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ECCV 2026

SSDD: Single-Step Diffusion Decoder for Efficient Image Tokenization

Théophane Vallaeys, Jakob Verbeek, Matthieu Cord

Tokenizers are a key component of state-of-the-art generativeimage models, extracting the most important features from the signalwhile reducing data dimension and redundancy. Most current imagetokenizers are based on KL-regularized variational autoencoders (KL-VAE), trained with reconstruction, perceptual and adversarial losses.Diffusion decoders have been proposed as a more principled alternativeto model the distribution over images conditioned on the latent. However,matching the performance of KL-VAE still required adversarial losses,as well as a higher decoding time due to iterative sampling. To addressthese limitations, we introduce a new pixel diffusion decoder architecturefor improved scaling and training stability, benefiting from transformercomponents and GAN-free training. We use distillation to replicate theperformance of the diffusion decoder in an efficient single-step decoder.This makes SSDD the first GAN-free single-step diffusion decoder toreach state-of-the-art continuous tokenization, with higher reconstructionquality and faster sampling than KL-VAE. In particular, SSDD improvesreconstruction FID from 0.87 to 0.46 with 1.4× higher throughput andpreserves generation quality of DiTs with 3.8× faster sampling. As such,SSDD can be used as a drop-in replacement for KL-VAE, and for buildinghigher-quality and faster generative models.

ECCV 2026

SARA: Structure-Aware Riemannian-Guided Alignment for Drone Image-Text Retrieval

Keyu Lu, Qing Ma, Zhenyu Lu, Cong Bai

Drone image–text retrieval (DITR) aims to associate aerialimages with textual descriptions in complex low-altitude environments.However, intricate spatial layouts and long-range structural dependen-cies in drone imagery challenge conventional Euclidean embedding mod-els, often leading to cross-modal misalignment. To address this issue, wepropose SARA, a structure-aware Riemannian-guided alignment frame-work that incorporates Riemannian manifold modeling to complementsemantic embeddings. Specifically, the Manifold Structural Feature Ex-traction (MSFE) module captures second-order structural dependenciesby encoding local feature correlations as symmetric positive definite rep-resentations. The Log-Euclidean Structural Alignment (LESA) moduleperforms geometry-consistent alignment in the tangent space, integratingsemantic correspondence with structural coherence through distributionalignment and feature fusion. We further provide theoretical analysisdemonstrating convergence and minimization of an upper bound on theexpected alignment error. Experiments on ERA and UDV benchmarksdemonstrate that SARA consistently improves retrieval performance overstate-of-the-art methods, particularly in structurally complex or seman-tically ambiguous drone scenarios.

ECCV 2026

SAM+D: Parameter-Efficient Dimensional Lifting of SAM-Family Models via Depth-Routed LoRA and Depth Shifting

Yu Song, Hao Sun, Teng Shiyu, Ikuko Nishikawa, Yen-wei Chen

Existing methods for adapting 2D foundation models such asSAM to 3D volumes either process slices independently—ignoring inter-slice context—or require substantial architectural changes and retraining.In this paper, we present SAM+D, a parameter-efficient framework thatlifts SAM-family models by one spatial dimension—enabling 3D volu-metric segmentation from 2D SAM and, for the first time via parameter-efficient fine-tuning, end-to-end 4D (3D+T) spatiotemporal segmenta-tion from video-based SAM2—while keeping the vast majority of pre-trained parameters frozen. SAM+D introduces two lightweight, model-agnostic modules into frozen transformer blocks: (1) Depth-RoutedLoRA (DRLoRA) experts with learned routing for spatially adaptivelow-rank updates, and (2) Depth Shift Modules (DSM) for cross-slice feature exchange at zero additional parameter cost. Together, theyprovide volume-level context while tuning only ∼2.8% of parameters forSAM and ∼3.7% for SAM2. We evaluate SAM+D in two distinct set-tings, each lifting the base model by one spatial dimension: 3D segmenta-tion, where SAM (2D → 3D) is evaluated on four CT benchmarks (KiTS,Pancreas, LiTS, Colon), and 4D segmentation, where SAM2 (2D+T →3D+T) is evaluated on a cell tracking challenge (CTC) dataset (Fluo-N3DH-SIM+). In both settings SAM+D achieves competitive or superiorresults under the single-point prompt setting while using fewer trainableparameters than existing methods, demonstrating that SAM+D general-izes across SAM-family architectures, target dimensionalities (3D, 4D),and domains spanning medical imaging and bio-scene understanding.Code is publicly available at https://github.com/JerrySongCST/SAM-Plus-D.

