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ECCV 2026

AdaBoosting Text Prompts for Vision-Language Models

Seokhee Jin, Changhwan Sung, Sunung Mun, Hoyoung Kim, Jungseul Ok

The classification accuracy of pretrained Vision-Language Models (VLMs) relies on the quality of the text prompts. Handcrafted templates and Large Language Model (LLM)-generated descriptions not only make predictions more interpretable, but also enable reuse of the same prompts across heterogeneous VLMs. Recent works construct taskadapted text prompts with a small number of labeled images. However, existing few-shot text prompting methods do not explicitly focus on misclassified examples during prompt construction, leading to only marginal improvements even as more shots become available. To fully exploit few-shot supervision, we propose Text Prompt Boosting (TPB), an AdaBoost-inspired framework that treats each text-prompt-based classifier as a weak learner and sequentially aggregates them into a strong ensemble by explicitly targeting hard, misclassified examples. Extensive experiments show that TPB preserves task-intrinsic, model-agnostic cues in text space, enabling robust cross-model transfer. Across eleven classification benchmarks, TPB improves accuracy on the source model and preserves shot-driven gains when transferred to larger, more capable VLMs, where existing methods struggle to sustain such improvements.

ECCV 2026

VolSplat: Rethinking Feed-Forward 3D Gaussian Splatting with Voxel-Aligned Prediction

Weijie Wang, Yeqing Chen, Zeyu Zhang, Hengyu Liu, Haoxiao Wang, ZhiYuan Feng, Wenkang Qin, Feng Chen, JiaWang Bian, Zheng Zhu, Donny Y. Chen, Bohan Zhuang

Feed-forward 3D Gaussian Splatting (3DGS) has emergedas a highly effective solution for novel view synthesis. Existing meth-ods predominantly rely on a pixel-aligned Gaussian prediction paradigm,where each 2D pixel is mapped to a 3D Gaussian. We rethink this widelyadopted formulation and identify several inherent limitations: it rendersthe reconstructed 3D models heavily dependent on the number of inputviews, leads to view-biased density distributions, and introduces align-ment errors, particularly when source views contain occlusions or lowtexture. To address these challenges, we introduce VolSplat, a new multi-view feed-forward paradigm that replaces pixel alignment with voxel-aligned Gaussians. By directly predicting Gaussians from a predicted3D voxel grid, it overcomes pixel alignment’s reliance on error-prone 2Dfeature matching, ensuring robust multi-view consistency. Furthermore,it enables adaptive control over density based on 3D scene complex-ity, yielding more faithful Gaussians, improved geometric consistency,and enhanced novel-view rendering quality. Experiments on widely usedbenchmarks demonstrate that VolSplat achieves state-of-the-art perfor-mance, while producing more plausible and view-consistent results.

