ECCV 2026 Papers

Knowledge Distillation (KD) is widely used to obtain com-pact models for efficient inference in resource-constrained environments.Yet the computational overhead of the distillation process itself is oftenoverlooked, raising the question of whether a better student model canbe obtained with less data and less compute via data pruning. However,existing data pruning methods are not designed for KD: some introducesubstantial overhead (e.g., obtaining training dynamics through retrain-ing), while others rely on heuristic selection rules that fail to capture whatKD actually requires, often resulting in suboptimal subsets. To addressthese issues, we propose IF-Beta, an efficient data pruning frameworkthat combines influence function and a learnable sampling policy. Em-pirically, we first demonstrate that influence functions can serve as aneffective and efficient estimator of sample impact in KD settings, whereonly a pretrained teacher is available. Building on this, our samplingpolicy is specifically parameterized by a Beta distribution, whose highlyflexible two-parameter family allows the policy to adapt to diverse prun-ing regimes rather than being tied to fixed heuristic forms. Next, weformulate KD pruning as optimizing this policy through a bilevel objec-tive, where the inner loop operates in the teacher’s feature space with aKD-aligned objective, enabling fast proxy training, while the outer loopupdates the policy parameters to maximize the distillation performance.This design ensures IF-Beta is both computationally efficient and inher-ently aligned with the goals of KD. Extensive experiments on CIFAR-10/100 and ImageNet show that IF-Beta consistently outperforms otherbaselines across a wide range of pruning ratios. Remarkably, IF-Betadoes enable students trained on less data and less compute to surpassthe performance of students distilled on the full dataset.

Precise human mesh recovery (HMR) from multi-view im-ages remains challenging: end-to-end methods produce entangled errorshard to localize, while _x001C_tting-based methods rely on sparse keypointsthat provide limited surface constraints. We observe that the true bot-tleneck lies in the quality of intermediate representations, and that densepixel-to-surface correspondences can be e_x001B_ectively generated by repur-posing pre-trained di_x001B_usion models with rich visual priors. We proposeDi_x001B_Proxy, a Stable-Di_x001B_usion-based framework trained on large-scalesynthetic data with pixel-perfect annotations. A multi-conditional proxygenerator predicts dense correspondences from multi-view images, pro-viding uniform surface constraints that enable precise _x001C_tting. Hand re-_x001C_nement feeds enlarged hand crops alongside full-body images for _x001C_ne-grained detail, while test-time scaling exploits di_x001B_usion stochasticity toestimate per-pixel uncertainty. Trained only on synthetic data, Di_x001B_Proxyachieves state-of-the-art results on _x001C_ve diverse real-world benchmarks.Project page: https://wrk226.github.io/DiffProxy.html

Recent advances in video generation have revealed an un-expected capability: diffusion-based video models can perform reason-ing through Chain-of-Frames (CoF), suggesting that reasoning unfoldssequentially across video frames. In this work, we revisit this assump-tion and uncover a different mechanism. Through systematic analysis,we show that video reasoning primarily develops along the diffusion de-noising steps instead, where early steps exhibit multiple hypotheses thatgradually converge. We term this mechanism Chain-of-Steps (CoS),which is validated through qualitative analysis and probing tests. Ourinvestigation reveals several intriguing emergent behaviors: 1) Modelsdemonstrate a form of persistent working memory that supports tasksrequiring consistent reference, such as object permanence. 2) They canself-correct intermediate mistakes during generation instead of com-mitting to incorrect trajectories. 3) Early steps function differently fromlater steps, exemplified by a perception before action phenomenon.Moreover, analysis of Diffusion Transformer layers shows that middlelayers conduct key reasoning procedures. Motivated by these insights,we propose Training-Free Ensemble (TFE), a simple strategy thatintegrates reasoning paths by merging latents from identical models withdifferent random seeds at inference time. This approach encourages theexploration of diverse reasoning trajectories and improves reasoning per-formance. Together, our findings provide the first systematic dissectionof the mechanisms underlying video reasoning and offer practical insightsfor developing more capable video reasoning models.

