ECCV 2026 论文列表

Magnetic resonance imaging comes in various modality con-trasts that provide complementary anatomical and pathological informa-tion. Complete multimodal acquisitions are often unavailable due to timeand protocol constraints. This leads to real-world datasets with missingmodalities, where conventional medical image translation methods aretypically limited to fixed source-target settings or require retraining foreach observed source-target pair. We propose a unified framework thatformulates missing-modality generation as a linear inverse problem un-der a joint distribution and solves it via posterior sampling with a flowmatching model. By learning a joint prior over the complete modalityset, our method can reconstruct arbitrary missing modalities at inferencetime by guiding the sampling trajectory to enforce measurement consis-tency with observed modalities. We further mitigate inter-modality er-ror propagation in multi-target generation by adopting a many-to-onesampling strategy. Experiments on BraTS and IXI datasets show thatour method achieves the best performance over baselines across mostmissing-modality scenarios. In downstream tumor segmentation, synthe-sized images from our method result in higher segmentation performance,indicating better preservation of clinically relevant structures. Our codeis available at github.com/jongdory/PS-MIT.

Flow matching (FM) trains a time-dependent vector field that transports samples from a simple prior to a complex data distribution. However, for high-dimensional images, each training sample supervises only a single trajectory and intermediate point, yielding an extremely sparse and high-variance training signal. This under-constrained supervision can cause flow collapse, where the learned dynamics memorize specific source–target pairings, mapping diverse inputs to overly similar outputs, failing to generalize. We introduce Posterior Augmented Flow Matching (PAFM), a theoretically grounded generalization of FM that replaces single-target supervision with an expectation over an approximate posterior of valid target completions for a given intermediate state and condition. PAFM factorizes this intractable posterior into (i) the likelihood of the intermediate under a hypothesized endpoint and (ii) the prior probability of that endpoint under the condition, and uses an importance sampling scheme to construct a mixture over multiple candidate targets. We prove that PAFM yields an unbiased estimator of the original FM objective while substantially reducing gradient variance during training by aggregating information from many plausible continuation trajectories per intermediate. Finally, we show that PAFM improves over FM by up to 3.4 FID50K across different model scales (SiT-B/2 and SiT-XL/2), different architectures (SiT and MMDiT), and in both class and text conditioned benchmarks (ImageNet and CC12M), with a negligible increase in the compute overhead. Code: https://github.com/gstoica27/PAFM.git.

When should an intelligent assistant speak up without beingasked? Continuous egocentric video offers rich, evolving context that en-ables a new form of assistance: one that is proactive rather than merelyreactive. Yet existing approaches either wait passively for user queries ortreat every detected event as requiring a response, without consideringthe user’s history, current activity, or whether assistance would actuallybe welcome. We reframe proactive assistance as a context-dependent de-cision problem: the agent must not only perceive what is happening,but reason over accumulated temporal context to determine when andwhether to intervene. To this end, we present Vinci2, a proactive ego-centric assistance system that advances the on-device assistant Vincifrom reactive response toward proactivity. On the evaluation side, wepresent EgoServe, the first large-scale benchmark for proactive assistancein continuous egocentric video. EgoServe comprises over 3,000 serviceinstances organized along 4 temporal memory horizons, ranging fromimmediate safety alerts to long-term habit coaching, across 10 servicecategories. On the modeling side, we propose EgoMemo, a training-free,memory-augmented agent that maintains three complementary mem-ory representations: multi-scale temporal summaries, a semantic knowl-edge graph, and visual embedding archives. At each timestep, EgoMemoperforms retrieval-augmented reasoning to determine whether assistanceis warranted and, if so, produces contextually grounded responses. Ex-periments demonstrate that EgoMemo establishes strong baselines onEgoServe while remaining competitive on existing egocentric benchmarks.Our benchmark and code are publicly available at Vinci2.

