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ECCV 2026

DroneFINE: Domain-Aware Parameter-Efficient Fine-Tuning of Vision-Language Detectors for Drone Images

Wu Ke, Yanan Zhang, Yingjie Gao, Wenhao Li, Chenyu Zhou, Xinzhu Ma, Jiaxin Chen, Di Huang

Object detection for Unmanned Aerial Vehicles (UAVs) work-ing in open and dynamic environments is a highly challenging task.While Vision-Language Models (VLMs) have offered a powerful solutionfor universal object detection, adapting them to UAV scenarios remainsnon-trivial due to a substantial domain gap between VLM pre-trainingdata and aerial imagery. The prevailing Parameter-Efficient Fine-Tuning(PEFT) methods prove ineffective in bridging this gap, as VLMs’ “natural-scene, foreground-dominant” visual priors misalign with the “bird’s-eye-view, background-dominant, small-object” characteristics of UAV data.To address this issue, we propose DroneFINE, a novel PEFT paradigmcomprising two domain-aware complementary modules tailored for VLM-based drone image detectors. Specifically, a data-dependent, foreground-aware, and multi-path adaptation mechanism named HyperAdapter isdesigned, which overcomes the static structural constraints of PEFT. Inaddition, a background suppression algorithm named SemanticGate isdeveloped, which is a text-conditioned guidance strategy that employsbackground vocabulary to actively guide the model in suppressing re-sponses from irrelevant regions. Extensive experiments on VisDrone andUAVDT demonstrate that DroneFINE significantly outperforms existingPEFT methods and achieves performance comparable to full fine-tuningwhile substantially reducing the fine-tuned parameters.

ECCV 2026

Drift-AR: Single-Step Visual Autoregressive Generation via Anti-Symmetric Drifting

Zhen Zou, Xiaoxiao Ma, Mingde Yao, Jie Huang, Linjiang Huang, Feng Zhao

Autoregressive (AR)-Di_x001B_usion hybrid paradigms combine AR'sstructured semantic modeling with di_x001B_usion's high-_x001C_delity synthesis, yetsu_x001B_er from a dual speed bottleneck: the sequential AR stage and theiterative multi-step denoising of the di_x001B_usion vision decode stage. Exist-ing methods address each in isolation without a uni_x001C_ed principle design.We observe that the per-position prediction entropy of continuous-spaceAR models naturally encodes spatially varying generation uncertainty,which simultaneously governing draft prediction quality in the AR stageand re_x001D_ecting the corrective e_x001B_ort required by vision decoding stage,which is not fully explored before. Since entropy is inherently tied toboth bottlenecks, it serves as a natural unifying signal for joint accel-eration. In this work, we propose Drift-AR, which leverages entropysignal to accelerate both stages: 1) for AR acceleration, we introduceEntropy-Informed Speculative Decoding that align draft_x0015_target entropydistributions via a causal-normalized entropy loss, resolving the entropymismatch that causes excessive draft rejection; 2) for visual decoder ac-celeration, we reinterpret entropy as the physical variance of the initialstate for an anti-symmetric drifting _x001C_eld_x0016_high-entropy positions acti-vate stronger drift toward the data manifold while low-entropy positionsyield vanishing drift_x0016_enabling single-step (1-NFE) decoding without it-erative denoising or distillation. Moreover, both stages share the sameentropy signal, which is computed once with no extra cost. Experimentson MAR, TransDi_x001B_, and NextStep-1 demonstrate 3.8_x0015_5.5× speedup withgenuine 1-NFE decoding, matching or surpassing original quality. Codeis available at https://github.com/aSleepyTree/Drift-AR.

ECCV 2026

Domain Generalization via Text-Anchored Information Bottleneck

Eunyi Lyou, YUNJEONG CHOI, Junho Lee, Lee Joonseok

Visual recognition models often fail when deployed in newenvironments. Domain Generalization (DG) addresses this by learningrepresentations that remain invariant to environment-specific variations.Recent approaches increasingly rely on large vision-language models, as-suming that preserving their expressive visual representations improvesrobustness. However, we show that such visual expressiveness can insteadpropagate spurious cues that tie representations to the training environ-ments, hindering invariant learning. We therefore discard visual guidanceand instead treat the language embedding space as the primary sourceof domain invariance, naturally acting as an information bottleneck thatpreserves core semantics while suppressing domain-specific variations.Extensive experiments across diverse backbones exhibit state-of-the-artperformance and further analyze what makes guidance effective for ro-bust generalization. These findings shift the focus of DG from improvingrepresentations to designing supervision that enforces invariance.