ECCV 2026

Zero-Shot Image Personalization from Personas

Harini S I, Somesh Singh, Yaman K Singla, David Doermann, Rajiv Shah

Text-to-image diffusion models are increasingly deployed increative contexts, yet remain impersonal, optimized for aggregate aes-thetics rather than individual taste. Human preferences are inherentlypluralistic: one user who favors muted, nostalgic portraits may prefer vi-brant compositions for street photography, while another gravitates to-ward dreamy, overexposed film aesthetics. Existing personalization meth-ods require dense interaction histories or per-user fine-tuning, failing incold-start settings and collapsing each user’s context-dependent prefer-ences into a single static representation. We introduce zero-shot imagepersonalization from personas (ZIPP), a paradigm that conditions im-age generation on natural-language personas (concise descriptors of auser’s identity, interests, and aesthetic sensibilities) without any user-specific data or weight updates. ZIPP uses an LLM in a roleplay set-ting to rewrite input prompts from the perspective of a given persona,steering diffusion models toward personalized outputs. To mine per-sonas at scale, we develop an inductive Graph Attention Network over a23M-user Reddit interaction graph with dual contrastive objectives thatalign graph structure with users’ visual behavior, and verbalize learnedrepresentations into coherent natural-language personas via an MLLM.We further introduce ZIP-Bench the first zero-shot image personal-ization benchmark, pairing 1.5K users with graph-mined personas and40K generated images. Across four benchmarks and 14 LLMs spanningfive model families, persona conditioning yields consistent improvementsin both zero-shot and few-shot settings, with frontier models achievingthe strongest gains (13–20%). In the few-shot setting, ZIPP matchesor exceeds fine-tuned baselines requiring per-user adapters trained on100+ examples. Unlike baselines that collapse preferences into a fixedstyle, ZIPP preserves intra-user preference diversity, achieving the low-est distributional divergence from users’ true preference distributions(CMMD 0.16 vs. 0.55 for fine-tuned alternatives). IPF-normalized eval-uation against global population demographics further reveals that ex-isting methods exhibit substantial bias toward narrow subpopulations,which persona conditioning significantly mitigates. A human evaluationconfirms these findings: ZIPP achieves a 79% win rate over genericgeneration and outperforms all fine-tuned baselines (58–65% win rate)without any user-specific training. We release ZIP-Bench at: https://huggingface.co/datasets/behavior-in-the-wild/zip-bench

ECCV 2026

Teaching an Agent to Sketch One Part at a Time

Xiaodan Du, Ruize Xu, David Yunis, Yael Vinker, Greg Shakhnarovich

We develop a method for producing vector sketches one partat a time. To do this, we train a multi-modal language model-based agentusing a novel multi-turn process-reward reinforcement learning follow-ing supervised fine-tuning. Our approach is enabled by a new datasetwe call ControlSketch-Part, containing rich part-level annotations forsketches, obtained using a novel, generic automatic annotation pipelinethat segments vector sketches into semantic parts and assigns paths toparts with a structured multi-stage labeling process. Our results indicatethat incorporating structured part-level data and providing agent withthe visual feedback through the process enables interpretable, control-lable, and locally editable text-to-vector sketch generation. Project page:https://xiaodan.io/teaching-an-agent-to-sketch/

ECCV 2026

Falcon: Functional Assembly and Language for Compositional Reasoning in X-ray

Yonathan Michael, Mohamad Alansari, Natnael Takele, Andreas Henschel, Naoufel Werghi

Conventional vision-language models are largely object-centric, focusing on detecting and describing individual entities. In safety-critical X-ray baggage screening, however, threat often emerges not from a single object but from the functional compatibility of spatially dispersed components, such as batteries, detonators, and explosive charges. We formalize this setting as compositional threat reasoning, where risk is modeled as a relational property of grounded regions rather than an independent detection outcome. We introduce Falcon, a multimodal framework that abstracts segmentation-aware region features into a structured safety state capturing component presence, pairwise functional compatibility, and scene-level risk. This structured representation is injected into the language model as an explicit intermediate interface, encouraging relationally consistent and safety-aware reasoning. To evaluate this problem, we present Falcon-X, a benchmark that unifies dense grounding with structured supervision over component completeness and risk inference in cluttered X-ray imagery. Experiments show that while existing multimodal models adapt to appearance, they struggle with compositional safety reasoning. Falcon improves functional grounding and produces more coherent threat assessments, establishing compositional safety reasoning as a distinct evaluation paradigm for multimodal systems.

ECCV 2026

Iterative Perceptual Alignment for VLMs via Deterministic Reconstruction Feedback

Xiaorui Chen, Hanzhong Guo, Nizhe Cai, Jieliang Luo

While high-quality preference data is vital for fine-grainedvision-language alignment, its creation remains bottlenecked by the highcost and subjective noise of human annotation. We propose a self-supervisedframework that derives solid, highly discriminative supervisory signalsthrough deterministic reconstruction feedback. By transforming abstractsemantic matching into concrete visual reconstruction, our approach pro-vides reliable consistency signals that overcome the input limits of tra-ditional models. A reward model trained on our generated data achievescompetitive accuracy across multiple zero-shot preference benchmarksusing only 20% of the typical training data volume. Furthermore, fine-tuning VLMs via Direct Preference Optimization (DPO) based on thisdata significantly improves performance in handling complex, high-densitydescriptions. Our results demonstrate the potential of reconstruction-driven self-correction for low-cost, high-precision alignment of multi-modal models. Our code of the scoring model is released at https://github.com/XC0053/iterative-perceptual-alignment.

