resulting allocation provably approximates the optimum of the block-level quantization objective. On six multimodal benchmarks spanning Qwen2-VL-7B, Qwen2.5-VL-7B, and LLaVA-OneVision, GloBitQ consistently surpasses GPTQ, GPTAQ, and VLMQ: the 3-bit model performs within 2.1% of full precision(81.70 vs. 83.76), while the 2-bit model remains stable where prior methods collapse—all within a calibration-only, finetuning-free, hardware-compatible pipeline. Code is available in the supplementary material.

While modern multimodal models integrate vision with lan-guage, audio, or touch, olfaction remains largely unexplored due to thelack of paired visuo-olfactory data. We introduce SmellNet-V, a scal-able visuo-olfactory dataset built on the insight that odor identity islargely invariant to visual transformations within a semantic category.This allows us to synthetically pair smell-only samples with semanticallyaligned in-the-wild web images, converting a unimodal olfactory datasetinto a cross-modal benchmark without costly co-collection. Building onthis dataset, we propose See & Sniff, a self-supervised framework thatlearns joint visuo–olfactory representations via dense local alignment andnaturally produces smell saliency maps for spatial grounding of odorsources. We further introduce pixel-level smell localization task and abenchmark for evaluation. Our method surpasses smell-only baselines by7% in smell classification from smell alone and generalizes to cross-modalretrieval and smell localization, establishing visuo-olfactory learning asa new direction in multimodal perception.

Continual learning in visual question answering (VQACL) re-quires a single vision–language model to acquire new multimodal reason-ing skills from a task stream while retaining prior capabilities. However,naïve sequential finetuning suffers from catastrophic forgetting. Existingcontinual VQA methods primarily rely on replay or parameter regulariza-tion but largely overlook how task-specific updates accumulate and inter-act in parameter space, particularly whether successive updates are syn-ergistic or conflicting across layers. To address this, we introduce SCoT(Similarity-guided Conflict-aware Task Consolidation), a continual learn-ing framework that represents each task as a parameter update relative to apretrained anchor model and integrates tasks through layer-wise parameter-space reasoning. For each layer, SCoT measures alignment between incom-ing and accumulated task vectors, removes only destructive components viaconditional projection when conflicts arise, and adaptively modulates con-solidation strength using similarity-guided weighting. This preserves ben-eficial transfer while suppressing harmful interference, enabling stable yetadaptive continual learning. Experiments on VQAv2 and NExT-QA demon-strate strong continual VQA performance, reducing forgetting to near-zero(0.07 and -1.90) while achieving rare positive backward transfer (+5.64 and+6.97), outperforming strong continual-learning and task-vector baselines.Project page: https://anand-patel05.github.io/SCoT

SceneHI is a framework that lifts high-resolution, illumina-tion-aware priors from 2D diffusion models to perform 3D texture syn-thesis. It is the first to demonstrate that high-resolution textures, previ-ously limited to 2D synthesis, can be generated directly on 3D objectswithout model fine-tuning or optimization. Designed for complex, multi-object environments, SceneHI uniquely combines 3D-consistency, high-resolution fidelity, and physically plausible baked shadows within a singlegenerative pipeline. To enforce strict geometric coherence, we introducean exact analytical pixel-to-texel mapping that aligns diffusion trajecto-ries across multiple viewpoints. We utilize High-Resolution Latent Tex-tures (HRLTs) as a persistent canvas for gradually denoised textures,while camera views perform the denoising steps in latent pixel space.This ensures a shared base texture that can be subsequently refined tohigh resolution without compromising multi-view consistency. Finally, alight-aware generative pass embeds realistic geometry-consistent shad-ows directly into the atlases, bridging the gap to production workflows.SceneHI achieves high visual fidelity while reducing generation time by80% compared to existing scene-level methods. The code for our work isavailable at https://github.com/Thanos-DB/SceneHI.

Modern dense predictors largely follow a pyramidal pixel-decoding paradigm: they progressively lift low-resolution representationsto dense, high-resolution outputs. While effective, this design tightlycouples computation with output resolution, making dense predictionincreasingly costly to scale. In this work, we argue that strong denseprediction can be achieved by separating capacity from resolution: per-forming high-capacity reasoning in a compact latent space, and readingout to pixels with a lightweight operator. To instantiate this principle,we introduce Latent Dense Prediction Transformer (L-DPT), a minimalarchitecture that decouples dense decoding from pixel-space rendering.Instead of progressively decoding toward the pixel grid, L-DPT performsattention-based dense decoding entirely in latent space, keeping compu-tation largely stable as resolution increases while enabling more flexiblefeature integration from the encoder. The final output is produced viaa lightweight pixel readout, such as pixel shuffle, so scaling to extremeresolutions does not amplify decoding cost. As a result, L-DPT achievesstronger dense prediction accuracy while enabling inference on profes-sional ultra-high-resolution images up to 100 megapixels. More broadly,we view L-DPT as an instance of a scalable design principle rather thana task-specific decoder for dense prediction.