ECCV 2026

Divide and Align: Disentangled Vision-Language Learning for Text-Based Person Anomaly Search

Alex Ergasti, Tomaso Fontanini, Claudio Ferrari, Massimo Bertozzi, Andrea Prati

Text-based person anomaly search (TPAS) refers to the taskof retrieving people exhibiting normal or anomalous behaviors from nat-ural language descriptions. Existing TPAS models often learn a singlejoint embedding where appearance, action, and background informationare entangled, causing over-reliance on identity cues, poor alignment foraction-centric queries, and limited semantic connection between actionsand places where they occur. To address these issues, we propose Se-mantic Decoupled Alignment (SeDA), a disentangled vision–languageretrieval framework that explicitly factorizes both visual and textualrepresentations into appearance, action, and background components.SeDA introduces Semantic Token Projection, which derives three se-mantic queries from the global [CLS] token, softly aggregates modal-ity tokens relevant to each factor, and recomposes the resulting factortokens into a compact retrieval embedding. To enforce factor-specific se-mantics, we decompose each caption into appearance/action/backgroundsub-captions and supervise the corresponding tokens with a Feature De-coupling Loss, combined with contrastive and image–text matching ob-jectives. On the Person Anomaly Benchmark (1M pairs), SeDA achieves86.45% R@1 (+1.52 over SOTA), improves average multi-weather R@1by +2.43, and gains +3.74 R@1 under out-of-distribution evaluation.https://github.com/ErgastiAlex/SeDA

ECCV 2026

DiffRGD: An Inference-Time Diffusion Guidance Through Riemannian Gradient Descent

Jia-Wei Liao, Li-Xuan Peng, Mei-Heng Yueh, Min Sun, Cheng-Fu Chou, Jun-Cheng Chen

Recently, diffusion models have been widely adopted in gen-erative modeling and have served as foundational models for many imagegeneration tasks. To control the generation without costly re-training orfine-tuning, many works seek inference-time guidance methods to steerthe latent via a differentiable objective at inference time. However, thesemethods cannot effectively preserve the original Gaussian distributionbecause they introduce distributional drift, thereby degrading the sam-ple quality. To address this gap, we propose DiffRGD, a distribution-aware guidance framework that explicitly preserves the latent Gaussianstructure. DiffRGD formulates each sampling step as a constrained op-timization problem on a spherical manifold induced by the latent Gaus-sian distribution, and solves it efficiently via Riemannian Gradient De-scent (RGD). DiffRGD is a plug-and-play method that can be seamlesslyintegrated into any pre-trained diffusion model. Extensive experimentsdemonstrate that DiffRGD outperforms previous methods in most im-age restoration and conditional generation tasks. Our project page isavailable at https://diffrgd.github.io/.

ECCV 2026

Delayed Bidirectional Alignment via Disentangled Audio Semantics for Audio-Visual Segmentation

Jingqi Tian, Yiheng Du, Haoji Zhang, Yuji Wang, Isaac Ning Lee, Xulong Bai, Tianrui Zhu, Jingxuan Niu, Yansong Tang

Audio–Visual Segmentation (AVS) aims to localize sound-producing objects at the pixel level by integrating auditory and visualcues. However, existing methods often struggle with multi-source en-tanglement and audio–visual misalignment, leading to a dominance biastoward acoustically or visually salient objects (i.e., louder or larger ones)at the expense of subtler or co-occurring sources. To address these chal-lenges, we propose DDAVS: Delayed Bidirectional Alignment via Dis-entangled Audio Semantics for Audio-Visual Segmentation. To mitigatemulti-source entanglement, DDAVS employs learnable queries to extractaudio semantics and anchor them within a structured semantic spacederived from an audio prototype memory bank. This process is furtheroptimized through contrastive learning to enhance discriminability androbustness. To alleviate audio–visual misalignment, DDAVS introducesdual cross-attention with delayed modality interaction, improving therobustness of multimodal alignment. Extensive experiments on the AVS-Objects and VPO benchmarks demonstrate that DDAVS achieves state-of-the-art performance across single-source, multi-source, and multi-classmulti-instance scenarios. These results validate the effectiveness and gen-eralization ability of our framework under challenging real-world au-dio–visual segmentation conditions. Project page.

