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ECCV 2026

MUSE: Unlocking Timestep as Native Task Steering for One-Step Dense Prediction

Shuo Zhou, Zhaoxin Li, Xiujuan Chai

Monocular dense prediction has recently seen remarkablesuccess by repurposing pre-trained diffusion models. This opens a promis-ing yet challenging avenue for more efficient multi-task learning paradigm.However, existing multi-task diffusion methods often introduce parameter-heavy adapters, experts, or learnable task tokens, leading to computa-tional redundancy. In this paper, we reveal an inherent mechanism withinone-step diffusion models: the native, fixed timestep positional embed-ding can be repurposed as an endogenous task steering signal. Based onthis discovery, we propose Multi-task Unified eStimation via timestepEmbedding (MUSE), a parameter-free, single-model multi-tasking ap-proach for dense prediction. We interpret this mechanism via Mani-fold Decoupling, where discrete, fixed timestep values deterministicallysteer the generation process towards decoupled, task-specific manifolds inthe latent space. Extensive experiments across 10 datasets demonstratethat MUSE achieves highly competitive performance on both monoculardepth and normal estimation, and its efficacy generalizes across U-Netand DiT architectures. Our work offers a concise and efficient path to-ward generalist vision models by simply unlocking the latent potentialof existing generation infrastructure.

ECCV 2026

Multi-Head Normalization for Wide Vision Transformers

Guoyizhe Wei, Feng Wang, Alan Yuille, Rama Chellappa

Scaling the width of Vision Transformers (ViT) often triggerssevere instability issues. This work identifies that these instabilities stemfrom the “high-activation domination” phenomenon frequently encoun-tered during feature normalization in high-dimensional spaces. Specifi-cally, the norm of a high-dimensional vector can be easily dominated by afew outlier elements with disproportionately large values. Upon normal-ization, the remaining elements are suppressed toward zero, leading tosignificant gradient propagation issues. To address this, this work intro-duces Multi-Head RMSNorm (MH-RMSNorm), which partitions a high-dimensional feature vector into multiple chunks and applies RMSNormto each segment independently. This simple, drop-in replacement sig-nificantly enhances the training stability and predictive performance ofhigh-dimensional ViTs. For example, Our 1280-dimensional ViT achieves85.5% top-1 accuracy on ImageNet-1K, whereas baseline models usingstandard LayerNorm or RMSNorm suffer from complete training col-lapse; when applied to DiT on ImageNet-256, our method achieves a1.95 FID, significantly outperforming standard ViT counterparts.

ECCV 2026

ETCH-X: Robustify Expressive Body Fitting to Clothed Humans with Composable Synthetic Data

Xiaoben Li, Jingyi Wu, Zeyu Cai, YU Siyuan, Boqian Li, Yuliang Xiu

Human body fitting, which aligns parametric body models,such as SMPL, to raw 3D point clouds of clothed humans, serves as acrucial first step for downstream tasks like animation and texturing. Aneffective fitting method should be both locally expressive – capturingfine details such as hands and facial features – and globally robust tohandle real-world challenges, including clothing dynamics, pose variations,and noisy or partial inputs. Existing approaches typically excel in onlyone aspect, lacking an all-in-one solution. We upgrade ETCH to ETCH-X,which leverages a tightness-aware fitting paradigm to filter out clothingdynamics (“undress”), extends expressiveness with SMPL-X, and replacesexplicit sparse markers (which are highly sensitive to partial data) with im-plicit dense correspondences (“dense fit”) for more robust and fine-grainedbody fitting. Our disentangled “undress” and “dense fit” modular stages en-able separate and scalable training on composable data sources, includingdiverse simulated garments (CLOTH3D), large-scale full-body motions(AMASS), and fine-grained hand gestures (InterHand2.6M), improvingoutfit generalization and pose robustness of both bodies and hands. Ourapproach achieves robust and expressive fitting across diverse clothing,poses, and levels of input completeness, delivering a substantial perfor-mance improvement over ETCH on both 1) seen data, such as 4D-Dress(MPJPE-All, 33.0% ↓) and CAPE (V2V-Hands, 35.8% ↓), and 2) unseendata, such as BEDLAM2.0 (MPJPE-All, 80.8% ↓; V2V-All, 80.5% ↓).Code and models will be released at xiaobenli00.github.io/ETCH-X.