Infrared–visible video fusion aims to integrate complemen-tary thermal and visual information to produce stable and informativefused videos for real-world applications. While recent advances haveachieved remarkable progress in image-level fusion, extending these meth-ods to dynamic video scenarios remains challenging. Existing video fu-sion approaches typically adopt a ‘temporal-first’ processing strategy,where temporal modeling is performed within each modality before cross-modal fusion. However, such designs may become vulnerable to modality-specific degradations, including low illumination in visible videos andnoise or flicker in infrared sequences, which often lead to unreliablemotion estimation and temporally inconsistent fusion results. To ad-dress this limitation, we advocate a ‘fusion-first’ strategy and proposeDRT-VF, a unified degradation-robust and temporally consistent in-frared–visible video fusion method built upon a one-step diffusion ar-chitecture with a two-stage progressive training strategy. In Stage I, aModality-Collaborative Attention module integrates complementary in-frared and visible cues to establish robust intra-frame fusion representa-tions. In Stage II, temporal consistency is modeled through a hierarchicaldesign consisting of a Global Temporal Consistency module and a LocalMotion Compensation module, which jointly enhance global temporalconsistency and refine motion-sensitive regions. Extensive experimentsdemonstrate that DRT-VF improves temporal stability while preservingfine structural details.

Robust out-of-distribution (OOD) detection is crucial fordeploying deep learning systems in high-stakes settings such as medi-cal imaging and autonomous driving. Post-hoc feature-based detectorsare attractive because they operate on pre-trained models, yet they of-ten treat all feature dimensions as equally informative. We show thatOOD-discriminative information in deep feature representations is of-ten concentrated in subsets of features and is frequently axis-aligned. Toexploit this structure, we introduce a distribution-aware feature selec-tion strategy that ranks feature dimensions according to the discrepancybetween in-distribution (ID) and OOD feature distributions, using theWasserstein-1 distance as a principled metric.To avoid the need for curated OOD validation data, we construct proxy-OOD data based on cross-domain mixup and evaluate adversarial per-turbations as an alternative. Our method is a lightweight, broadly ap-plicable add-on to existing feature-based OOD detectors that requiresno retraining or changes to scoring functions. Evaluations across pub-lic benchmarks spanning medical and natural image domains show con-sistent performance improvements, while the reduced feature set lowerscomputational complexity and enables substantially faster inference. Ourcode is available at https://github.com/remic-othr/mfs-ood.

Recently, 3D Gaussian Splatting (3DGS) greatly acceleratedmesh extraction from posed images due to its explicit representation andfast software rasterization. While the addition of geometric losses andother priors has improved the accuracy of extracted surfaces, mesh ex-traction remains difficult in scenes with abundant view-dependent effects.To resolve the resulting ambiguities, prior works rely on multi-view tech-niques, iterative mesh extraction, or large pre-trained models, sacrificingthe inherent efficiency of 3DGS. In this work, we present a simple and ef-ficient alternative by introducing a self-supervised confidence frameworkto 3DGS: within this framework, learnable confidence values dynam-ically balance photometric and geometric supervision. Extending ourconfidence-driven formulation, we introduce losses which penalize per-primitive color and normal variance and demonstrate their benefits tosurface extraction. Finally, we complement the above with an improvedappearance model, by decoupling the individual terms of the D-SSIMloss. Our final approach delivers state-of-the-art results for unboundedmeshes while remaining highly efficient.