ECCV 2026

DefenseSplat: Enhancing the Robustness of 3D Gaussian Splatting via Frequency-Aware Filtering

Yiran Qiao, Yiren Lu, Yunlai Zhou, Rui Yang, Linlin Hou, Yu Yin, Jing Ma

3D Gaussian Splatting (3DGS) has emerged as a powerfulparadigm for real-time and high-fidelity 3D reconstruction from posedimages. However, recent studies reveal its vulnerability to adversarialcorruptions in input views, where imperceptible yet consistent pertur-bations can drastically degrade rendering quality, increase training andrendering time, and inflate memory usage, even leading to server denial-of-service. In our work, to mitigate this issue, we begin by analyzingthe distinct behaviors of adversarial perturbations in the low- and high-frequency components of input images using wavelet transforms. Basedon this observation, we design a simple yet effective frequency-aware de-fense strategy that reconstructs training views by filtering high-frequencynoise while preserving low-frequency content. This approach effectivelysuppresses adversarial artifacts while maintaining the authenticity ofthe original scene. Notably, it does not significantly impair training onclean data, achieving a desirable trade-off between robustness and per-formance on clean inputs. Through extensive experiments under a widerange of attack intensities on multiple benchmarks, we demonstrate thatour method substantially enhances the robustness of 3DGS without ac-cess to clean ground-truth supervision. By highlighting and addressingthe overlooked vulnerabilities of 3D Gaussian Splatting, our work pavesthe way for more robust and secure 3D reconstructions.

ECCV 2026

Debiased Textual Prompt Tuning for Enhancing Unknown Class Discovery

Yuxin Fan, Junbiao Cui, Changhao Liu, Xingwang Zhao, Jiye Liang

Open-world semi-supervised learning (OWSSL) aims to rec-ognize known and unknown classes from unlabeled samples, where la-beled samples only include known classes. Recent studies introduce class-specific textual descriptions as semantic information and employ learn-able textual prompts to optimize these descriptions, thereby enhancingthe model’s ability to recognize classes. However, these methods oftenoverlook the model bias toward known classes during textual prompttuning, and establish only limited cross-modal connections, leading tosevere sparsity in the graph structure and hindering the propagation ofvisual and semantic information across the graph. These issues collec-tively limit the model’s ability to discover unknown classes, particularlyin scenarios with a large number of classes. To address these issues, wepropose a novel OWSSL method, which consists of two core components:(1) textual prompt optimization aimed at mitigating the model bias to-ward known classes, and (2) bimodal graph enhancement designed toalleviate the sparsity issue in the graph structure. Experimental resultsacross multiple datasets indicate that our method achieves an averageaccuracy 20.7% higher on unknown classes than state-of-the-art OWSSLmethods (relative improvement).

ECCV 2026

DCARL: A Divide-and-Conquer Framework for Autoregressive Long-Trajectory Video Generation

Junyi Ouyang, Wenbin Teng, Gonglin Chen, Yajie Zhao, Haiwei Chen

Long-trajectory video generation is a crucial yet challeng-ing task for world modeling, primarily due to the limited scalability ofexisting video diffusion models (VDMs). Autoregressive models, whileoffering infinite rollout, suffer from visual drift and poor controllability.To address these issues, we propose DCARL, a novel divide-and-conquer,autoregressive framework that effectively combines the structural stabil-ity of the divide-and-conquer scheme with the high-fidelity generationof VDMs. Our approach first employs a dedicated Keyframe Generatortrained without temporal compression to establish long-range, globallyconsistent structural anchors. Subsequently, an Interpolation Generatorsynthesizes the dense frames in an autoregressive manner with overlap-ping segments, utilizing the keyframes for global context and a singleclean preceding frame for local coherence. Trained on a large-scale inter-net long trajectory video dataset, our method achieves superior perfor-mance in both visual quality (lower FID and FVD) and camera adher-ence (lower ATE and ARE) compared to state-of-the-art autoregressiveand divide-and-conquer baselines, demonstrating stable and high-fidelitygeneration for long trajectory videos up to 32 seconds in length.