ECCV 2026

Revisiting the Volumetric Data of 4DME: Compression, Extension and Benchmarking for Micro-Expression Analysis

Samuel Boccara, Amar Tious, Guoying Zhao, Yante Li, Toinon Vigier, Vincent Ricordel

Micro-expressions are brief, involuntary facial movementsthat reveal subtle affective states. While most computational work fo-cuses on micro-expression recognition, which classifies clips into dis-crete emotion categories, Micro-Expression Action Unit (ME-AU) de-tection offers a more theory-consistent formulation grounded in the Fa-cial Action Coding System. Despite its finer granularity, ME-AU de-tection remains comparatively under-explored due to the limited avail-ability of high-quality data for efficient automatic models. Recent volu-metric micro-expression datasets provide temporally coherent 3D facialrecordings with reliable AU labels, creating new opportunities for fine-grained analysis. However, their adoption remains limited due to largedata volumes, heterogeneous formats, and the absence of standardizedbenchmarks, which together hinder reproducibility and cross-study com-parison. Moreover, because of the small spatio-temporal spanning of theAUs, designing models that effectively combine volumetric representa-tions with temporal dynamics remains a non-trivial research challenge.To address these barriers, we introduce an extended and standardizedrelease of the 4DME dataset with cleaned data and normalized format-ting, accompanied by a compressed version to reduce storage overhead.We further design VoluME, a family of dual-stream optical-flow-basedarchitecture for volumetric ME-AU detection. To determine appropriatesettings for compression, we combine state-of-the-art compression base-lines, perceptual evaluation, and downstream task-specific benchmark-ing. As the first work dealing with volumetric ME-AU detection undercompression, our results show that a compression ratio of approximately3000× can be achieved with ≈ 1% F1 degradation. The extended releaseadditionally introduces explicit two-way cultural balancing, supportingfuture cross-cultural micro-expression studies.

ECCV 2026

Bottom-up modeling of repeated elements via single image analysis-by-synthesis

syrine kalleli, Alexei Efros, Mathieu Aubry

We address the problem of discovering repeated elementsfrom a single image. In contrast to existing approaches that depend onlarge annotated datasets, curated multi-image collections, or object seg-mentation masks, we show that a single image can suffice to learn ameaningful object model in a completely bottom-up fashion, without anyprior knowledge beyond a coarse scale prior. Our method learns a tunableimage-space prototype of the repeated elements through a reconstructionobjective, enabling the model to identify and synthesize consistent objectinstances within the same image. Experiments on 116 real images fromthe FSC-147 dataset demonstrate that our method successfully learnscoherent element models and captures intra-category variation on chal-lenging images. Qualitative results reveal superior reconstructions andinterpretable decompositions compared to classical decomposition, jointalignment, and 3D object modeling methods, while maintaining a simple2D formulation. These results suggest that meaningful object discoverycan emerge from single-image learning alone.

ECCV 2026

ReSWD: ReSTIR‘d, not shaken. Combining Reservoir Sampling and Sliced Wasserstein Distance for Variance Reduction.

