ECCV 2026 论文列表

Estimating the 6D pose of unseen objects is a fundamentalyet challenging problem for open-world robotics and embodied percep-tion. Model-based methods are accurate but depend on CAD assets orheavy onboarding, while most model-free approaches are still limitedto pairwise single-anchor matching and thus fail under occlusion andlarge viewpoint changes with low query–reference overlap. Therefore, wepresent PANY, a unified model-free framework that seamlessly sup-ports both RGB and RGB-D inputs, operates on one or sparse pose-freereference views, and generalizes effectively to novel objects. Built on amulti-view transformer geometry backbone, PANY moves beyond pair-wise matching by learning view-consistent geometry and cross-view align-ment cues that remain stable under wide baselines and limited overlap.When additional unposed assist views are available, PANY aggregatesthem via pose-graph canonical registration to increase geometric cover-age and reinforce the final pose. Extensive experiments show that PANYachieves state-of-the-art performance across multiple benchmarks, sub-stantially outperforming existing model-free methods, improving poseaccuracy by +12% on YCB-V and over +20% on LM-O. Further-more, PANY consistently performs well under both single-reference andsparse-reference settings, demonstrating strong robustness in real-worldenvironments.

Neural Parametric Head Models (NPHMs) are a recent ad-vancement over mesh-based 3d morphable models (3DMMs) to facilitatehigh-fidelity geometric detail. However, fitting NPHMs to visual inputsis notoriously challenging due to the expressive nature of their under-lying latent space, which heavily limits NPHM’s practical use. To thisend, we propose Pix2NPHM, a vision transformer (ViT) network that di-rectly regresses NPHM parameters, given a single image as input, finallybridging the gap from theoretical to practical use. Compared to exist-ing 3DMM regressors, the neural parametric space allows our methodto reconstruct more recognizable facial geometry and accurate facial ex-pressions. For broad generalization, we exploit domain-specific ViTs asbackbones, which are pretrained on geometric prediction tasks. We trainPix2NPHM on a mixture of 3D data, including a total of over 100KNPHM registrations, and large-scale 2D video datasets, for which nor-mal estimates serve as pseudo ground truth. While Pix2NPHM runs atinteractive frame rates, it is possible to improve geometric fidelity by asubsequent optimization against estimated surface normals and canoni-cal point maps. As a result, we achieve unprecedented face reconstructionquality that can run at scale on in-the-wild data.

Recently, self-evolving large multimodal models (LMMs) havereceived attention for improving visual reasoning in a purely unsuper-vised setting. However, multi-role self-play and self-consistency rewardschemes in existing self-evolving LMMs optimize answer agreement with-out ensuring the decoder attends to visual content, relying instead onstatistical language priors to produce self-consistent outputs. This leadsto a persistent failure mode we term visual under-conditioning, wherethe decoder relies on language priors rather than the image during gen-eration, manifesting as insufficient attention to visual tokens. As a result,current self-evolving LMMs struggle on vision–language understandingtasks such as image captioning and visual question answering. To addressthis, we propose VISE (Visual Invariance Self-Evolution), a purely un-supervised self-evolving framework that directly regularizes the model’svisual conditioning policy through two complementary invariance-basedrewards: a geometric invariance reward that enforces spatial consistencyunder known transformations, and a semantic invariance reward thatpenalizes evidence-agnostic generation by requiring the model to recog-nize the absence of evidence when predicted regions are perturbed. VISEoperates within a single model without specialist roles, external rewardmodels, or annotations, and is trained on raw unlabeled images. Ex-periments on 18 benchmarks demonstrate the efficacy of our approach.Using Qwen3-VL-2B as the base model, VISE achieves gains of +16.85CIDEr on COCO and +19.66 CIDEr on TextCaps, reduces object hal-lucination by 5.0 Chair-I points, and generalizes across four model fam-ilies and scales. Our code and models are available at https://mbzuai-oryx.github.io/VISE/.

We present One4D, a unified framework for 4D generationand reconstruction that produces dynamic 4D content as synchronizedRGB frames and pointmaps. By consistently handling varying sparsitiesof conditioning frames through a Unified Masked Conditioning (UMC)mechanism, One4D can seamlessly transition between 4D generationfrom a single image, 4D reconstruction from a full video, and mixed gen-eration and reconstruction from sparse frames. Our framework adapts apowerful video generation model for joint RGB and pointmap generation,with carefully designed network architectures. The commonly used diffu-sion finetuning strategies for depthmap or pointmap reconstruction oftenfail on joint RGB and pointmap generation, quickly degrading the basevideo model. To address this challenge, we introduce Decoupled LoRAControl (DLC), which employs two modality-specific LoRA adapters toform decoupled computation branches for RGB frames and pointmaps,connected by lightweight, zero-initialized control links that graduallylearn mutual pixel-level consistency. Trained on a mixture of syntheticand real 4D datasets under modest computational budgets, One4D pro-duces high-quality RGB frames and accurate pointmaps across both gen-eration and reconstruction tasks. This work represents a step towardgeneral, high-quality geometry-based 4D world modeling using video dif-fusion models. Project page: https://mizhenxing.github.io/One4D.