Realistic and diverse multi-agent driving scenes are crucial for evalu-ating autonomous vehicles, but safety-critical events which are essential for thistask are rare and underrepresented in driving datasets. Data-driven scene genera-tion offers a low-cost alternative by synthesizing complex traffic behaviors fromexisting driving logs. However, existing models often lack controllability or yieldsamples that violate physical or social constraints, limiting their usability. Wepresent OMEGA, an optimization-guided, training-free framework that enforcesstructural consistency and interaction awareness during diffusion-based samplingfrom a scene generation model. OMEGA re-anchors each reverse diffusion stepvia constrained optimization, steering the generation towards physically plausibleand behaviorally coherent trajectories. Building on this framework, we formu-late ego–attacker interactions as a game-theoretic optimization in the distributionspace, approximating Nash equilibria to generate realistic, safety-critical adver-sarial scenarios. Experiments on nuPlan and Waymo show that OMEGA improvesgeneration realism, consistency, and controllability, increasing the ratio of physi-cally and behaviorally valid scenes from 32.35% to 72.27% for free explorationcapabilities, and from 11% to 80% for controllability-focused generation. Our ap-proach can also generate 5× more near-collision frames with a time-to-collisionunder three seconds while maintaining the overall scene realism.

While modern 3D reconstruction excels at modeling objectgeometry and appearance, it largely ignores the rich acoustic cues re-vealed through physical interaction. Object impact sounds convey mate-rial, stiffness, and structural properties that complement vision, yet exist-ing impact sound modeling approaches either rely on expensive physics-based simulation or require large datasets to generalize in a purely data-driven manner. We introduce Audio-Visual Modal Sound Field (AV-MSF), a novel object-level acoustic representation reconstructed frommulti-view images and only a few impact sound recordings. AV-MSFbuilds on 3D Gaussian Splatting integrated with dense 3D visual featureto provide a strong geometry-aware prior, and represents the impactsound field using compact, physically meaningful modal parameters, en-abling robust few-shot reconstruction. Experiments on two real-worlddatasets show that AV-MSF achieves state-of-the-art impact sound ren-dering, outperforming both physics-based and data-driven baselines. Fur-thermore, we demonstrate downstream applications enabled by our rep-resentation, including contact localization and object sound editing.

Material transfer seeks to re-render an object’s surface usinga reference exemplar while preserving its original geometry and identity.Current training-free methods often rely on attention-based injection,which entangles attributes like color, texture, and structural patternsinto a single representation. This entanglement leads to structural in-stability and imprecise results. Additionally, inversion-based editing inflow models is prone to reconstruction-induced trajectory drift, whereaccumulated errors degrade object details. We propose MaterialFlow, atraining-free framework for precise material transfer using pre-trainedflow models. Our approach introduces a trajectory-aware velocity modu-lation mechanism that rebalances the generative flow in an inversion-free manner. This ensures stable and semantically consistent editingdynamics without the need for explicit latent reconstruction. We fur-ther introduce an attribute-aware disentanglement paradigm that sepa-rates reference materials into color, texture, and meso-scale patterns forfine-grained control. Evaluations on the Material Transfer Benchmark(MTB) show that MaterialFlow surpasses state-of-the-art methods intransfer quality, inference efficiency, and controllability. Project page:https://github.com/Sung-Lin/MaterialFlow.

High-resolution multi-contrast MRI is essential for capturingrich anatomical structures and supports more accurate disease diagnosisthrough the reference images from another contrast. It often suffers fromlimited spatial resolution due to acquisition constraints, and requestsexploring arbitrary-scale reconstruction beyond fixed scale factors. How-ever, related research exists two following issues: 1) Implicit neural rep-resentations (INR) as mainstream methods are prone to spectral bias,which limits their ability to recover high-frequency details; 2) Multi-contrast MRI is often used as the effective prior, but lacks the targetednetwork design to further merge INR positional information. To solvethese problems, we propose a Fourier-enhanced implicit framework forarbitrary-scale multi-contrast MRI super-resolution (MArFE). First, theFourier reparameterization module (FRM) is introduced to character-ize frequency features through fixed Fourier basis decomposition. Then,Fourier neural operators can alleviate frequency degradation by the non-local Galerkin-type linear attention (NLGLA) based on kernel integralmechanisms. Additionally, we design a hybrid loss that jointly supervisesboth spatial and frequency domains to improve high-frequency texturereconstruction. Extensive experiments on two public multi-contrast MRIdatasets demonstrate that MArFE outperforms existing methods undervarious scale factors with superior visual performances and quantitativemetrics. These results highlight the clinical potential of MArFE in en-hancing MRI quality across multi-contrast sequences. The project codecan be found at https://github.com/zhiwen-shi/MArFE.