Mark Boss, Andreas Engelhardt, Simon Donne, Varun Jampani

Distribution matching is central to many vision and graphicstasks, where the widely used Wasserstein distance is too costly to com-pute for high-dimensional distributions. The Sliced Wasserstein Distance(SWD) offers a scalable alternative, yet its Monte Carlo estimator suffersfrom high variance, resulting in noisy gradients and slow convergence. Weintroduce Reservoir SWD (ReSWD), which integrates Weighted Reser-voir Sampling into SWD to adaptively retain informative projectiondirections in optimization steps, resulting in stable gradients while re-maining unbiased. Experiments on synthetic benchmarks and real-worldtasks such as color correction and diffusion guidance show that ReSWDconsistently outperforms standard SWD and other variance reductionbaselines. Project page with code: https://ReservoirSWD.github.io

ECCV 2026

Low-Rank Ternary Adaptation for Fine-Tuning Transformers

Alexandru-Dragos Manolache, Yunqiang Li, Jan van Gemert

Ternary transformers offer extreme memory and compute ef-ficiency, but existing low-bit LoRA-based methods cannot directly fine-tune ternary weights. Current approaches either require dequantization,restoring low-bit base weights to higher precision to merge with adap-tation weight, or update only quantization parameters, preventing amerged model that remains ternary. We propose ternary multiplica-tive adaptation, which represents discrete updates of ternary weightssuch as sign flips or zeroing through a low-rank Kronecker factoriza-tion into two small ternary matrices applied element-wise to ternaryweights. This design is parameter-efficient and expressive, preserves theternary domain, and supports direct merging without dequantization.Experiments on six models across language and vision, including ternar-ized LLaMA-3 1B and 3B and a ternary ViT-B/16, demonstrate thatour method recovers much of the performance lost to quantization andoutperforms strong low-bit and ternary baselines. Code is available athttps://github.com/alexmanoo/ternary_adaptation.

ECCV 2026

Reflecting Process Expertise in Procedural Material Generation

Kunal Gupta, Gaurav Joshi, Yen-Ru Chen, Seemandhar Jain, Ishit Mehta, Manmohan Chandraker

Procedural material creation underpins applications in digitalcontent creation, visual effects, and 3D asset design. Achieving high-quality results requires more than reproducing node graphs—it demandsunderstanding the process by which experts construct materials. We for-mulate procedural material generation as retrieval-time process reasoningover expert demonstrations, elevating process to a first-class represen-tation beyond graph-only synthesis. Concretely, we represent expertworkflows as process traces: textual records of construction steps, param-eters, and design intent. To instantiate this idea, we use a pretrainedLLM-based ProcessSynthesizer to synthesize a process trace alignedwith a user’s intent and a pretrained LLM-based Compiler to groundthe process trace into an executable Blender material graph. Becauseprocedural expertise is most naturally conveyed through demonstrations,we leverage tutorial videos as a source of process knowledge and extracttextual, LLM-compatible traces using automated video analysis tools.In an expert study with five Blender artists (avg. 7.5 years of expe-rience), materials generated by reflecting expert demonstrations werefound to produce workflows requiring fewer edits, and more closely matchprofessional design strategies than methods operating solely on staticartifacts. A user study with 150 participants further shows that ourapproach achieves superior generation and editing performance comparedto prior procedural systems. All code, models, and data will be availableat https://materialapprentice.github.io.

ECCV 2026

PRISM: Latent Composition Consistency for Single-Image Reflection Removal

Junseong Shin, Tae Hyun Kim

Single-image reflection removal (SIRR) seeks to recover thetransmission layer from a mixture corrupted by reflections—a severelyill-posed problem. Existing methods operate in pixel space, where thenonlinear sRGB formation model entangles the two layers and limits gen-eralization. We observe that pretrained VAE latent spaces exhibit sub-stantially lower coherence between image layers compared to pixel space,providing a more favorable working space for decomposition. Buildingon this finding, we propose PRISM (Pretrained-latent Reflection ImageSeparation Model), which reinterprets SIRR as a latent linear separa-tion problem. Under an approximate additive formulation in latent space,PRISM learns a flow matching velocity field on a pretrained FLUX back-bone that recovers both transmission and reflection in a single forwardpass. To enforce robust disentanglement, we introduce a Latent Com-position Consistency (LCC) strategy that constructs synthetic mixturesby swapping reflection latents across samples and enforces consistent de-composition via a cycle loss. We further propose a Layer ContrastiveSeparation (LCS) loss that promotes semantic separation between layersthrough patch-level contrastive learning, without requiring explicit reflec-tion targets. Experiments on six benchmarks demonstrate that PRISMconsistently outperforms state-of-the-art methods by significant margins,with strong generalization to in-the-wild images.