Generative world models increasingly rely on 4D occupancyfor realistic autonomous driving simulation. However, existing genera-tion frameworks are limited in two respects: geometric-condition-basedmethods depend on explicit trajectories and cannot generalize to free-form instructions, while text-based methods operate at a coarse attributelevel and fail to orchestrate complex, sequential multi-agent interactions.We propose OccDirector, a framework that generates 4D occupancy dy-namics conditioned solely on natural language scripts, without requir-ing any geometric priors. OccDirector encodes free-form scripts via afrozen VLM and processes occupancy tokens through a Spatio-TemporalMMDiT backbone. A history-prefix anchoring strategy further ensuresthat generated sequences remain consistent with any provided contextover long horizons. To support training and evaluation, we introduceOccInteract-85k, a dataset of 85k clips annotated with multi-level lan-guage instructions spanning static layouts to intricate multi-agent be-haviors, alongside a novel VLM-based evaluation benchmark. Extensiveexperiments demonstrate state-of-the-art generation quality and stronginstruction-following fidelity across diverse driving scenarios.

Diffusion models have recently advanced text-to-video (T2V)generation, yet they still struggle with fine-grained compositional align-ment, such as attribute binding, spatial relations, and object interactions.While reward-based fine-tuning improves alignment, it is susceptible toreward hacking and adapts poorly to new prompt distributions. In thiswork, we propose NoisEasier, a test-time scaling framework that im-proves T2V generation through differentiable reward-guided noise opti-mization without modifying the underlying model. By combining effi-cient short-step generators with a multi-objective reward formulation,NoisEasier enables stable and practical test-time optimization under re-alistic inference budgets. Our key insight is that jointly optimizing theentire stochastic trajectory accelerates reward convergence and improvescompositional alignment over optimizing only the initial latent, with neg-ligible additional computational and time cost. Experiments on VBenchand T2V-CompBench demonstrate consistent improvements across mul-tiple backbones, achieving over 10% average gains on challenging dimen-sions such as attribute binding, object interaction, and numeracy. Over-all, NoisEasier serves as both a flexible alternative and a complementaryenhancement to reward-based fine-tuning, establishing test-time scalingas an effective paradigm for controllable text-to-video generation.

Thermal-to-visible face translation presents fundamental challenges including geometric discontinuities, semantic attribute mismatches, and identity degradation. We propose MTVDiff, a novel multimodal latent diffusion framework that synergistically integrates depth and textual information to address these limitations while preserving identity characteristics. The MTVDiff framework presents three core technical contributions: (1) a Dual-Branch Cross-Attention Fusion (DBCAF) module for multi-scale thermal-depth feature extraction and fusion; (2) a Gated Text-to-Visual Feature Alignment mechanism for semantically-guided generation; and (3) Spatial Feature Transformations (SFT) for adaptive multimodal prior integration. Extensive experiments on the MCXFace and SpeakingFaces datasets demonstrate that our multimodal approach significantly outperforms existing GAN-based and diffusion-based approaches across multiple metrics, achieving substantial improvements in both image quality and face verification performance, with FID reductions of up to 48.3% and Rank-1 accuracy improvements of up to 8.9%. Our work provides a robust solution for face recognition systems operating under varying illumination conditions and advances the state-of-theart in cross-spectral facial image translation through effective multimodal integration.

Makeup-transfer applies the reference makeup to the sourceface while preserving the source identity. Despite advances in full-faceediting by diffusion-based methods, strong regional controllability, makeupfidelity, and identity preservation remain challenging. The reasons are(i) pixel-to-attention misalignment that causes spillover into non-targetareas and weakens regional control; (ii) unclear transfer/preservationconcept separation under two-image conditioning, leading to couplingbetween makeup attributes and identity; and (iii) the lack of a high-resolution dataset that is identity-consistent and region-labeled for fine-grained supervision. In this paper, we propose MagicMakeup, a dif-fusion transformer-based framework for region-controllable and high-fidelity makeup transfer, built on spatial constraints and concept dis-entanglement. To enable precise region-specific editing while preservingidentity, we propose Token-Aligned Region Gating, which aligns pixelmasks with attention and applies region-specific logit gating. To clarifythe concepts of transfer and preservation, we further introduce Cross-Modal Perception Guidance, which aligns text and image features to en-hance cross-modal concept perception. We also design a pipeline for thegeneration of 1024 × 1024 data pairs through region-specific makeup re-moval and establish a unified benchmark in synthetic and real settings.Extensive quantitative and qualitative experiments show that Magic-Makeup improves regional controllability, makeup fidelity, and identitypreservation, with strong robustness across styles, races, and poses.