The performance of vision-language models (VLMs) in videounderstanding declines with increasing video duration, as video momentsunrelated to the query confuse their language components. Multimodalretrieval has emerged as a critical component of video understanding,addressing this challenge by localizing key visual evidence. However, ex-isting multimodal retrieval methods suffer from biased relevance estima-tion, limited diversity, and temporal collapse. In this paper, we proposeQSVideo, a unified framework that systematically addresses relevance,diversity, and temporal modeling in video retrieval. We first introducea query-conditioned semantic ranker, QSRanker, which reformulates ar-bitrary questions into retrieval-friendly queries and estimates structuredrelevance along object, action, and location dimensions. Building uponthis, we design QSRetrieval to jointly optimize relevance and diversityfor more informative frame selection. Moreover, we propose temporalalignment strategies tailored for both long and streaming videos to im-prove evidence recall. Extensive experiments on long and streaming videobenchmarks demonstrate that QSVideo greatly enhances video VLM per-formance under strict frame limit constraints. The code is available athttps://github.com/human-analysis/QSVideo.

While Image-to-Video models unlock new creative possibil-ities, they also raise risks of unauthorized adaptation, deepfakes, andscenario manipulation, making robust authentication and traceability so-lutions urgent for image owners. While image watermarking offers a solu-tion, existing methods expose a critical vulnerability: due to the inherentsemantic drift of generated videos, images watermarks survive only in ini-tial frames. This allows attackers to easily evade detection via temporalcropping—discarding early frames to obtain watermark-free derivativevideos. Consequently, in an era of pervasive I2V models, long-term ro-bustness is imperative for image watermarking. Since simply increasingforward embedding strength fails against severe generative divergence,we introduce LoT-Pass, a watermarking framework built on a new in-sight: only one-directional robustness enhancement is hard to achievelong-term robustness. We propose a two-way strategy: (1) expand therobustness distance by training under simulated temporal evolution inI2V generation, and (2) recover unextractable frames by reversing themthrough a temporal inversion module. This shift, from merely resistingdistortions to actively rewinding the video to a watermark-friendly state,enables LoT-Pass to maintain extractability even under temporal drift.Experiments on mainstream open-source and commercial I2V modelsdemonstrate that LoT-Pass achieves stronger long-term robustness whilepreserving imperceptibility, offering a new paradigm for image copyrightprotection in the era of widespread I2V adoption. Code

Recent advancements in open-vocabulary 3D scene under-standing heavily rely on 3D Gaussian Splatting (3DGS) to register vision-language features into 3D space. However, we identify two critical limi-tations in these approaches: the spatial ambiguity arising from unstruc-tured, overlapping Gaussians which necessitates probabilistic feature reg-istration, and the multi-level semantic ambiguity caused by pooling fea-tures over object-level masks, which dilutes fine-grained details. To ad-dress these challenges, we present a novel framework that leverages SparseVoxel Rasterization (SVRaster) as a structured, disjoint geometry repre-sentation. By regularizing SVRaster with monocular depth and normalpriors, we establish a stable geometric foundation. This enables a de-terministic, confidence-aware feature registration process and suppressesthe semantic bleeding artifact common in 3DGS. Furthermore, we resolvemulti-level ambiguity by exploiting the emerging dense alignment proper-ties of the AM-RADIO foundation model, avoiding the computationaloverhead of hierarchical training methods. Our approach achieves state-of-the-art performance on Open Vocabulary Point Cloud Understanding,and highly competitive results on 3D Object Retrieval benchmarks.

Recent video generation models have achieved remarkableprogress and are now deployed in film, social media production, and ad-vertising. Beyond their creative potential, such models also hold promiseas world simulators for robotics and embodied decision making. Despitestrong advances, current approaches still struggle to generate physicallyplausible object interactions and lack object-level control mechanisms. Toaddress these limitations, we introduce KineMask, an approach for videogeneration that enables realistic rigid body control, interactions, and ef-fects. Given a single image and a specified object velocity, our methodgenerates videos with inferred motions and future object interactions.We propose a two-stage training strategy that gradually removes futuremotion supervision via object masks. Using this strategy we train videodi!usion models (VDMs) on synthetic scenes of simple interactions anddemonstrate significant improvements and generalization to rigid bodyand hand-object interactions in real scenes. Furthermore, KineMask in-tegrates low-level motion control with high-level textual conditioning viapredicted scene descriptions, leading to support for synthesis of complexdynamical phenomena. Our experiments show that KineMask general-izes to di!erent VDMs and achieves strong improvements over recentmodels of comparable size. Ablation studies further highlight the com-plementary roles of low- and high-level conditioning in VDMs.Project Page: https://daromog.github.io/KineMask/