ECCV 2026

LIBERO-Safety: A Comprehensive Benchmark for Physical and Semantic Safety in Vision-Language-Action Models

Rongxu Cui, Zongzheng Zhang, Jingrui Pang, Haohan Chi, Jinbang Guo, Saining Zhang, Shaoxuan Xie, Xin Jin, Yao Mu, Jiaolong Yang, Guocai Yao, Xianyuan Zhan, Ya-Qin Zhang, Hao Zhao

Despite the impressive manipulation capabilities of Vision-Language-Action (VLA) models, their operational safety under strictconstraints remains largely unverified. To address this, we introducea parametric safety benchmark to procedurally generate safety-criticalscenarios with comprehensive stochasticity. To overcome the scalabilitybottlenecks of human teleoperation, we develop a novel keypose-drivendata generation pipeline. Leveraging this infrastructure, we curate alarge-scale dataset of 19,664 strictly collision-free demonstrations withextensive domain randomization. We then conduct a systematic cross-paradigm evaluation of eight VLA and two embodied foundation mod-els. Our analysis reveals a critical generalization-safety tension: althoughhigh-diversity training fosters safer trajectories, task success remains fun-damentally bottlenecked by sub-optimal trajectory synthesis and seman-tic misalignment. By providing a scalable pipeline, a robust dataset, andprofound failure-mode insights, LIBERO-Safety establishes a crucialfoundation for developing safe and reliable VLA models.

ECCV 2026

LENS: Adaptive Spatio-Temporal Zooming for Keyframe Sampling in Long-Form Videos

Ce Zhang, Jinxi He, Yaqi Xie, Katia Sycara

Despite rapid progress in Multi-modal Large Language Mod-els (MLLMs), understanding long-form videos is still bottlenecked bylimited context windows. While recent keyframe sampling methods at-tempt to mitigate this by distilling video inputs into a compact set ofquery-relevant frames, navigating the vast spatio-temporal search spaceremains challenging, as spatial detail and temporal coverage often conflict.To address this, we introduce LENS, a training-free keyframe samplingframework that dynamically decides when to zoom in for fine-graineddetails and when to zoom out for broader context based on the textquery. Concretely, LENS adaptively allocates a limited frame budgetbetween spatial zoom-ins, which highlight query-relevant regions withinindividual frames, and temporal zoom-outs, which expand the temporalscope through multi-frame aggregation, enabling the model to reasonacross multiple granularities while capturing both high-fidelity details andlong-range context. Across diverse long-form video benchmarks, LENSconsistently outperforms prior state-of-the-art keyframe sampling meth-ods and delivers substantial gains over uniform sampling, improvingVideo-MME accuracy from 53.3% to 60.7% with Qwen2.5-VL. Code isavailable at https://github.com/zhangce01/LENS.

ECCV 2026

Learning Geometry-Aware Embedding Fields for Intrinsic Riemannian Mappings

Bo Pang, Simone Foti, Tolga Birdal

Computing exponential and logarithmic maps on raw geome-tries is a fundamental yet challenging task in geometry processing, oftenhindered by the lack of explicit connectivity and the presence of noise.We present a novel learning-based framework that computes these mapsefficiently by learning a continuous geometric embedding field directlyfrom discrete point sets. Our key insight is to construct an embeddingfield that encapsulates the intrinsic local geometry governing exponen-tial and logarithmic maps. Specifically, we encode the input into a high-dimensional feature volume using a sparse Octree-based CNN. For anyarbitrary query point, we retrieve its corresponding embedding to mod-ulate two specialized triplane-based neural networks, which then predictthe maps in a single forward pass. By formulating the problem as learninga query-able latent field, our method bypasses the need for mesh con-nectivity while ensuring robustness against irregular sampling and noise.Our results demonstrate that the proposed framework achieves compet-itive accuracy and runtime over previous methods, while maintainingrobustness on challenging geometric structures.