ECCV 2026

OrthoTailor: Geometric Orthogonalization for Conflict-Free Unified Fashion Generation

Zhaotong Yang, Ying Tai, Jiahui Zhan, Yu Zheng, Jianjun Qian, Jian Yang

Unified fashion generation integrates tasks like virtual try-onand garment reconstruction into a single model to reduce task-specificadaptation costs. However, naive parameter sharing across semanticallydistinct tasks induces negative transfer through severe inter-task gradientconflict. We propose OrthoTryOn, a unified framework mitigating thisinterference within a shared Low-Rank Adaptation (LoRA) module. ItsOrthogonal Subspace Projection (OSP) applies task-specific orthogonalrotations to bottleneck features, mapping them into decorrelated coordi-nate frames. To address residual semantic coupling at inference time, wefurther propose Fisher-guided Negative Guidance (FNG), a parameter-free strategy that utilizes diagonal Fisher information to quantify inter-task sensitivity overlap and explicitly repels generation trajectories fromthe most confusable task via Classifier-Free Guidance. Extensive exper-iments demonstrate that OrthoTryOn avoids the severe performancedegradation typical of naive unified training and even surpasses inde-pendently trained task-specific models, achieving state-of-the-art resultsacross multiple benchmarks while generalizing robustly across diversediffusion backbones. Code is available at https://github.com/NJU-PCALab/OrthoTryOn.

ECCV 2026

OmniX: Any-view and Any-time 4D reconstruction via Feed-forward Trajectory Fields

Yanqin Jiang, Tengfei Wang, Zhenwei Wang, Chenjie Cao, Junta Wu, Wenhan Luo, Weiming Hu, Jin Gao, Chunchao Guo

Previous feed-forward 4D reconstruction methods either pre-dict per-frame static point clouds, ignoring foreground motion, or esti-mate point cloud trajectories while being limited to small camera mo-tions. This limits their ability to aggregate observations over time andreconstruct complete dynamic scenes under large viewpoint changes. Toaddress this, we propose OmniX, a feed-forward 4D reconstruction frame-work that predicts dense 3D point trajectories for every pixel fromvideos with large camera motion. 1) OmniX separates dynamic mo-tion modeling from static geometry prediction and represents motionwith a small set of dynamic tokens. Leveraging the sparse and low-rankstructure of 3D motion, these tokens generate trajectory fields for allpixels in all images while efficiently preserving global interactions. 2) Tofacilitate training, we build an automatic UE5-based 4D data engine andintroduce a dataset of 80k scenes and 1.28M multi-view videos with fullgeometric annotations. OmniX achieves state-of-the-art results on dense3D point trajectory prediction and 3D point tracking, with competitiveperformance on video depth estimation and camera pose estimation.

ECCV 2026

OmniMapBench: Benchmarking Visual-Centric Reasoning on Diverse Map Documents

Yang Chen, Yufan Shen, Yunwen Li, minghao liu, Tuney Tianyu, Bin Fu, Qunshu Lin, Zhi Yu, Botian Shi

Recent advancements in LVLMs necessitate robust bench-marks for complex, visually grounded reasoning. A critical limitation isidentified in many document understanding benchmarks: visual contentis often reducible to text, enabling high performance without genuine vi-sual grounding. To address this limitation, OmniMapBench is introducedto foster visual-centric reasoning for map documents. The benchmarkcomprises 2,096 manually annotated question-answer pairs across 1,603map documents from nine categories. It is designed to probe a hierar-chy of skills, ranging from perception to multi-step visual reasoning. Toquantify benchmark properties, a simple yet effective benchmark-levelmetric is proposed: the Visual Dependency Index (VDI), defined as theaccuracy drop when images are replaced with question-agnostic descrip-tions. OmniMapBench exhibits higher VDI than established benchmarks,which quantitatively validates its focus on irreducible visual reasoning.Comprehensive evaluations of 25 leading LVLMs are conducted on Om-niMapBench. A significant performance gap is observed, with the top-performing model achieving only 75.03% accuracy. This result under-scores the challenges posed by OmniMapBench to current LVLMs. Thiswork aims to catalyze progress in visual-centric reasoning for documentunderstanding of LVLMs. The dataset and code are publicly available athttps://github.com/SIGMME/OmniMapBench.