Color transfer aims to align the color distribution of a sourceimage with that of a reference image while preserving structural andsemantic consistency. However, existing methods often suffer from inac-curate global mapping, semantic misalignment, and visual artifacts. Toaddress these issues, we propose ColorFM, an optimization-to-learningframework. ColorFM connects online optimization to offline inference byreformulating color transfer as the transport of pixel distributions alongvelocity fields via Flow Matching. Specifically, we introduce ColorFM-O, an instance-specific optimization scheme that fits the velocity fieldthrough hierarchical color coupling guided by semantic priors. By nu-merically integrating the induced flow trajectories, ColorFM-O producesprecise and semantically consistent color transfer results, while generat-ing high-quality paired data as pseudo-supervision. Building upon this,we design ColorFM-L, an efficient feed-forward model trained on thegenerated pairs. Through implicit state modeling, ColorFM-L extractsdeep semantic features to predict flow parameters for bidirectional lin-earized transport, ensuring accurate color transfer. Extensive experi-ments demonstrate that ColorFM-L outperforms state-of-the-art meth-ods in visual quality, structural fidelity, and semantic consistency, suc-cessfully combining the accuracy of optimization with the speed of feed-forward inference.

Spatial reasoning is a critical capability for intelligent robots,yet current vision-language models (VLMs) still fall short of human-levelperformance in video-based spatial reasoning. This gap mainly stemsfrom two challenges: a semantic-geometric misalignment that preventsconsistent 3D understanding, and the absence of persistent memory toretain 3D representation and understanding across frames. To addressthese limitations, we present VLM2 , a Vision-Language Model with per-sistent Memory for spatial reasoning with a view-consistent, 3D-awarerepresentation purely from 2D videos. Specifically, we incorporate a dual-memory module consisting of a working memory that operates as a slid-ing window to focus on immediate context, and an episodic memory thatconsolidates and stores critical information across frames. This designenables bounded and efficient spatial reasoning under a fixed computa-tional cost. Extensive experiments on multiple benchmarks show thatVLM2 achieves state-of-the-art performance among video-based models,significantly advancing the frontier of visual-spatial intelligence.

Retrieval-Augmented Text-to-Motion (RAG-T2M) modelshave demonstrated superior performance over conventional T2M ap-proaches, particularly in handling uncommon and complex textual de-scriptions by leveraging external motion knowledge. Despite these gains,existing RAG-T2M models remain limited by two closely related factors:coarse-grained text-motion retrieval that overlooks the hierarchical struc-ture of human motion, and underexplored mechanisms for effectivelyfusing retrieved information into the generative process. In this work,we present ReMoMask, a structure-aware RAG framework for text-to-motion generation that addresses these limitations. To improve retrieval,we propose Hierarchical Bidirectional Momentum (HBM) contrastivelearning, which employs dual objectives to jointly align global motionsemantics and fine-grained part-level features with text. To bridge thegap between structured retrieval and generation, we introduce TopologyStructured Masking (TSM), a training strategy that adaptively masksmotion tokens based on semantic relevance, forcing the model to learnrobust part-level grounding. Furthermore, we design Semantic Spatial-Temporal Attention (SSTA), a topology-aware fusion module that inte-grates retrieved knowledge via an asymmetric attention mechanism. Ex-tensive experiments on HumanML3D, KIT-ML, and SnapMoGen demon-strate that ReMoMask consistently outperforms prior methods on bothtext-motion retrieval and text-to-motion generation benchmarks.

Open World Classification (OWC) with Large MultimodalModels (LMMs) is an emerging and promising task that moves beyondthe closed-world setting by enabling direct answer generation in responseto open-ended queries. However, we observe that the OWC performanceof LMMs significantly degrades on fine-grained tasks. Empirically, wefind that their performance is sensitive to contextual information in thequery. To address this limitation, we propose Adaptive Memory ContextInjection (AMCI), a training-free and model-agnostic framework that dy-namically restores contextual information during OWC inference. AMCImaintains a non-parametric memory that aligns visual indices with theircorresponding attribute-augmented descriptions. By employing an adap-tive similarity threshold to preserve memory diversity and utilizing arefinement LLM to transform accumulated context into a structuredprompt, AMCI transforms the initial zero-shot description into a cali-brated fine-grained prediction without parameter updates. Furthermore,we construct a specialized low-resource fine-grained benchmark to sim-ulate extreme data scarcity and introduce a new metric that penalizesoverly generic predictions, tailored for fine-grained OWC evaluation. Ex-tensive experiments demonstrate that AMCI consistently improves per-formance by a substantial margin while remaining computationally effi-cient and compatible with various LMMs.