ECCV 2026

Keep It Simple: Multi-Key Episodic Memory Retrieval for Ultra-Long Video Understanding

Yeeun Choi, Youngbeom Yoo, Joon-Young Lee, Hyolim Kang, Seon Joo Kim

When videos extend from hours to days, directly process-ing them end-to-end becomes impractical for current Multi-modal LargeLanguage Models (MLLMs). This ultra-long setting necessitates a two-stage paradigm: query-agnostic memory construction followed by retrieval-based inference. Prior work invests in complex memory construction topre-model high-level relations in videos, despite not knowing the down-stream query at build time. We instead prioritize high-recall retrievabil-ity during memory building, and defer query-specific, high-level relationcomposition to inference time. To this end, we propose MERIT (Multi-key Episodic Retrieval with Inference-time Temporal expansion), a sim-ple yet effective agentic framework for ultra-long video understanding.First, we formulate an episodic multi-key representation that enablesprecise retrieval of fine-grained memories through a simple key-matchingmechanism. Second, we introduce a neighbor filtering mechanism to cap-ture broader semantic context without the massive computational over-head of global memory construction. This is achieved by expanding thetemporal scope exclusively around the retrieved segments at inferencetime. By leveraging simple key-matching with this on-demand tempo-ral expansion, MERIT achieves state-of-the-art performance across threelong-video benchmarks: EgoLifeQA, LVBench, and Video-MME(Long).

ECCV 2026

HyFL-CLIP: Hyperbolic Fine-Tuning of CLIP for Robust Long-Context Understanding

jiha jang, Hayeon Kim, Junghun James Kim, Chulwon Lee, Se Young Chun

CLIP (Contrastive Language-Image Pre-training) has be-come a de facto paradigm for image-text alignment, but it struggleswith long-context descriptions (> 77 tokens) due to absolute positionalencoding and pretraining on short captions. In long contexts, sentencesare often reordered, summarized, or partially omitted. Although priorworks extend CLIP with longer positional encodings, they often sufferfrom degraded image-text alignment under such text perturbations. Weattribute this limitation to the Euclidean contrastive objective, whichenforces strict one-to-one matching and lacks explicit mechanisms formodeling hierarchical relationships between global context and its con-stituent elements. To address this issue, we propose HyFL-CLIP, a hy-perbolic fine-tuning framework that distills the well-established text-image alignment learned in Euclidean CLIP into hyperbolic space viacross-manifold similarity distillation, leveraging its geometry to capturehierarchical and entailment relations. Our method models hierarchicalsemantics by linking summarized token-wise features, long-context de-scriptions, constituent short textual components, and images, capturingpart–whole relationships via hyperbolic entailment with Einstein mid-point aggregation. Experiments on diverse benchmarks, including long-context cross-modal retrieval, cross-modal retrieval with caption pertur-bations, intra-modality retrieval, and short-text cross-modal retrieval,show that HyFL-CLIP achieves more robust long-context understand-ing. In particular, it yields up to 19.5% improvement in long-text cross-modal retrieval under textual perturbations over the best prior method.We also show HyFL-CLIP can be seamlessly integrated into other modelframeworks by applying it to Stable Diffusion XL (SDXL). The projectpage is available at https://janeyeon.github.io/hyflclip.

ECCV 2026

HSImul3R: Physics-in-the-Loop Reconstruction of Simulation-Ready Human–Scene Interactions

Yukang Cao, Haozhe Xie, Fangzhou Hong, Long Zhuo, Zhaoxi Chen, Liang Pan, Ziwei Liu

We present HSImul3R1 , a unified framework for simulation-ready 3D reconstruction of human-scene interactions (HSI) from casualcaptures, including sparse-view images and monocular videos. Existingmethods suffer from a perception-simulation gap: visually plausible re-constructions often violate physical constraints, leading to instability inphysics engines and failure in embodied AI applications. To bridge thisgap, we introduce a physically-grounded bi-directional optimiza-tion pipeline that treats the physics simulator as an active supervisorto jointly refine human dynamics and scene geometry. In the forwarddirection, we employ Scene-targeted Reinforcement Learning to optimizehuman motion under dual supervision of motion fidelity and contactstability. In the reverse direction, we propose Direct Simulation RewardOptimization, which leverages simulation feedback on gravitational sta-bility and interaction success to refine scene geometry. We further presentHSIBench, a new benchmark with diverse objects and interaction sce-narios. Extensive experiments demonstrate that HSImul3R produces thefirst stable, simulation-ready HSI reconstructions and can be directlydeployed to real-world humanoid robots.