ECCV 2026

Focusing by Contrastive Attention: Enhancing VLMs' Visual Reasoning

Yuyao Ge, Shenghua Liu, Yiwei Wang, Lingrui Mei, Baolong Bi, Xuanshan Zhou, Jiayu Yao, Jiafeng Guo, Xueqi Cheng

Vision-Language Models (VLMs) have demonstrated remark-able success across diverse visual tasks, yet their performance degradesin complex visual environments. Existing enhancement approaches re-quire additional training, rely on external segmentation tools, or operateat coarse-grained levels, overlooking VLMs’ innate attention capabilities.To bridge this gap, we investigate VLMs’ attention patterns and discoverthat: (1) visual complexity strongly correlates with attention entropy,negatively impacting reasoning performance; (2) attention progressivelyrefines from global scanning in shallow layers to focused convergencein deeper layers, with the degree of convergence determined by visualcomplexity; (3) theoretically, under a multiplicative decomposition as-sumption, we show that contrasting attention maps between general andtask-specific queries approximately separates visual signal into seman-tic and visual noise components. Building on these insights, we proposeContrastive Attention Refinement for Visual Enhancement (CARVE),a training-free method that extracts task-relevant visual signals throughattention contrasting at the pixel level. Experiments on seven bench-marks show that CARVE improves performance on visual perceptiontasks, with notable gains on both recent and earlier-generation models.Our analysis reveals how visual complexity affects attention mechanismsand demonstrates an effective strategy for improving visual reasoningthrough attention contrasting.

ECCV 2026

FreqOrtho-SR: Frequency-Guided Orthogonal Expert Learning for Real-World Image Super-Resolution

Minh Son Hoang, Dinh Tran, Quyen Nguyen Duc, Phuong Dam, Daeyoung Kim

Diffusion prior-based methods have shown impressive resultsin real-world image super-resolution (ISR), yet two key challenges per-sist: balancing pixel-level fidelity with semantic quality, and adapting todiverse degradations. Existing dual-branch approaches freeze the pixelmodule during semantic training, but the semantic branch can still ex-pand capacity within the pixel subspace, precluding genuine perceptualimprovement. Moreover, using a single static adapter cannot generalizeacross heterogeneous real-world corruptions. To address both issues, wepropose FreqOrtho-SR, which comprises: Frequency-guided Mixture ofLoRA Experts (FreqMoE), it routes inputs to specialized experts via anon-parametric FFT-based degradation-feature extractor that encodesfrequency-domain signatures, enabling stable and interpretable special-ization across corruption types; and Orthogonal Gradient Projection(OGP), which reframes the dual-objective optimization as a subspace-constrained problem: by extracting the pixel-fidelity subspace via SVDon combined expert weight deltas and projecting semantic gradients ontoits null space, OGP guarantees orthogonality between the two objectives,enabling genuinely complementary learning without mutual interference.Experiments show that FreqOrtho-SR achieves competitive overall per-formance and a strong fidelity-perception trade-off across multiple bench-marks with efficient single-step inference. The source code of our methodcan be found at sonhm3029/FreqOrtho-SR.

ECCV 2026

DA-MergeLoRA: Hypernetwork-Based LoRA Merging for Few-Shot Test-Time Domain Adaptation