ECCV 2026

Cooking beyond Frames: A Stereo Event Camera Dataset in the Kitchen

Chengming Feng, Hesam Araghi, Liming Zheng, Julien Dupeyroux, Xucong Zhang, Jan van Gemert, Nergis Tomen

Event cameras, also known as neuromorphic cameras, havegained significant attention in recent years due to their high tempo-ral resolution, high dynamic range, and low power consumption. Whilemany studies and datasets in neuromorphic vision have focused on auto-motive and drone applications, human-centric daily-life scenarios remainlargely underrepresented, despite their importance for developing andbenchmarking event-based perception systems. Moreover, the few ex-isting event-based human activity datasets are typically recorded withscripted human actions, limiting their ability to capture natural hu-man behaviors. In this paper, we introduce EventKitchen, a large-scalestereo event camera benchmark dataset of human cooking activities inthe kitchen. EventKitchen is egocentrically collected from 10 participantsin 13 diverse kitchens, where the participants wear a helmet with multiplesensors and naturally perform cooking activities, without any scriptedactions. EventKitchen comprises 5.5 hours of stereo event recordingswith synchronized RGB, depth, and IMU data. We provide human an-notations for 10,762 action segments and 13,482 bounding boxes. Wetrain baseline models on EventKitchen to perform multiple event-basedtasks, including action recognition, object detection, and stereo depthestimation. By capturing natural, real-world human activities, Event-Kitchen establishes a challenging benchmark for neuromorphic visionbeyond autonomous driving. The dataset and toolkit are available athttps://chengmingf.github.io/EventKitchen.github.io/

ECCV 2026

Coarse-to-fine Contrast: A Hybrid Self-supervised Method for Non-rigid 3D Shape Matching

Feifan Luo, Ting Li, Zhao Li, Hongyang Chen

Non-rigid 3D shape matching is a fundamental task in com-puter vision and graphics. In this paper, we propose a hybrid self-supervisedmethod based on a coarse-to-fine strategy, which ensures consistencybetween the coarse mapping and the refined correspondence producedby our refinement module. The architecture features a dual-branch de-sign, consisting of two symmetric functional map learning streams: onebased on the Laplacian basis and the other utilizing the elastic ba-sis. Extensive experiments show that our approach not only maintainscomputational efficiency, but also achieves state-of-the-art performanceacross a variety of challenging scenarios, including non-isometric defor-mations and topological noise. Finally, we rigorously demonstrate thatcontrastive energies promote feature discrimination. Furthermore, inte-grating these energies with existing methods yields consistent improve-ments, validating the overall efficacy of our approach. Our code is avail-able at https://github.com/LuoFeifan77/Coarse-to-Fine-Hybrid-Self-Supervised-Matching.

ECCV 2026

BRepFacetGen: Reverse Engineering B-Reps By Generative Face Segmentation

Ka-Hei Hui, Vikas Thamizharasan, Pradeep Kumar Jayaraman, Xiang Xu

Boundary representations (B-Reps) are fundamental to com-puter aided design (CAD), as they encode editable parametric surfacestogether with the precise topological structure required by downstreamengineering workflows. In contrast, modern 3D generative models pro-duce meshes or implicit fields, leaving a gap between high-quality geom-etry synthesis and CAD-native representations. Existing reconstructionpipelines treat B-Rep recovery as deterministic segmentation and fit-ting on discrete samples, despite the inherent ambiguity of CAD-styleface decompositions, often resulting in unstable topology recovery. Weintroduce BRepFacetGen, a generative framework that formulates CADstructure inference as a latent variable problem conditioned on geometryrepresenting continuous surfaces. Our representation couples a pretrainedgeometry latent set with a geometry-conditioned label latent space thatmodels multiple plausible dense surface segmentations, naturally han-dling permutation ambiguity and structural non-uniqueness. Joint de-coding yields a faceted mesh where each segment maps to a B-Rep facethat can be recovered by surface fitting and topology reconstruction. Ex-periments show improved segmentation and more reliable downstreamB-Rep recovery, as well as strong performance in image-conditioned andpoint-conditioned B-Rep generation.