ECCV 2026

HairOrbit: Multi-view Aware 3D Hair Modeling from Single Portraits

Leyang Jin, Yujian Zheng, Bingkui Tong, Yuda Qiu, Zhenyu Xie, Hao Li

Reconstructing strand-level 3D hair from a single-view image is highly challenging, especially when preserving consistent and realistic attributes in unseen regions. Existing methods rely on limited frontalview cues and small-scale/style-restricted synthetic data, often failing to produce satisfactory results in invisible regions. In this work, we propose a novel framework that leverages the strong 3D priors of video generation models to transform single-view hair reconstruction into a calibrated multi-view reconstruction task. To balance reconstruction quality and efficiency for the reformulated multi-view task, we further introduce a neural orientation extractor trained on sparse real-image annotations for better full-view orientation estimation. In addition, we design a two-stage strand-growing algorithm based on a hybrid implicit field to synthesize the 3D strand curves with fine-grained details at a relatively fast speed. Extensive experiments demonstrate that our method achieves state-ofthe-art performance on single-view 3D hair strand reconstruction on a diverse range of hair portraits in both visible and invisible regions.

ECCV 2026

Graph Coloring for Multi-Task Learning

Santosh Patapati, Ian Noronha

When different objectives conflict with each other in multi-task learning, gradients begin to interfere and slow convergence, therebypotentially reducing the final model’s performance. To address this, weintroduce SON-GOKU, a scheduler that computes gradient interference,constructs an interference graph, and then applies greedy graph-coloringto partition tasks into groups that align well with each other. At eachtraining step, only one group (color class) of tasks are activated, and thegrouping partition is constantly recomputed as task relationships evolvethroughout training. By ensuring that each mini-batch contains onlytasks that pull the model in the same direction, our method improvesthe effectiveness of any underlying multi-task learning optimizer withoutadditional tuning. Since tasks within these groups will update in compati-ble directions, multi-task learning will improve model performance ratherthan impede it. Empirical results on six different datasets show thatthis interference-aware graph-coloring approach consistently outperformsbaselines and state-of-the-art multi-task optimizers. We provide extensivetheory showing why grouping and sequential updates improve multi-tasklearning, with guarantees on descent, convergence, and the ability toaccurately identify what tasks conflict or align.

ECCV 2026

GeCo: Evaluating Geometric Consistency for Video Generation via Motion and Structure

Leslie Gu, Junhwa Hur, Charles Herrmann, Fangneng Zhan, Todd Zickler, Deqing Sun, Hanspeter Pfister

Visual generation models can produce photorealistic videosyet violate multiview geometry, exhibiting non-rigid deformations andocclusion inconsistencies (e.g., hallucinated content in disoccluded re-gions). These failures hinder downstream applications such as video worldmodel development and 3D asset creation, and are poorly captured byexisting metrics. We introduce GeCo, a geometry-grounded metric forjointly detecting geometric deformation and occlusion-inconsistency ar-tifacts in static scenes. By fusing residual motion and depth priors, GeCoproduces interpretable, dense consistency maps that localize these arti-facts. Using GeCo, we systematically benchmark recent video generationmodels, revealing common geometric failure modes. We further showthat GeCo provides an actionable signal by applying it as a training-free guidance loss that substantially reduces geometric artifacts duringgeneration.