Dataset distillation (DD) condenses large corpora into com-pact, information-rich subsets for efficient training and reuse. However,under noisy supervision, DD risks condensing corrupted associations to-gether with useful signals, degrading robustness. Conventional noisy-label remedies (sample selection, loss weighting, label correction) tightlycouple noise estimation with model optimization, often require clean an-chors, and can amplify confirmation bias—assumptions that are mis-aligned with DD’s goal of compact, plug-and-play supervision. We there-fore propose a trajectory-based DD framework that jointly suppressesnoise and preserves transferable knowledge without relabeling or cleansubsets. It comprises two complementary components: Selective Guid-ance Reweighting (SGR), which fuses global forgetting patterns (second-split forgetting) with local neighborhood consistency into a progressivereweighting scheme that prioritizes clean supervision along the teachertrajectory; and Teacher-Inspired Auxiliary Targets (TIAT), which injectauxiliary residual guidance distilled from intermediate teacher dynamicsto reinforce informative signals while remaining internally consistent. To-gether, SGR and TIAT produce distilled datasets with cleaner and richerrepresentations under noisy supervision. The framework is robust, label-preserving, computationally lightweight, and broadly applicable, yield-ing consistent gains over state-of-the-art DD baselines across symmetric,asymmetric, and real-world noise.

Indoor scene generation is crucial for robot simulation andmodern interior design. However, complex layouts together with scarce3D scene data make learning-based generation challenging. Existing meth-ods often rely on hand-crafted rules or focus on isolated sub-tasks (e.g.,floorplan synthesis or single-room furnishing), producing whole-homescenes that lack global coherence, realism, and simulation readiness. Tomitigate these limitations, we propose a unified hierarchical frameworkthat decomposes indoor scene synthesis into controllable stages. First, wecurate a large-scale dataset of 300K real residential floorplans to traina large language model for whole-home floorplan generation. With de-tailed descriptions and a K-D tree–based representation, our method en-ables fine-grained, controllable whole-home floorplan generation. Build-ing upon the generated whole-home floorplan, we leverage image gener-ation models to draft furniture layouts from multi-level roaming view-points, and then generate the layouts of small manipulable objects ondifferent supporting surfaces (e.g. cabinets, desks, and dining tables) forembodied AI simulation. During furniture and object layout generation,a VLM-based refiner iteratively corrects furniture and object placement,and a 3D generative model enables flexible replacement of individualassets. We further attach basic physical attributes and simple surfacetexture and lighting setups to complete the pipeline for embodied AIuse. Experiments and user studies demonstrate that our pipeline pro-duces indoor spaces with greater layout diversity and stronger 3D designappeal, outperforming prior methods on both quantitative and qualita-tive metrics. Finally, alongside our generation pipeline, we will releasethe floorplan dataset and 5K fully furnished scenes to the community.Project page: https://kairos-homeworld.github.io/.

Estimation of Human-Object Interactions in 3D (3D HOI)is a fundamental problem in 3D computer vision with applications inAR/VR, robotics, and embodied AI. However, reconstructing these in-teractions in 3D remains challenging due to depth ambiguities, occlu-sions, and object shape variability. Existing approaches are primarilyconcerned with reprojection and contact constraints, fitting parametrichuman models and object templates to 2D images. In this paper, weexplore a different avenue. We present MILO, a framework that lever-ages the visual capabilities of Large Reconstruction Models (LRMs) torecover detailed 3D human-object interactions from a single image. Ourkey observation is that LRMs provide a powerful geometric scaffold thatpreserves relative human-object arrangement and proximity cues. Thissignificantly simplifies the reconstruction procedure, reframing the prob-lem as interpreting the LRM mesh: we segment it into human and objectcomponents, fit a parametric body model to the human part, and op-tionally align an object template to the object part (if such a templateis available). MILO achieves strong reconstruction accuracy and out-performs existing baselines across multiple benchmarks and interactionscenarios. Our code is available at https://ac5113.github.io/MILO.

Cameras capture scene-referred linear raw images, which areprocessed by onboard image signal processors (ISPs) into display-referred8-bit sRGB outputs. Although raw data is more faithful for low-levelvision tasks, collecting large-scale raw datasets remains a major bottle-neck, as existing datasets are limited and tied to specific camera hard-ware. Generative models offer a promising way to address this scarcity;however, existing diffusion frameworks are designed to synthesize photo-finished sRGB images rather than physically consistent linear representa-tions. This paper presents RawGen, to our knowledge the first diffusion-based framework enabling text-to-raw generation for arbitrary targetcameras, alongside sRGB-to-raw inversion. RawGen leverages the gener-ative priors of large-scale sRGB diffusion models to synthesize physicallymeaningful linear outputs, such as CIE XYZ or camera-specific raw rep-resentations, via specialized processing in latent and pixel spaces. Tohandle unknown and diverse ISP pipelines and photo-finishing effectsin diffusion-model training data, we build a many-to-one inverse-ISPdataset where multiple sRGB renditions of the same scene generatedusing diverse ISP parameters are anchored to a common scene-referredtarget. Fine-tuning a conditional denoiser and specialized decoder on thisdataset allows RawGen to obtain camera-centric linear reconstructionsthat effectively invert the rendering pipeline. We demonstrate RawGen’ssuperior performance over traditional inverse-ISP methods that assume afixed ISP. Furthermore, we show that augmenting training pipelines withRawGen’s scalable, text-driven synthetic data can benefit downstreamlow-level vision tasks.