Face recognition in unconstrained environments remains high-ly challenging due to diverse and extreme variations encountered inreal-world scenarios. To mitigate these effects, existing margin-basedapproaches model sample quality through feature magnitude. However,magnitude-based modeling alone is susceptible to identity-agnostic noise,which can degrade the reliability and discriminative power of learned rep-resentations. In this paper, we propose Dual Quality Margin Learning forFace Recognition (DQM-Face), a novel framework that enables refinedattraction and repulsion dynamics during representation learning. Ourapproach unifies conventional magnitude-based quality estimation witha newly introduced semantic quality learning mechanism, realized viasqueeze-and-excitation semantic attention. By jointly leveraging magni-tude and semantic cues, we construct enhanced quality-aware marginsthat adaptively strengthen intra-class compactness through improvedattraction during learning. To further enhance inter-class discrimina-tion, we introduce a repulsion margin formulation that explicitly en-larges inter-class separation. The unified integration of semantic qualitymodeling with dual attraction–repulsion margin optimization results ina more structured and discriminative feature geometry. Extensive ex-periments on multiple challenging benchmarks demonstrate that DQM-Face consistently outperforms state-of-the-art face recognition methods.Moreover, we show that the quality learned for margin optimization ishighly effective for face image quality assessment within the proposedframework, demonstrating that the learned quality signal is intrinsicallyaligned with the recognition objective. The code is publicly available:https://github.com/RAIB-group/DQM-Face

We propose HeadsUp, a scalable feed-forward method forreconstructing high-quality 3D Gaussian heads from large-scale multi-camera setups. Our method employs an efficient encoder-decoder archi-tecture that compresses input views into a compact latent representation.This latent representation is then decoded into a set of UV-parameterized3D Gaussians anchored to a neutral head template. This UV represen-tation decouples the number of 3D Gaussians from the number andresolution of input images, enabling training with many high-resolutioninput views. We train and evaluate our model on an internal dataset withmore than 10 000 subjects, which is an order of magnitude larger than ex-isting multi-view human head datasets. HeadsUp achieves state-of-the-artreconstruction quality and generalizes to novel identities without test-timeoptimization. We extensively analyze the scaling behavior of our modelacross identities, views, and model capacity, revealing practical insightsfor quality-compute trade-offs. Finally, we highlight the strength of ourlatent space by showcasing two downstream applications: generating novel3D identities and animating the 3D heads with expression blendshapes.

Executing long-term tasks in dynamic environments requiresembodied agents to maintain robust and adaptive 3D scene represen-tations. However, most existing 3D memory frameworks rely on staticworld assumptions. When objects are displaced by human activities orunobserved events, agents encounter memory-observation conflicts andoften require costly geometric recomputations or inefficient global re-exploration. To address this, we propose HitMem, a hierarchical tempo-ral 3D memory framework with a multi-modal context-aware retrievalmechanism. Through continuous perception, HitMem unifies semanticand spatial information into a lightweight topological graph that cap-tures support relationships, while a temporal decay mechanism dynami-cally regulates memory activeness to mitigate the impact of stale repre-sentations. In addition, the multi-modal context-aware retrieval mecha-nism defaults to filtering candidates using integrated semantic, spatial,and temporal memory features, and activates a specialized two-stage re-trieval process when object displacement is detected. This process com-bines spatial constraints inferred from external agent trajectories withsemantic common sense grounded in class affinities, efficiently identi-fying high-probability candidate regions. Extensive evaluations on ourconstructed Dyna-THOR benchmark demonstrate that HitMem signif-icantly improves object relocation accuracy, reduces exploration costs,and enhances task execution performance in dynamic environments.