ECCV 2026

Foundation-Guided Representation Alignment for Multimodal Medical Image Registration

Mengjie Guo*, Xinxing Cheng, Wenqi Lu, Qingjie Meng, Guanyu Yang, Yang Chen, Ziyun Ding, Alejandro Frangi, Jinming Duan

Multimodal medical image registration is crucial for diagno-sis and treatment planning, yet remains challenging due to significantappearance differences between modalities such as MR and CT. Themodality gap requires that the encoder in learning-based registrationmethods has cross-modal capabilities. Meanwhile, recent vision founda-tion models (VFMs) have demonstrated strong capabilities in extractingrich cross-modal representations that help bridge such modality gaps.Inspired by this, we propose a foundation-guided multimodal represen-tation alignment framework, which transfers the rich cross-modal knowl-edge from a pretrained VFM to the registration encoder. We introduce anovel Data ReAssembly strategy to transform volumetric medical imagesinto VFM input while preserving spatial information. Additionally, weimplement a Hierarchical Representation Fusion module to dynamicallyintegrate multi-level features from the VFM, yielding semantically andstructurally rich representations. Extensive experiments on two publicMR–CT datasets for brain and abdominal imaging demonstrate the ef-fectiveness of the proposed method compared with existing approaches.The code is available at https://github.com/MeggieGuo/FGRA-Reg.

ECCV 2026

Exploiting Local Flatness for Efficient Out-of-Distribution Detection

Seonghwan Park, Hyunji Jung, Dongyeop Lee, Namhoon Lee

Detecting out-of-distribution (OOD) data is crucial for reliable ma-chine learning deployment. Among detection strategies, post-hoc methods areparticularly attractive due to their efficiency, as they operate directly on pre-trainednetworks without requiring retraining. Within this paradigm, one promising di-rection exploits loss-landscape curvature to estimate model uncertainty; however,such methods incur substantial computational cost and rely on implicit assump-tions about how landscape flatness differs between in-distribution (ID) and OODdata. In this work, we provide the first systematic investigation of this curvaturediscrepancy and show that OOD inputs exhibit larger Hessian curvature than IDdata, with the gap widening under stronger distributional shifts. Motivated by theseobservations, we propose F OLD, a lightweight flatness-modulated OOD detectorthat leverages the feature Hessian and partial feature normalization to improveID-OOD separability while avoiding costly parameter-space curvature approxi-mations. To optimally adapt this normalization across diverse datasets, we furtherintroduce AUTO F OLD, a self-supervised tuning scheme that synthesizes pseudo-OOD samples via ID logit masking for automatic calibration without requiringexternal data. Experiments on OOD benchmarks show that F OLD outperformsprior methods, improving the average AUROC by 1.63% and reducing FPR95by 2.30%, while maintaining computational efficiency comparable to a standardforward pass. Supported by theoretical analysis and extensive ablations, F OLDprovides a principled and practical solution for robust real-world deployment.

ECCV 2026

EruDiff: Refactoring Knowledge in Diffusion Models for Advanced Text-to-Image Synthesis

Xiefan Guo, Xinzhu Ma, Haoxiang Ma, Zihao Zhou, Di Huang

Text-to-image diffusion models have achieved remarkable fi-delity in synthesizing images from explicit text prompts, yet exhibita critical deficiency in processing implicit prompts that require deep-level world knowledge, ranging from natural sciences to cultural com-monsense, resulting in counter-factual synthesis. This paper traces theroot of this limitation to a fundamental dislocation of the underlyingknowledge structures, manifesting as a chaotic organization of implicitprompts compared to their explicit counterparts. In this paper, we pro-pose EruDiff, which aims to refactor the knowledge within diffusionmodels. Specifically, we develop the Diffusion Knowledge DistributionMatching (DK-DM) to register the knowledge distribution of intractableimplicit prompts with that of well-defined explicit anchors. Furthermore,to rectify the inherent biases in explicit prompt rendering, we employthe Negative-Only Reinforcement Learning (NO-RL) strategy for fine-grained correction. Rigorous empirical evaluations demonstrate that ourmethod significantly enhances the performance of leading diffusion mod-els, including FLUX and Qwen-Image, across both the scientific knowl-edge benchmark (i.e., Science-T2I) and the world knowledge bench-mark (i.e., WISE), underscoring the effectiveness and generalizability.Our code is available at https://github.com/xiefan-guo/erudiff.