Multimodal Large Language Models (MLLMs) have shownimpressive abilities in understanding and reasoning over conventionalimages. However, their perception of 360° images remains largely un-derexplored. Unlike conventional images, 360° images capture the en-tire surrounding environment, enabling holistic spatial reasoning butintroducing challenges such as geometric distortion and complex spa-tial relations. To comprehensively assess MLLMs’ capabilities to per-ceive 360° images, we introduce 360Bench, a Visual Question Answering(VQA) benchmark featuring 7K-resolution 360° images, seven represen-tative (sub)tasks with annotations carefully curated by human annota-tors. Using 360Bench, we systematically evaluate seven MLLMs and sixenhancement methods, revealing their shortcomings in 360° image per-ception. To address these challenges, we propose Free360, a training-free scene-graph-based framework for high-resolution 360◦ VQA. Free360decomposes the reasoning process into modular steps, applies adaptivespherical image transformations to 360° images tailored to each step,and seamlessly integrates the resulting information into a unified graphrepresentation for answer generation. Experiments show that Free360consistently improves its base MLLM and provides a strong training-free solution for 360◦ VQA tasks. The source code and dataset will beavailable at https://tranhuyen1191.github.io/360Bench-Free360/.

Although recent multimodal large language models (MLLMs)have advanced rapidly in vision–language reasoning, their capability bound-aries, particularly in cross-modal integration and reasoning, remain un-derexplored. Existing benchmarks primarily focus on evaluating uni-modal or loosely coupled multimodal abilities, leaving a gap in assess-ing complex cross-modal reasoning. To address this gap, we introduceVisWordBench, a benchmark comprising 2,625 English and 2,000 Chi-nese visual word puzzles with detailed human annotations. The bench-mark is designed to evaluate not only fundamental perceptual under-standing and world knowledge but also deep cross-modal reasoning skillsthat require consistently integrating visual and linguistic information.Through evaluations on VisWordBench, we find that current MLLMs ex-hibit under-diversified hypothesis search during reasoning. We thereforepropose a data construction pipeline and a training-free inference-timesteering strategy that promotes more diverse hypothesis exploration dur-ing CoT reasoning. Experimental results show consistent improvementsin reasoning quality and hypothesis diversity, supporting the utility ofVisWordBench for studying multimodal reasoning. The benchmark isavailable at: https://zenodo.org/records/20957955.

Generating high-fidelity remote sensing images is severelychallenged by their specific overhead perspectives, complex structures,and rigorous physical attributes. Although recent controllable RS gen-eration methods have made progress, they are still limited by two keybottlenecks. The first is semantic conflict, where dominant global conceptsoften overshadow fine-grained details. The second is physical inconsis-tency, which easily leads to structural collapse and radiometric distortionduring the generation process. To address these issues, we propose SGP2(Synergizing Geometric and Physical Priors), a novel coarse-to-fine frame-work for controllable multimodal RS image generation. Specifically, toresolve semantic conflicts, we introduce the Grassmann Miner, whichconstructs dynamic geodesic trajectories on the Grassmann Manifold,enabling the model to gradually shift its focus from global backgrounds tofine-grained local details during the denoising process. To ensure physicalconsistency, we propose Physical Prior Expert, a module that dynamicallyaligns multi-level features from Visual Foundation Models and employsa frequency-aware Spectral Loss to enforce both amplitude and phaseconsistency. Furthermore, to bridge RS data gaps, we present MMEarth-1.5M, a 1.5 million text-image dataset featuring strictly co-registeredoptical and SAR pairs for robust cross-modal simulation. Our approach,without bells and whistles, achieves favorable performance in controllablemulti-modal remote sensing image generation task. The dataset will beavailable in https://github.com/cpy0029/MMEarth-1.5M.