ECCV 2026

Beyond Isolated Objects: Relationship-aware Open Vocabulary Scene Understanding via 3D Scene Graph Analysis

Xianhao Chen, Jiarui Hu, Yuanbo Yang, Xiyu Zhang, Tengyue Wang, Hujun Bao, Guofeng Zhang, Zhaopeng Cui

Open-vocabulary 3D scene understanding aims to segment3D scenes beyond predefined categories by transferring semantic knowl-edge from vision-language models. Existing methods have advanced thistask by lifting language-aligned 2D features into 3D, yet they oftenrely on context-independent semantic representations, leaving object re-lationships underexplored for contextual refinement. We propose Rel-GraphOV, a relationship-aware framework that uses 3D scene graphs toenhance open-vocabulary 3D understanding. Our method constructs re-lational scene graphs from multi-view observations by leveraging vision-language reasoning to infer object relationships and prune geometricallyimplausible connections, without manual relationship annotations. Toaggregate relational context while avoiding feature interference, we in-troduce an Adaptive Gated Dual-Stream Contextual GAT that separatesdense geometric features and semantic CLIP embeddings, performs edge-guided message passing, and adaptively fuses complementary semantics.A hierarchical contrastive objective further promotes instance-level con-sistency and category-level discrimination. Experiments on ScanNetV2,ScanNet200, ScanNet++, and Replica demonstrate strong performanceand generalization ability. Project Page: cxavireh.github.io/relgraphov-projectpage

ECCV 2026

AnyGround3D: Towards Grounding Any 3D Object in the Wild via 2D-to-3D Lifting

Yingping Liang, Wenxuan Guo

3D visual grounding aims to localize language-referred ob-jects in 3D scenes. However, existing methods rely on limited annotateddatasets under controlled environments and thus struggle to generalizein the wild. Therefore, the scarcity of scalable 3D grounding supervisionremains a major challenge. In this paper, we propose AnyGround3D,a scalable framework that learns to ground arbitrary 3D objects in thewild via 2D-to-3D lifting. Instead of requiring manual 3D annotations,our approach lifts diverse Internet images into structured 3D supervisionthrough multi-level 2D-to-3D lifting. First, we perform scene-level 2D-to-3D lifting to reconstruct metric point clouds from monocular images,providing geometric context. Second, we conduct object-level 2D-to-3Dlifting to recover complete 3D object representations with fine-gained 3Dbounding boxes and neural language descriptions. To further enhancethe representation robustness and open-vocabulary generalization of thepoint encoder, we introduce feature-level lifting that distills semantic rep-resentations from 2D foundation models into the 3D encoder. Together,these lifting processes enable large-scale synthetic 3D grounding supervi-sion across diverse real-world scenes. Extensive experiments demonstrateimproved in-the-wild generalization across multiple 3D grounding bench-marks, without relying on human-annotated 3D grounding data.

ECCV 2026

Ada-VNNs: Adaptive Equivariance for Vector Neural Networks

Bing Han, Ruitao Pan, Yumin Chen, Peixin Hong, Weiyuan Liu, Zhibin Zhao, Chenxi Wang, Zhi Zhai

Vector Neuron Networks (VNNs) are widely used in 3D tasksfor their data efficiency and strong generalization from equivariance.However, their rigid equivariance constraints hinder handling symmetry-breaking, where low-symmetry outputs must be inferred from highlysymmetric inputs. In this paper, we reveal the representation collapse is-sue in VNNs and propose Ada-VNNs, a vector-neuron architecture thatcan adaptively relax equivariance constraints according to the symmetrylevel of the data. Ada-VNNs introduce an Adaptive Gating Unit (AGU)to control equivariance relaxation in linear layers, and design a resid-ual pathway that transforms hard structural constraints into adjustablesoft priors. We further provide a theoretical characterization that linksthe learned equivariance behavior to intrinsic symmetry breaking in thedata. Experiments on pose estimation across 26 categories with differ-ent degrees of symmetry breaking demonstrate that Ada-VNNs enabledata-driven adaptive equivariance adjustment, alleviate representationcollapse, and significantly improve symmetry-breaking capability overVNN and VN-Transformer, achieving higher performance with almostno additional overhead.