Siobhan Reid, Zhixiang Chi, Li Gu, Omid Heidari, Ziqiang Wang, Yang Wang

Few-shot Test-Time Domain Adaptation (FSTT-DA) seeksto adapt models to novel domains using only a handful of unlabeledtarget samples. This setting is more realistic than typical domain adap-tation setups, which assume access to target data during source training.However, prior FSTT-DA approaches fail to effectively leverage sourcedomain-specific knowledge, relying on shallow batch normalization up-dates, prompt-based methods that treat the model as a black box, orensembling strategies that do not capture cross-domain relationships. Toaddress these limitations, we introduce a new FSTT-DA framework thatintegrates LoRA fine-tuning with model merging. In our approach, sep-arate LoRA modules are fine-tuned on CLIP’s vision encoder for eachsource domain. Since LoRA modifies only a small fraction of the model’sparameters, it retains the base model’s generalized knowledge while in-ternally learning domain-specific features. To adapt the learned knowl-edge to a specific target domain, we propose a hypernetwork trainedvia meta-learning that generates per-column merging factors to combineLoRA modules. Given a small batch of target images, the hypernetworkproduces merging weights that fuse source LoRA modules into a singleadapted representation. Our results demonstrate state-of-the-art perfor-mance across various domain adaptation datasets. Our code is publiclyavailable at https://github.com/nahbois4321/DA-MergeLoRA.

ECCV 2026

REVA-PO: Stabilizing Reinforcement Learning for Chest X-ray Report Generation

Li Guo, Anas Tahir, Z. Jane Wang

Automated chest X-ray report generation has recently bene-fited from reinforcement learning (RL) and large language models. How-ever, RL training often suffers from instability or limited explorationdue to fixed Kullback-Leibler (KL) regularization and a static referencepolicy that accumulates KL pressure over time. We propose Response-Weighted and Validation-Anchored Policy Optimization (REVA-PO), aRL framework that stabilizes long-term training via Response-WeightedRegularization (RER) and Validation-Anchored Policy Reset (VAPR).RER dynamically adjusts per-response KL weights based on advantageand reference-policy entropy, relaxing constraints for high-quality re-sponses while tightening them for low-quality ones. Complementarily,VAPR periodically synchronizes the reference and current policies tothe best validation checkpoint, resetting accumulated regularization pres-sure to expand the viable exploration space. To ensure a robust startingpoint, we employ a three-stage pipeline consisting of warm-up training,classifier-guided supervised fine-tuning, and RL. Extensive evaluationson MIMIC-CXR and IU-Xray demonstrate that REVA-PO sets newstate-of-the-art benchmarks in both linguistic quality and clinical accu-racy. Notably, BLEU-4 improves by 5.1% on MIMIC-CXR and 3.6% onIU-Xray, while CheXpert F1 and RadGraph F1 scores increase by 4.5%and 12.8%, respectively, over prior leading methods. The code is publiclyavailable at https://github.com/LiGuo12/REVA_PO/.

ECCV 2026

SelfMOTR: Revisiting MOTR with Self-Generating Detection Priors

Fabian Gülhan, Emil Mededovic, Yuli Wu, Johannes Stegmaier

End-to-end transformer architectures have driven significant progress in multi-object tracking by unifying detection and association into a single, heuristic-free framework. Despite these benefits, poor detection performance and the inherent conflict between detection and association in a joint architecture remain critical concerns. Recent approaches aim to mitigate these issues by employing advanced denoising or label assignment strategies, or by incorporating detection priors from external object detectors. In this paper, we propose SelfMOTR, a simple yet highly effective detector-free alternative that decouples proposal discovery from association using self-generated internal detection priors. Through extensive analysis and ablation studies, we show that end-to-end transformer trackers with joint detection–association decoding retain substantial hidden detection capacity, and we provide a practical detector-free mechanism for leveraging it. To shed light on these joint decoding dynamics, we draw inspiration from attention sink analyses in large language models, leveraging Track Attention Mass to show that standard generic queries exhibit unbalanced attention, frequently struggling to weigh track context against novel object discovery. SelfMOTR achieves highly competitive performance in complex, dynamic environments, yielding 69.2 HOTA on DanceTrack and leading with 71.1 HOTA on the Bird Flock Tracking (BFT) dataset. Project page: https://medem23.github.io/SM.