Current Large Vision Language Models (LVLMs) excel atmany zero-shot tasks like image captioning, visual question answeringand OCR. However, these same models suffer from poor performance atimage classification tasks, underperforming against CLIP-based meth-ods. Notably, this gap is surprising because many LVLMs use CLIP-pretrained vision encoders. Yet LVLMs are not inherently limited byCLIP’s architecture with independent vision and text encoders. In CLIP,this separation biases classification toward class-name matching ratherthan joint visual–text reasoning. In this paper we show that, despite theirpoor raw performance, LVLMs can improve visual feature class separa-bility at inference using prompt conditioning, and LVLMs’ internal rep-resentations, especially attention heads, can outperform the model itselfat zero-shot and few-shot classification. We introduce Head EnsembleClassifiers (HEC) to bridge the performance gap between CLIP-basedand LVLM-based classification methods. Inspired by Gaussian Discrimi-nant Analysis, HEC ranks the most discriminative vision and text headsand combines them into a training-free classifier. We show that HECachieves state-of-the-art performance in few-shot and zero-shot classifi-cation across 12 datasets. Code: github.com/AdhemarDeSenneville/HEC

Typical video object-centric learning (VOCL) approachesemploy slot-based frameworks that rely on reconstruction-driven en-coder–decoder architectures, where learning is mediated by two spatialmaps: attention maps from the encoder and object maps from the decoder.As these two distinct maps exhibit different properties, a recent densealignment strategy attempted to reconcile this discrepancy by enforcingagreement across all spatio-temporal patches via contrastive learning.However, this indiscriminate alignment inadvertently propagates the in-herent weaknesses of each module, such as noisy encoder predictionsand blurred decoder boundaries. Moreover, computing dense similaritiesacross all pairs incurs a computational cost quadratic in the total numberof spatio-temporal patches, severely limiting scalability. Motivated bythis, we propose Selective Synergistic Learning (SSync). Instead of ex-haustive patch-to-patch alignment, SSync prevents error propagation byselectively distilling only the most reliable cues: leveraging the encoderstrictly for boundary refinement and the decoder for interior denoising.This is realized via a pseudo-labeling with linear complexity, eliminatingthe need for quadratic spatial comparisons. Also, to prevent the rein-forcement of architectural biases like slot redundancy, we introduce atransitive pseudo-label merging that consolidates overlapping slots basedon spatio-temporal activation consistency. Extensive studies demonstratethat SSync improves decomposition quality and serves as a versatile,plug-and-play module while also exhibiting exceptional robustness to slotconfigurations. Code is available at github.com/wjun0830/SSync.

Recent advances in multimodal generative models have drivensubstantial improvements in image editing. However, current generativemodels still struggle with handling diverse and complex image editingtasks that require implicit reasoning, underscoring the need for a compre-hensive benchmark to systematically assess their performance across var-ious reasoning scenarios. Existing benchmarks primarily focus on single-object attribute transformation in realistic scenarios, which, while effec-tive, encounter two key challenges: (1) they largely overlook multi-objectinteractions as well as game-world scenarios that involve human-definedrules, which are common in real-life applications; (2) they only rely ontextual references to evaluate the generated images, potentially leadingto systematic misjudgments, especially in complex reasoning scenarios.To this end, this work proposes UniREditBench, a unified benchmarkfor reasoning-based image editing evaluation. It comprises 2,700 metic-ulously curated samples, covering both real- and game-world scenariosacross 8 primary dimensions and 18 sub-dimensions. To improve evalua-tion reliability, we introduce multimodal dual-reference evaluation, pro-viding both textual and ground-truth image references for each sampleassessment. Furthermore, we design an automated multi-scenario datasynthesis pipeline and construct UniREdit-Data-100K, a large-scalesynthetic dataset with high-quality chain-of-thought (CoT) reasoning an-notations. We fine-tune Bagel on this dataset and develop UniREdit-Bagel, demonstrating substantial improvements in both in-domain andout-of-distribution settings. Through thorough benchmarking of bothopen-source and closed-source image editing models, we reveal theirstrengths and weaknesses across various aspects.