ECCV 2026

EoS-FM: Can an Ensemble of Specialist Models act as a Generalist Feature Extractor?

Pierre Adorni, Minh Tan Pham, Stephane May, Sébastien Lefèvre

Recent advances in foundation models have shown greatpromise in domains such as natural language processing and computervision, and similar efforts are now emerging in the Earth Observationcommunity. These models aim to generalize across tasks with limitedsupervision, reducing the need for training separate models for eachtask. However, current strategies, which largely focus on scaling modelsize and dataset volume, require prohibitive computational and data re-sources, limiting accessibility to only a few large institutions. Moreover,this paradigm of ever-larger models stands in stark contrast with theprinciples of sustainable and environmentally responsible AI, as it leadsto immense carbon footprints and resource inefficiency. In this work,we present a novel and efficient alternative: EoS-FM1 , an Ensemble-of-Specialists framework for building Remote Sensing Foundation Models(RSFMs). Our method decomposes the pre-training process into train-ing lightweight, task-specific ConvNeXtV2 specialists that can be frozenand reused. This modular approach offers strong advantages in efficiency,interpretability, and extensibility. Moreover, it naturally supports feder-ated training, pruning, and continuous specialist integration, making itparticularly well-suited for collaborative and resource-constrained set-tings. Our framework sets a new direction for building scalable and effi-cient RSFMs.

ECCV 2026

EGGS: Explicitly Granular 3D Gaussian Splatting via Luma-Aware and Volume-Preserving Attribute Factorization

InGyu Jeong, Hyunmin Jung

3D Gaussian Splatting has revolutionized the field of novelview synthesis by enabling high-fidelity and real-time rendering. How-ever, its explicit representation requires millions of primitives, result-ing in massive storage overheads that hinder scalable deployment. Toaddress this challenge, we propose Explicitly Granular Gaussian Splat-ting (EGGS), a novel representation and compression framework thatachieves an ultra-compact storage footprint without compromising vi-sual quality or rendering speed. Our method is built upon two key in-novations designed to minimize attribute dimensionality. First, we intro-duce Luma-based DC Representation, which leverages the dominance ofluminance information to compress the 3-dimensional DC component ofSpherical Harmonics into a single explicit dimension. Second, we proposeVolume-Preserving Scale Factorization, which reduces the 3-dimensionalscale attribute to a single explicit dimension while ensuring training sta-bility by maintaining consistent volume. By combining these techniqueswith a lightweight neural field for implicit attribute estimation, EGGSachieves a remarkably compact representation with the smallest storagefootprint among existing methods. Extensive experiments on the Mip-NeRF360, Tanks&Temples, and Deep Blending datasets demonstratethat EGGS significantly outperforms state-of-the-art methods in rate-distortion efficiency, reducing gigabyte-scale scenes to a mere handful ofmegabytes while maintaining high-quality real-time rendering. Code isavailable here.

ECCV 2026

EchoVLA: Robotic Vision-Language-Action Model with Synergistic Declarative Memory for Mobile Manipulation

Min Lin, Xiwen Liang, Bingqian Lin, Jingzhi Liu, Zijian Jiao, Kehan Li, Ziang Yan, Yu Sun, Weijia Liufu, Yuhan Ma, Jiarui Hu, Yuecheng Liu, Shen Zhao, Yuzheng Zhuang, Xiaodan Liang