ECCV 2026

PhenoLeaf-TS: A Time-Series Benchmark for Leaf Instance Segmentation, Tracking, and Growth Stage Classification

Rijad Saric, Basim Azam, Sarmad Khan, Edhem Custovic

Image-based high-throughput plant phenotyping leveragesinstance-level understanding of individual leaves growth over experimen-tal time, yet existing datasets lack temporal depth and annotation con-sistency to jointly benchmark segmentation, tracking, and growth stageclassification. We introduce PhenoLeaf-TS, a time-series dataset of 17,082top-down RGB images spanning 21 Arabidopsis thaliana genotypes, to-talling 318 plant replicates, each annotated with colour-coded leaf in-stance masks that maintain consistent identity throughout the growthsequence. We define three benchmark computer vision tasks with stan-dardised protocols and evaluate 21 distinct models: 9 instance segmenta-tion architectures, 6 multi-object trackers, and 6 classification architec-tures. Mask R-CNN with ResNet-50 achieves 73.2 mAP for leaf segmen-tation, ByteTrack reaches 84.1% MOTA for tracking individual leaves insequences, and Swin-T achieves 91.7% accuracy for growth stage classifi-cation. Cross-dataset experiments on CVPPP and Komatsuna show thatfine-tuning from PhenoLeaf-TS weights yields up to +51 mAP over zero-shot transfer. The dataset, the evaluation code, and the trained modelsare publicly accessible at https://pisyntor.github.io/PhenoLeaf-TS.

ECCV 2026

UniQueR: Unified Query-based Feedforward 3D Reconstruction

Chensheng Peng, Quentin Herau, Jiezhi Yang, Yichen Xie, Yihan Hu, Wenzhao Zheng, Matthew Strong, Masayoshi Tomizuka, Wei Zhan

We present UniQueR, a unified query-based feedforward framework for efficient and accurate 3D reconstruction from unposed images. Existing feedforward models such as DUSt3R, VGGT, and AnySplat typically predict per-pixel point maps or pixel-aligned Gaussians, which remain fundamentally 2.5D and limited to visible surfaces. In contrast, UniQueR formulates reconstruction as a sparse 3D query inference problem. Our model learns a compact set of 3D anchor points that act as explicit geometric queries, enabling the network to infer scene structure— including geometry in occluded regions—in a single forward pass. Each query encodes spatial and appearance priors directly in global 3D space (instead of per-frame camera space) and spawns a set of 3D Gaussians for differentiable rendering. By leveraging unified query interactions across multi-view features and a decoupled cross-attention design, UniQueR achieves strong geometric expressiveness while substantially reducing memory and computational cost. Experiments on Mip-NeRF 360 and VR-NeRF demonstrate that UniQueR surpasses recent feedforward baselines in both rendering quality and geometric accuracy, using an order of magnitude fewer primitives than dense alternatives.

ECCV 2026

Spectral Consistent Flow for One-step 3D Medical Image Translation

Haoqing Li, Jun Shi, Mingchao Li, Zehua Zhu, Qiwei Jia, Jiong SHI, Hong An

We present Spectral Consistent Flow (SC-Flow), a 3D med-ical image translation framework with a single function evaluation (1-NFE) in the latent space. This approach reformulates medical imagetranslation as a stochastic Brownian bridge process that directly con-structs a mapping between source and target modalities by predictingthe support regularized mean velocity field. To mitigate modality entan-glement, over-smoothing, and artifacts induced by the implicit low-passmodulation of the latent average velocity, we introduce a Spectral Consis-tency Corrector that dynamically regularizes the evolution of the powerspectral density via learnable frequency-domain gain modulation. Thismechanism establishes an explicit bridge between spatial textures andspectral energy flow, enabling the model to recover fine-grained anatom-ical fidelity while maintaining global structural coherence. Extensive ex-periments on four datasets demonstrate that SC-Flow delivers signifi-cantly more accurate, consistent, and robust performance across varioustranslation scenarios.