Acquiring a complete set of magnetic resonance imaging (MRI)contrasts is time-intensive and uncomfortable for patients, despite thediagnostic value of multi-contrast imaging. This motivates synthesizingmissing contrasts from those already acquired, which is an inherently3D problem requiring anatomical coherence across axial, sagittal, andcoronal planes. However, fully 3D generative models are often impracti-cal under computational resources that scale cubically with volume size.We propose a unified Multi-Plane Autoregressive Diffusion (MPAD), alatent diffusion framework that achieves full-volume 3D synthesis usingefficient plane-wise 2D operations while preserving volumetric coherence.A 3D autoencoder first compresses MRI scans into an isotropic 3D la-tent representation. A 2D diffusion model is then trained to reconstructmasked latent slices of the target contrast, conditioned on both source-contrast slices and unmasked target-contrast slices. During inference,we introduce plane-wise autoregressive synthesis with inter-plane priors.Slices are generated autoregressively in random order within one planeorientation to maintain intra-plane continuity, then propagated as con-ditioning priors to orthogonal plane orientations to enforce inter-planeconsistency. Compared to 3D latent diffusion baselines, MPAD reducestraining and inference FLOPs by 7× and 3×, respectively, while alsolowering inference time and peak memory consumption. Experimentson multiple datasets demonstrate that MPAD achieves superior perfor-mance, generating high-fidelity 3D volumes and supporting one-to-manytranslation within a single unified model.

With the advancement of imaging technology, ultra-highdefinition images have become increasingly essential in modern visual applications. However, existing multi-focus image fusion remains largely confined to low-resolution images and faces three major barriers in UHD scenarios, namely data availability, model adaptability, and deployment feasibility, which severely hinder its practical application. To shatter these barriers, first, we propose the UHD-MFF dataset, the first largescale ultra-high-resolution multi-focus fusion dataset. Second, we propose a scale-specialized lookup-table framework tailored for ultra-highresolution images, termed as UMF-LUT. It consists of Coarse-Region Lookup Table (C-LUT) and Detail-Edge Lookup Table (D-LUT). Specifically, C-LUT performs joint queries of multiple gradient cues and semantic cues at low-resolution scales to enable region-level decision-making. Also, D-LUT operates at high-resolution scales, leveraging efficient Laplacian cues to provide complementary edge-level decision information. Such a design makes the model particularly well-suited for ultra-high-resolution multi-focus image fusion. Finally, it offers strong deployability with minimal computational overhead, enabling real-time 4K multi-focus fusion and showing promising potential for smartphone. Extensive experiments demonstrate that it outperforms SOTA methods in both visual fidelity and quantitative metrics. It effectively advances the development of multifocus image fusion toward ultra-high-resolution imaging scenarios. The code is available at https://github.com/zyb5/UHD-MFF.

Enabling embodied agents to imagine future states is essen-tial for robust and generalizable visual navigation. Yet, state-of-the-artsystems typically rely on modular designs that decouple navigation plan-ning from visual world modeling, which often induces state–action mis-alignment and weak adaptability in novel or dynamic scenarios. We pro-pose UniWM, a unified, memory-augmented world model that integratesegocentric visual foresight and planning within a single multimodal au-toregressive backbone. UniWM explicitly grounds action selection in vi-sually imagined outcomes, tightly aligning prediction with control. Mean-while, a hierarchical memory mechanism fuses short-term perceptual cueswith longer-term trajectory context, supporting stable and coherent rea-soning over extended horizons. Extensive experiments on four challeng-ing benchmarks (Go Stanford, ReCon, SCAND, HuRoN) and the 1XHumanoid Dataset show that UniWM improves navigation success ratesby up to 30%, substantially reduces trajectory errors against strong base-lines, generalizes zero-shot to the unseen TartanDrive dataset, and scalesnaturally to high-dimensional humanoid navigation. These results po-sition UniWM as a principled step toward unified, imagination-drivenembodied navigation. All materials are committed to be open-sourced.