We introduce LaMP, a dual-expert Vision-Language-Actionframework that embeds dense 3D scene flow as a latent motion prior forrobotic manipulation. Existing VLA models regress actions directly from2D semantic visual features, forcing them to learn complex 3D physicalinteractions implicitly. This implicit learning strategy degrades underunfamiliar spatial dynamics. LaMP addresses this limitation by aligninga flow-matching Motion Expert with a policy-predicting Action Expertthrough gated cross-attention. Specifically, the Motion Expert generatesa one-step partially denoised 3D scene flow, and its hidden states condi-tion the Action Expert without full multi-step reconstruction. We eval-uate LaMP on the LIBERO, LIBERO-Plus, and SimplerEnv-WidowXsimulation benchmarks as well as real-world experiments. LaMP consis-tently outperforms evaluated VLA baselines across LIBERO, LIBERO-Plus, and SimplerEnv-WidowX benchmarks, achieving the highest re-ported average success rates under the same training budgets. On LIBERO-Plus OOD perturbations, LaMP shows improved robustness with an av-erage 9.7% gain over the strongest prior baseline. Our project page isavailable at https://summerwxk.github.io/lamp-project-page/.

Masked generative models have made significant advancesin text-to-motion synthesis. Previous approaches adopt a non-factorized,whole-body tokenization, limiting compositional and fine-grained con-trol. We present K-Mask, a kinematic-aware generative framework thatfactors motion into anatomically grounded groups and learns temporaland cross-group interactions through masked modeling. In the proposedapproach, a kinematic-group residual VQ-VAE (KG-RVQ) encodes mo-tion into disentangled, group-aligned latents. In particular, a latent-awarekinematic dropout (LAKD) loss is proposed to promote within-groupreconstruction and suppress inter-group leakage. Then, we introduce twobidirectional spatiotemporal transformers: (i) a base model that generatescoarse tokens over a time-group lattice with axis-factorized attention, and(ii) a residual model that predicts deeper quantizer layers for refinement.Finally, We introduce kinematic group masking, which randomly occludesentire group-specific subtrees, to enhance inter-group reasoning. On theHumanML3D and KIT-ML text-to-motion benchmarks, K-Mask obtainscompetitive FID scores of 0.041 and 0.154, respectively, while providingan anatomically factorized token interface for localized motion control.

Modern video generation models, despite high-quality frame synthesis, still struggle with motion fidelity and temporal consistency, causing artifacts and semantic drift; current solutions typically require extensive backbone retraining. We introduce a training-free framework for inference-time motion calibration that intervenes in the sampling trajectory of pretrained models to correct nascent inconsistencies efficiently. Our framework has two complementary components. First, Dynamic Latent Control (DLC) at selected steps estimates a clean latent, evaluates a VFM-based motion–semantic loss on proxy frames, and applies a corrective update to steer the trajectory. To control overhead, Amortization Trajectory Rectification (ATR) interleaves sparse calibration with fast amortized steps by caching a rectification field. Second, Dynamic RoPE Control (DRC) modulates temporal RoPE in attention blocks from the same motion signals, strengthening long-range coherence or sharpening short-range dynamics when flicker is detected. Extensive experiments on VBench and VBench 2.0 show that our method improves advanced motion fidelity metrics while incurring a small trade-off on superficial temporal smoothness for some backbones.

We present Generative Relightable Avatars (GRA), a personspecific method for photorealistic free-view rendering and environmentmap relighting of full-body humans. We postulate that modeling finegrained appearance details is inherently a one-to-many problem that can benefit from a generative formulation. In contrast to fully regressive relightable avatar methods, GRA follows a hybrid approach that combines controllable, physics-grounded relighting with probabilistic refinement. Starting from a tracked animated mesh, we optimize material parameters in UV-space and render a coarse relit appearance under a target HDR environment map. Next, we refine the textures with a feedforward model to capture pose-dependent texture dynamics and illumination effects beyond simplified reflectance assumptions. Finally, a finetuned video-to-video diffusion model transforms the physically grounded renderings into temporally coherent, high-detail videos while preserving 3D control, with an error-recycling strategy for generating long videos. Experimental evaluations demonstrate our method’s improved perceptual quality over prior relightable avatar baselines. We urge the readers to watch the supplementary video. See the project page for more details.