ECCV 2026

InstantRetouch: Personalized Image Retouching without Test-time Fine-tuning

Temesgen Muruts Weldengus, Binnan Liu, Fei Kou, Youwei Lyu, Jinwei Chen, Changqing Zou, Qingnan Fan

Personalized image retouching aims to adapt retouching stylesof individual users from reference examples, but existing methods oftenrequire user-specific fine-tuning or fail to generalize effectively. To addressthese challenges, we introduce RefRetouch, a general framework forpersonalized image retouching that instantly adapts to user retouchingstyles without any test-time fine-tuning. It employs an asymmetric auto-encoder to encode the retouching style from paired examples into a con-tent disentangled latent representation that enables faithful transfer ofthe retouching style to new images. To adaptively apply the encoded re-touching style to new images, we further propose retrieval-augmented re-touching (RAR), which retrieves and aggregates style latents from refer-ence pairs most similar in content to the query image. With these compo-nents, RefRetouch enables superior and generic content-aware retouch-ing personalization across diverse scenarios, including single-reference,multi-reference, and mixed-style settings, while also generalizing out ofthe box to photorealistic style transfer.

ECCV 2026

Event-based Gaze Control Systems for Real-time Spin Estimation in Professional Ball Games

Yunpu Hu, Fabian Schilling, Valentina Cavinato, Asude Aydin, Agis Politis, Ricardo Morales, Kirk Scheper, Peter Dürr, Naoya Takahashi

Spin plays a crucial role in many ball sports due to its effecton the trajectory of the ball. Vision-based estimation of the ball’s spinduring a game with conventional cameras is challenging due to the ball’ssmall size, high speed, and fast rotation. To address these challenges, wepropose an event-based active vision system that can track unmodifiedballs and measure their spin in real time. The system consists of an eventcamera for its high temporal resolution and minimal motion blur, high-speed pan/tilt galvanometer mirrors to keep the ball in the field of view,and a low-latency focus-tunable telephoto lens to increase the spatial res-olution on the ball and keep it in focus. To track the ball, we use a hybridapproach that combines 2D event-based detection for centering and 3Dpositions from a ball localization system for re-initialization. For high-accuracy spin estimation, we propose an offline method that performscontrast maximization on the sphere (s-CMax). This method achievesstate-of-the-art accuracy on static balls across multiple sports (table ten-nis, baseball, tennis, and golf), with mean magnitude and axis errors of1.2% and 1.5 degrees, respectively. We then develop a low-latency onlinemethod for table tennis as a case study in real-time applications. Thismethod uses an uncertainty-aware convolutional neural network trainedon pseudo-ground-truth spin labels from the offline approach, combinedwith a GPU-accelerated batch implementation of contrast maximizationfor refinement. We demonstrate reliable tracking and spin estimationwith a three-view setup during professional table tennis matches, withhigh accuracy (8.8% magnitude and 6.4 degrees axis mismatch w.r.t. theoffline method), 3 ms latency, and 750 Hz throughput.

ECCV 2026

Leaving the City: A Large-Scale Aerial Dataset for Cross-Season Localization in Unstructured Environments

Michael Schleiss, Henry Hölzemann, Fahmi Rouatbi, Torsten Fiolka, Thomas Pany, Roger Förstner, Daniel Cremers

Long-term aerial localization requires matching live flightimagery against archival reference maps, demanding feature represen-tations that are invariant to severe appearance changes and perceptualaliasing. However, current benchmarks report only aggregate accuracyover predominantly man-made environments, masking severe terrain-dependent performance gaps. As a result, localization performance overunstructured natural landscapes—where self-similar textures and drasticseasonal changes dominate—remains effectively unmeasured. To addressthis, we introduce Leaving the City, the first large-scale aerial benchmarkdesigned to isolate and quantify terrain-dependent localization gaps. Cap-tured via a microlight aircraft, our dataset comprises 1,379 km of flighttrajectories flown repeatedly to capture distinct seasonal variations. Wepair high-frame-rate imagery and inertial measurements with semanticterrain masks, multi-year-old orthophotos, and precise 6-DoF groundtruth. Evaluating state-of-the-art matchers through our terrain-stratifiedprotocol reveals a systematic bias: methods that succeed on man-madesurfaces degrade sharply over natural terrain undergoing strong appear-ance change. By exposing where current methods fail, our benchmarkprovides a rigorous foundation for developing robust, all-terrain aeriallocalization. The dataset and code are publicly available.4