ECCV 2026

3D Field of Junctions: A Noise-Robust, Training-Free Structural Prior for Volumetric Inverse Problems

Namhoon Kim, NARGES MOEINI, Justin Romberg, Sara Fridovich-Keil

Volume denoising is a foundational problem in computa-tional imaging, as many 3D imaging inverse problems face high levels ofmeasurement noise. Inspired by the strong 2D image denoising propertiesof Field of Junctions (ICCV 2021), we propose a novel, fully volumetric3D Field of Junctions (3D FoJ) representation that optimizes a junctionof 3D wedges that best explain each 3D patch of a full volume, while en-couraging consistency between overlapping patches. In addition to directvolume denoising, we leverage our 3D FoJ representation as a structuralprior that: (i) requires no training data, and thus precludes the risk of hal-lucination, (ii) preserves and enhances sharp edge and corner structuresin 3D, even under low signal to noise ratio (SNR), and (iii) can be used asa drop-in denoising representation via projected or proximal gradient de-scent for any volumetric inverse problem with low SNR. We demonstratesuccessful volume reconstruction and denoising with 3D FoJ across threediverse 3D imaging tasks with low-SNR measurements: low-dose X-raycomputed tomography (CT), cryogenic electron tomography (cryo-ET),and denoising point clouds such as those from lidar in adverse weather.Across these challenging low-SNR volumetric imaging problems, 3D FoJoutperforms the evaluated classical denoisers, untrained neural denois-ers, and denoisers trained only on noisy examples. Code is available athttps://github.com/voilalab/3D-Field-of-Junctions.

ECCV 2026

Who Does What and Where to Go: Orthogonal Alignment and Hierarchical Planning for Multi-Entity Trajectories

Zhang Wan, Yu Li, Tianze Huang, Juan Cao, Sheng Tang

Generating precise multi-entity 3D trajectories is fundamen-tal for interactive video generation and embodied world models. However,existing text-to-trajectory mappings face two bottlenecks: severe featureleakage from coupled multi-modal inputs, and physical collisions causedby blind end-to-end 3D coordinate regression. We propose ProgTraj-Director, a framework driven by physical property disentanglement andhierarchical spatial planning. To resolve the “who does what” attribu-tion ambiguity, our Structured Vision-Language Alignment (StructVLA)module disentangles static identity masks from dynamic spatial motionflows, binding them via orthogonal factorized tensor fusion in the latentspace. This formulation encourages entity-specific semantic separationand mitigates cross-entity feature leakage. To bridge the “where to go”semantic-physical gap, we formulate trajectory generation as hierarchicalspatial planning. The model first constructs a Geometry-Aware Bird’s-EyeView (BEV) to resolve anti-collision topological boundaries. Guided bythis structural prior, 3D trajectories are derived via progressive spatialdenoising to improve geometric and temporal consistency. To supportthis research, we introduce Stepwise-ME, a large-scale dataset of over31,000 interactive video clips (8M frames) with fine-grained stepwiseannotations. Extensive experiments show that our approach enhancesaction attribution and spatial coherence, providing a reliable structuralprior for downstream video generation. Code and dataset are available athttps://github.com/wzbos-token/Who-Does-What-and-Where-to-Go.