Diffusion models have made significant progress in both text-to-image (T2I) generation and text-guided image editing. However, thesemodels are typically built with billions of parameters, leading to high la-tency and increased deployment challenges. While on-device diffusionmodels improve efficiency, they largely focus on T2I generation and lacksupport for image editing. In this paper, we propose DreamLite, a com-pact unified on-device diffusion model (0.39B) that supports bothT2I generation and text-guided image editing within a single network.DreamLite is built on a pruned mobile U-Net backbone and unifies con-ditioning through in-context spatial concatenation in the latent space.It concatenates images horizontally as input, using a (target | blank)configuration for generation tasks and (target | source) for editing tasks.To stabilize the training of this compact model, we introduce a task-progressive joint pretraining strategy that sequentially targets T2I, edit-ing, and joint tasks. After high-quality SFT and reinforcement learning,DreamLite achieves GenEval (0.72) for image generation and ImgEdit(4.11) for image editing, outperforming existing on-device models and re-maining competitive with several server-side models. By employing stepdistillation, we further reduce denoising processing to just 4 steps, en-abling our DreamLite could generate or edit a 1024 × 1024 image in lessthan 3s on iPhone 17 pro. To the best of our knowledge, DreamLiteis the first unified on-device diffusion model that supports both imagegeneration and image editing. Our code and model are available at here.

Reasoning segmentation aims to predict pixel-wise masks fortargets given complex language queries. Existing approaches leverageMultimodal Large Language Models (MLLMs) for vision-language rea-soning and generate intermediate target cues (e.g., points or boxes) toguide a segmentation model. However, compressing rich reasoning intosparse cues often introduces ambiguity and noise, preventing these cuesfrom accurately preserving the reasoning intent. While multiple comple-mentary cues can enrich target information, existing methods typicallyfeed them jointly into a single segmentation process, allowing ambiguousor erroneous cues to affect the entire prediction. Therefore, we proposeDGSeg, a reasoning segmentation framework that learns to fuse pre-dictions guided by semantic and spatial cues. Specifically, the MLLMjointly reasons about both target identity and spatial location, produc-ing complementary semantic and spatial cues that are fed into separatesegmentation branches. Their predictions are adaptively integrated by alightweight dynamic gating module trained with relative branch-qualitysupervision to suppress noisy or conflicting regions. Extensive experi-ments demonstrate that DGSeg consistently outperforms strong base-lines on multiple benchmarks and achieves 69.6% and 67.3% gIoU on thechallenging ReasonSeg validation and test splits.

Object detection models deployed in safety-critical applica-tions remain vulnerable to backdoor attacks that cause targeted misbe-haviors when a hidden trigger is present. Existing detection methods ei-ther rely on trigger inversion or exploit architecture-specific assumptions,and critically, representative existing methods fail to generalize reliablyto scene-level attacks, where a single trigger induces anomalous behavioracross all objects in the scene simultaneously. We present DistScan, abackdoor detection framework based on a simple but previously unex-ploited observation: backdoor injection systematically shifts a model’spre-NMS prediction class distribution away from its training class fre-quencies, even on clean inputs without any trigger present. DistScanaggregates intermediate class predictions over a clean validation set andflags a model as backdoored if the resulting distribution deviates sig-nificantly from the training class frequencies, requiring no model weightaccess, no trigger knowledge, and no additional training. Extensive exper-iments on MS-COCO and PASCAL VOC across two architectures andthree scene-level attack scenarios demonstrate that DistScan substan-tially outperforms existing methods, improving average detection accu-racy over the best-performing applicable baseline by 27.32 percentagepoints.

From a Bayesian perspective, score-based di_x001B_usion solves in-verse problems through joint inference, embedding the likelihood withthe prior to guide the sampling process. However, this formulation failsto explain its practical behavior: the prior o_x001B_ers limited guidance, whilereconstruction is largely driven by the measurement-consistency term,leading to an inference process that is e_x001B_ectively decoupled from thedi_x001B_usion dynamics. We show that the di_x001B_usion prior in these solversfunctions primarily as a warm initializer that places estimates near thedata manifold, while reconstruction is driven almost entirely by measure-ment consistency. Based on this observation, we introduce DAPS++,which fully decouples di_x001B_usion-based initialization from likelihood-drivenre_x001C_nement, allowing the likelihood term to guide inference more directlywhile maintaining numerical stability and providing insight into whyuni_x001C_ed di_x001B_usion trajectories remain e_x001B_ective in practice. By requiringfewer function evaluations (NFEs) and measurement-optimization steps,DAPS++ achieves high computational e_x001E_ciency and robust reconstruc-tion performance across diverse image restoration tasks.