ECCV 2026

Atlas is Your Perfect Context: One-Shot Customization for Generalizable Foundational Medical Image Segmentation

Ziyu Zhang, Yi Yu, Simeng Zhu, Ahmed Aly, Yunhe Gao, Ning Gu, Yuan Xue

Accurate anatomical structure segmentation in medical im-age is essential for diagnosis and treatment planning. While recent inter-active segmentation foundation models enhance generalization throughlarge-scale multimodal pretraining, they still depend on precise promptsand can fail in underrepresented clinical contexts (e.g., small organs-at-risk). We present AtlasSegFM, an atlas-guided framework that cus-tomizes off-the-shelf foundation models to new clinical contexts with asingle annotated example. AtlasSegFM 1) performs atlas-query regis-tration to generate context-aware prompts, 2) refines the segmentationwith a frozen foundation model, and 3) applies a lightweight adaptivefusion module to combine atlas priors with foundation-model inputs andpredictions. Extensive experiments on six public and in-house datasetsacross radiotherapy and vascular scenarios show consistent gains, withthe largest improvements on small and delicate structures. AtlasSegFMprovides a lightweight, deployable solution for one-shot customization ofsegmentation foundation models in real-world clinical workflows.

ECCV 2026

Mitigating Positional Leakage in 3D Masked Autoencoders for Robust Representation Learning

Xu Yan, Huiqun Wang, Chen Wang, Lei Ren, Di Huang

Masked autoencoding has emerged as a prominent paradigmfor self-supervised learning on 3D point clouds, achieving competitiveperformance across downstream tasks. Unlike its 2D counterpart, 3Dmasked autoencoding directly reconstructs spatial coordinates, makingit inherently susceptible to positional leakage. In this work, we identifythat the decoder in existing 3D MAE frameworks tends to over-rely onpositional information, which weakens semantic representation learningand leads to suboptimal feature quality. To address this issue, we proposeMPL-MAE, a masked point learning framework that mitigates positionalover-reliance while enhancing the utilization of encoder features. Specifi-cally, we introduce a recalibrated positional embedding module that sup-presses metric-dominant coordinate signals while preserving geometrictopology, together with a gated positional interface module that dynam-ically regulates positional injection during reconstruction. These designspromote a more balanced interaction between spatial priors and seman-tic features, yielding robust and informative representations. Extensiveexperiments across downstream tasks demonstrate that MPL-MAE con-sistently achieves competitive performance, validating its effectiveness.Code is available at https://github.com/yanx57/MPL-MAE.

ECCV 2026

Dress-ED: Instruction-Guided Editing for Virtual Try-On and Try-Off

Fulvio Sanguigni, Davide Lobba, Bin Ren, Marcella Cornia, Nicu Sebe, Rita Cucchiara

Recent advances in Virtual Try-On (VTON) and VirtualTry-Off (VTOFF) have greatly improved photo-realistic fashion synthesisand garment reconstruction. However, existing datasets remain static,lacking instruction-driven editing for controllable and interactive fash-ion generation. In this work, we introduce the Dress Editing Dataset(Dress-ED), the first large-scale benchmark that unifies VTON, VTOFF,and text-guided garment editing within a single framework. Each sam-ple in Dress-ED includes an in-shop garment image, the correspondingperson image wearing the garment, their edited counterparts, and anatural-language instruction of the desired modification. Built througha fully automated multimodal pipeline that integrates MLLM-basedgarment understanding, diffusion-based editing, and LLM-guided ver-ification, Dress-ED comprises over 146k verified quadruplets spanningthree garment categories and seven edit types, including both appear-ance (e.g., color, pattern, material) and structural (e.g., sleeve length,neckline) modifications. Based on this benchmark, we further propose aunified multimodal diffusion framework that jointly reasons over linguisticinstructions and visual garment cues, serving as a strong baseline forinstruction-driven VTON and VTOFF.

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