Recent progress in Vision–Language–Action (VLA) modelshas enabled embodied agents to interpret multimodal instructions andperform complex tasks. However, existing VLAs are mostly confined toshort-horizon, table-top manipulation, lacking the memory and reasoningcapability required for mobile manipulation, where agents must coordi-nate navigation and manipulation under changing spatial contexts. Inthis work, we present EchoVLA, a memory-aware VLA model for mobilemanipulation. EchoVLA incorporates a synergistic declarative memoryinspired by the human brain, consisting of a scene memory that main-tains a collection of spatial–semantic maps and an episodic memory thatstores task-level experiences with multimodal contextual features. Thetwo memories are individually stored, updated, and retrieved based oncurrent observations, task history, and instructions, and their retrievedrepresentations are fused via coarse- and fine-grained attention to guidebase–arm diffusion policies. To support large-scale training, we furtherintroduce MoMani, an automated benchmark that generates expert-leveltrajectories through multimodal large language model (MLLM)–guidedplanning and feedback-driven refinement, supplemented with real-robotdemonstrations. Comprehensive simulated and real-world results demon-strate that EchoVLA substantially improves overall performance, e.g., itachieves the highest success rates of 0.52 on manipulation/navigationtasks and 0.31 on mobile manipulation tasks in simulation, exceedingthe strong baseline π0.5 by +0.20 and +0.11, respectively.

ECCV 2026

Dual Distribution Estimation for Zero-shot Noisy Test-Time Adaptation with VLMs

Wenjie Zhu, Yabin ZHANG, Liang Xu, Xin Jin, Wenjun Zeng, Lei Zhang

While test-time adaptation (TTA) empowers vision-languagemodels to adapt without costly retraining, it remains highly vulnerable toout-of-distribution (OOD) outliers prevalent in real-world applications.This discrepancy motivates Noisy TTA (NTTA), an online task to fil-ter noisy OOD samples on the fly while maximizing in-distribution (ID)classification accuracy. Existing zero-shot NTTA approaches typicallyrely on test-time discriminative training, leading to overconfident mis-classifications and significantly degraded inference efficiency. To addressthese limitations, we propose a novel framework named Dual Distribu-tion Estimation (DDE), shifting the zero-shot NTTA paradigm frominstance-level learning to training-free Gaussian distribution modeling.DDE incorporates two novel modules: Positive Feature Distribution Es-timation (PFDE) and Negative Label Distribution Estimation (NLDE).PFDE explicitly models class-wise inclusion and exclusion Gaussian dis-tributions to formulate a calibrated contrastive score, robustly enhancingID accuracy. In parallel, NLDE improves OOD identification by explicitlymodeling the negative label distribution to mine highly discriminative la-bels, effectively mitigating spurious correlations. Extensive experimentsshow that on the large-scale ImageNet benchmark, DDE achieves an im-provement of 3.70% in harmonic mean accuracy and reduces the FPR95for OOD detection by 6.20%, while ensuring highly scalable and effi-cient online inference. Furthermore, DDE is zero-shot and training-free,demonstrating remarkable robustness in data-scarce scenarios. Codes areavailable at https://github.com/PolyU-VCLab/OpenOOD-VLM.

ECCV 2026

DPGS: A Diffusion-Prior Guided Framework for Large-Scale 3D Gaussian Splatting Reconstruction

Shidong Zhang, shuaixin li, Juntong Qi, Haoxin Zhang, Xiao Zhang, Xiaozhou Zhu, Wen Yao

High-fidelity reconstruction of large-scale aerial scenes using3DGS confronts dual challenges: restricted nadir views result in facade"blind spots" and geometric collapse in textureless regions, while themassive scale of high-resolution data induces severe computational loadimbalance during parallel training. In this paper, we propose DPGS, aholistic framework that orchestrates visual foundation model priors andgenerative diffusion priors to achieve robust reconstruction. First, we in-troduce a foundation model-driven pixel-wise dense initialization to mit-igate geometric incompleteness from sparse SfM points, effectively pre-venting floating artifacts in weak-texture areas. Second, to address com-putational bottlenecks, we propose an observation density balanced par-titioning method. By formulating a graph partitioning problem weightedby feature track lengths and observation frequency, we dynamically bal-ance the rendering load across sub-blocks. Finally, we devise a diffusion-prior guided geometry completion mechanism. Leveraging a 3D-awarediffusion model with spiraling side-view sampling, we recover texturesfor unobserved facade blind spots. DPGS achieves state-of-the-art ren-dering quality, geometric completeness, and efficiency across standardbenchmarks and a self-collected dataset. Project page: https://zsddd.github.io/dpgs.

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