ECCV 2026

Which Layer Causes Distribution Deviation? Entropy-Guided Adaptive Pruning for Diffusion and Flow Models

Changlin Li, Jiawei Zhang, Zeyi Shi, Zhihui Li, Xiaojun Chang

Large-scale vision generative models, including diffusion andflow models, have demonstrated remarkable performance in visual gen-eration tasks. However, transferring these pre-trained models to down-stream tasks often results in significant parameter redundancy. In thispaper, we propose EntPruner, an entropy-guided automatic progressivepruning framework for diffusion and flow models. First, we introduceentropy-guided pruning, a block-level importance assessment strategyspecifically designed for generative models. Unlike discriminative models,generative models require preserving the diversity and condition-fidelityof the output distribution. As the importance of each module can varysignificantly across downstream tasks, EntPruner prioritizes pruning ofless important blocks using data-dependent Conditional Entropy Devia-tion (CED) metric. CED quantifies how much the distribution divergesfrom the learned conditional data distribution after removing a block.Second, we propose a zero-shot adaptive pruning framework to auto-matically determine when and how much to prune during training. Thisdynamic strategy avoids the pitfalls of one-shot pruning, mitigating modecollapse, and preserving model performance. Extensive experiments onDiT and SiT models demonstrate the effectiveness of EntPruner, achiev-ing up to 2.22× inference speedup while maintaining competitive gener-ation quality on ImageNet and three downstream datasets.

ECCV 2026

WearWow: Native 2K Multi-Garment Virtual Try-On via Adaptive Token Packing and Preference Alignment

Xujie Zhang, Runyan Du, Song Chang, Jiang Li, Dongliang Shao, Liping Wu, Luo Wei, Xiaochao Qu, Luoqi Liu, Xiaodan Liang

Synthesizing native 2K multi-garment virtual try-on is aformidable frontier in digital fashion, critically bottlenecked by two fun-damental limitations: the O(N 2 ) memory explosion induced by 2k con-ditions, and the spectral bias of diffusion models that over-smooths high-frequency fabric details. We present WearWow, an end-to-end, mask-freegenerative framework that pioneers ultra-high-resolution multi-garmentsynthesis. To mitigate the memory explosion , we propose Adaptive 2DToken Packing (ATP). ATP leverages inherent garment sparsity to al-gorithmically pack heterogeneous items onto a unified 2D canvas andprune uninformative background tokens, minimizing the effective se-quence length and subsequent memory overhead while rigorously pre-serving 2D spatial priors. To rectify texture degradation, we introducethe Multi-dimensional Try-on Reward (MTR) system. MTR synergizes aSemantic Guidance Reward to explicitly drive tactile restoration with aCloth Distribution Reward to implicitly anchor the physical distribution,a joint formulation that effectively mitigates the severe reward hacking.Furthermore, we curate WearWow-2K, an extreme-quality dataset com-prising native 2K triplets, providing physically correct spatial interac-tions that naturally empower the model’s mask-free generation. Exten-sive experiments demonstrate that WearWow establishes a new state-of-the-art, exceeding existing commercial baselines in native 2K multi-garment synthesis.

ECCV 2026

VICAL: Vicinal Consistency Alignment for Long-Tailed Visual Recognition

Jianggang Zhu, Zheng Wang, Bin Zhu, Yi-Ping Phoebe Chen, Jingjing Chen

Multi-expert models have become the dominant paradigmfor long-tailed learning, largely attributed to their presumed ability tobenefit from expert diversity. However, we revisit this central assump-tion and reveal that diversity induced by logit adjustment or explicitregularizers does not guarantee better ensemble accuracy. Our work sug-gests that multi-expert models benefit more from variance reduction thandiversity maximization. We introduce VICAL, a VIcinal ConsistencyALignment framework that improves long-tailed recognition not by en-forcing expert diversity, but by reducing prediction variance. Specifically,our approach comprises two key components: Self-Consistency Learn-ing and Deep Ensemble Distillation. Self-Consistency Learning discour-ages reliance on unstable high-frequency information, smoothing the lo-cal loss landscape and mitigating overfitting, especially for tail classes.Deep Ensemble Distillation promotes cross-expert low-frequency seman-tic agreement using a low-resolution view, thereby sidestepping opti-mization conflicts with established knowledge. Extensive experiments onCIFAR-LT, ImageNet-LT, and iNaturalist 2018 show that VICAL consis-tently outperforms state-of-the-art methods, validating the effectivenessof our consistency-driven design.

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