We propose a societal bias evaluation method for large visionlanguage models (LVLMs) in the era of strong safety guardrails. Existing benchmarks rely on prompts that ask models to infer attributes of people in images (e.g., “Is this person a CEO or a secretary?”). However, we find that LVLMs with strong guardrails, such as GPT and Claude, often refuse these prompts, making evaluations unreliable. To address this, we change the prior evaluation paradigm by decoupling the task from the depicted person: instead of inferring person’s attributes, we use prompts that do not ask about the person (e.g., “Write a fictional story about an imaginary person.”) and attach the image as provisional user information to implicitly provide demographic cues, then compare outputs across user demographics. Instantiated across three tasks — story generation, term explanation, and exam-style QA — our method avoids refusals even in guardrailed LVLMs, enabling reliable bias measurement. Applying it to 20 recent LVLMs, both open-source and proprietary, we find that all models undesirably use user demographic information in person-irrelevant tasks; for instance, characters in stories are often portrayed as mechanic for male users and nurse for female users. Although still biased, proprietary models like GPT-5 show lower bias than opensource ones. We analyze potential factors behind this gap, discussing continuous model monitoring and improvement as a possible contributor for reducing bias.

Previous works that leverage video models for image-to-3Dscene generation often suffer from geometric distortions and blurry con-tent. Using video generation models to implicitly maintain geometricconsistency according to a single-frame input is ineffective. In this paper,we present a two-stage method, named GeoWorld, that renovates theimage-to-3D scene generation pipeline by providing full-frame geometryfeatures. The first-stage video generation model, followed by a multi-viewgeometry model, produces full-frame geometry features, which are thenused as a mental draft of geometric conditions to aid the second-stagevideo-generation model. A geometric loss is proposed to impose real-worldgeometric constraints, and a geometry adaptation module is introducedto ensure the effective utilization of geometry features. Thanks to full-frame geometric modeling, the two smaller video models in our two-stagemethod can generate higher-fidelity 3D scenes than SOTA methods, whilebeing even faster, e.g. 7.5× faster than Hunyuan-Voyager. Project page:https://peaes.github.io/GeoWorld.

Time awareness is a fundamental ability of omni-modal largelanguage models, especially for understanding long videos and answeringcomplex questions. Previous approaches mainly target vision-languagescenarios and focus on the explicit temporal grounding questions, suchas identifying when a visual event occurs or determining what event hap-pens at a specific time. However, they often make insufficient use of theaudio modality, and overlook implicit temporal grounding across modal-ities (for example, identifying what is visually present when a characterspeaks, or determining what is said when a visual event occurs), de-spite such cross-modal temporal relations being prevalent in real-worldscenarios. In this paper, we first formally define the audiovisual tem-poral grounding task to systematically encompass both explicit andimplicit cross-modal temporal relations. To tackle this task, we proposeChronusOmni, an omni-modal large language model designed to en-hance temporal awareness for both explicit and implicit audiovisual tem-poral grounding. Specifically, we interleave text-based timestamp tokenswith visual and audio representations at each time unit, enabling uni-fied temporal modeling across modalities. Furthermore, to enforce correcttemporal ordering and strengthen temporal reasoning, we incorporate re-inforcement learning with specially designed reward functions. Moreover,we construct ChronusAV, a temporally-accurate, modality-complete, andcross-modal-aligned dataset to support the training and evaluation of theaudiovisual temporal grounding task. Experimental results demonstratethat ChronusOmni achieves state-of-the-art performance on audiovisualtemporal grounding datasets such as ChronusAV and LongVALE, out-performing the second-best results by 17.3% and 28.8%, respectively, anddelivers top-tier performance on visual-only temporal grounding datasetsincluding Charades-STA and ActivityNet. These achievements highlightthe strong temporal awareness of our model across modalities, while pre-serving its robust capabilities in general video and audio understanding.

We perceive our surrounding environments with an active fo-cus, paying more attention to regions of interest, such as the shelf labelsin a grocery store or a family photo on the wall. When it comes to scenereconstruction, this human perception trait calls for spatially varyingdegrees of detail ready for closer inspection in critical regions, prefer-ably reconstructed on demand as users shift their focus. While recentapproaches in 3D Gaussian Splatting (3DGS) can achieve fast, general-izable scene reconstruction from sparse views, their uniform resolutionoutput leads to high computational costs, making them unscalable tohigh-resolution training. As a result, they cannot leverage available imagecaptures at their original high resolution for detail reconstruction. Per-scene optimization methods reconstruct finer details with heuristic-basedadaptive density control, yet require dense observations and lengthy of-fline optimization. To bridge the gap between the prohibitive cost of high-resolution holistic reconstructions and the user needs for localized finedetails, we propose the problem of localized high-resolution reconstruc-tion through on-demand generalizable Gaussian densification. Given aninitial low-resolution 3DGS reconstruction, the goal is to learn a gener-alizable network that densifies the reconstruction to capture fine detailsin a user-specified local region of interest (RoI), based on sparse high-resolution observations of the RoI. This formulation avoids the high costand redundancy of uniformly high-resolution reconstructions and enablesthe full leverage of high-resolution observations in critical regions. To ad-dress the problem, we propose GaussianLens, a feed-forward densifica-tion framework that fuses multi-modal information from the initial 3DGSand multi-view images. We further propose a pixel-guided densificationmechanism that effectively captures details under significant resolutionincreases. Experiments demonstrate our method’s superior performancein local high-fidelity detail reconstruction and strong scalability to im-ages of up to 1024 × 1024 resolution.

Large Vision-Language Models (VLMs) have achieved re-markable multimodal performance yet remain prone to factual halluci-nations, particularly in long-tail or specialized domains. Moreover, cur-rent models exhibit a weak capacity to refuse queries that exceed theirparametric knowledge. In this paper, we propose a systematic frame-work to enhance the refusal capability of VLMs when facing such un-known questions. We first curate a model-specific "Visual-Idk" (Visual-Idon’t know) dataset, leveraging multi-sample consistency probing to dis-tinguish between known and unknown facts. We then align the modelusing supervised fine-tuning followed by preference-aware optimization(e.g., DPO, ORPO) to effectively delineate its knowledge boundaries. Re-sults on the Visual-Idk dataset show our method improves the TruthfulRate from 57.9% to 67.3%. Additionally, internal probing also demon-strates that the model genuinely recognizes its boundaries instead of justmemorizing refusal patterns. Our framework further generalizes to out-of-distribution medical and perceptual domains, providing a robust pathtoward more trustworthy and prudent visual assistants.

Group Activity Detection (GAD) aims to jointly infer groupmemberships and collective activities from videos. Existing approachestypically rely on actor-centric features to model group activities, assum-ing that collective semantics can be inferred solely from inter-actor re-lations. This overlooks critical context-interactive information, such asexplicit spatial constraints and actor–scene dependencies, which are es-sential in crowded and multi-view environments. To address these lim-itations, we propose a Context-Interactive Reasoning framework thatjointly models spatial topology and group semantic context. Specifically,a Short-Term Interaction Regularizer (STIR) softly constrains inter-actor spatial relations with learnable frame-level topology priors, sup-pressing spurious connections and promoting spatially coherent group-ing. Complementarily, a Long-Range Context Conditioner (LRCC) se-lectively incorporates global scene semantics via data-dependent gating,enabling activity-aware context utilization while avoiding unnecessarynoise. Extensive experiments on challenging benchmarks demonstratethat our method achieves state-of-the-art performance in both groupmembership identification and collective activity recognition.

Multimodal large language models (MLLMs) have made rapidprogress, yet they still exhibit object hallucination, generating plausiblebut incorrect descriptions that are inconsistent with the visual input. Di-rect Preference Optimization (DPO) mitigates this by training modelsto prefer non-hallucinated responses over hallucinated ones, and recentefforts further enrich the preference data with relevant context. How-ever, it remains unclear whether DPO actually leverages such context.To investigate this, we propose Contextual Preference Gain (CPG), asimple metric that measures how much a model’s preference strengthenswhen relevant context is provided. We find that higher CPG consistentlycorresponds to lower hallucination, yet standard DPO and its variantsexhibit only limited CPG, indicating that they underutilize contextualinformation and thus remain prone to hallucination. To address this, wepropose Context-Calibrated DPO (C2 -DPO), which directly maximizesCPG while preserving the original preference ordering. Across multiplebenchmarks, C2 -DPO substantially reduces hallucination without com-promising general reasoning, relatively reducing the Object HalBenchhallucination rate of Qwen2-VL-Instruct-2B by 36%. Code is availableat https://github.com/mlvlab/C2-DPO

Composition is a high-level visual intent that governs wheresubjects are placed and how a scene is organized, yet current unifiedmultimodal models remain unreliable at fine-grained composition recog-nition and struggle to turn such intent into controllable generation. Wepresent COMPASS, the first unified multimodal framework that groundscomposition-intent control in a single system spanning both compositionperception and composition-guided generation, with a shared expert to-ken τc as the central intent anchor. On the perception side, COMPASSinjects composition expertise into an MoE backbone in a minimally in-vasive manner and distills the inferred intent into τc . On the generationside, COMPASS reuses τc as a global conditioning signal that steers thedenoising trajectory, effectively converting passive composition analysisinto explicit layout control. To support systematic instruction-followingcomposition learning and evaluation at scale, we construct Comp-11, alarge-scale dataset with an 11-class taxonomy and reasoning-augmentedannotations. Extensive experiments show that COMPASS substantiallyimproves category-level composition understanding and delivers morecomposition-consistent, prompt-faithful generation than strong baselines.The code and dataset for this work will be released here.