ECCV 2026

Isotropic Embedding Perturbations for Robust Vision Language Encoders

Hyesong Choi, Daeun Kim, Song Park, Taekyung Kim, Byeongho Heo, Sangdoo Yun, Dongbo Min, Dongyoon Han

Data augmentation is fundamental to training modern deepvision and multimodal models. While individual methods, such as Ran-dAug, CutMix, Mixup, RandErase, and DropPath, offer strong regular-ization effects, their combined use has saturated in performance due tooverlapping functionalities, and aggressive pixel-level manipulations maydisrupt delicate cross-modal alignment. This saturation motivates thesearch for a new augmentation axis within the embedding space ratherthan the input space. We introduce Aether, a simple plug-in method thatapplies diffusion-style random perturbations in the embedding space viacontrolled alpha-mixing, specifically designed to provide isotropic regu-larization that remains semantically consistent. Inspired by feature-spaceperturbations in language models and image degradation in generativepretraining, Aether induces mild yet effective perturbations that smooththe representations without compromising the fine-grained structuralinformation required for strong vision-language encoders. Across diversearchitectures and across multiple recognition tasks, Aether delivers consis-tent gains over the advanced recipe combining CutMix, Mixup, DropPath,and RandAug—a level of improvement rarely observed with modern aug-mentation alternatives. Notably, Aether demonstrates superior effective-ness in multi-modal alignment, succeeding where traditional pixel-spaceaugmentations fail by providing a stable, isotropic regularization signalthat respects the integrity of the high-dimensional feature space. Code isavailable at https://github.com/naver-ai/aether.

ECCV 2026

Moebius: 0.2B Lightweight Image Inpainting Framework with 10B-Level Performance

Kangsheng Duan, Ziyang Xu, Wenyu Liu, XIAOHU RUAN, Xiaoxin Chen, Xinggang Wang

While 10B-level industrial foundation models have pushedthe boundaries of image inpainting, their prohibitive computational costsseverely hinder practical deployment. Constructing a highly optimizedtask-specific specialist offers a promising solution; however, extreme struc-tural compression inevitably triggers a severe representation bottleneck.To conquer this, we propose Moebius, a highly efficient lightweight in-painting framework. We systematically reconstruct the diffusion back-bone by introducing the Local-λ Mix Interaction (LλM I) block. Com-prising Local-λ and Interactive-λ modules, it elegantly summarizes spa-tial contexts and global semantic priors into fixed-size linear matrices,preserving complex latent interactions while drastically shedding param-eters. Furthermore, to unlock the full representational capacity of thishighly compact architecture, we synergistically pair it with an adap-tive multi-granularity distillation strategy. Operating strictly within thelatent space to avoid expensive pixel-space decoding, this strategy dy-namically balances multiple gradient-based losses to achieve high-fidelityalignment. Extensive experiments across natural and portrait bench-marks demonstrate that this optimal synergy enables Moebius to rival oreven surpass the generation quality of the 10B-level industrial generalistFLUX.1-Fill-Dev. Remarkably, Moebius achieves this using less than 2%of the parameters (0.22B vs. 11.9B) while delivering a > 15× accelerationin total inference time, setting a new efficiency standard for high-fidelityinpainting. Project page at https://hustvl.github.io/Moebius.

ECCV 2026

MobileVLA-R1: Reinforcing Vision-Language-Action for Mobile Robots

Ting Huang, Dongjian Li, Rui Yang, Zeyu Zhang, ZIDA YANG, Hao Tang

Grounding natural-language instructions into executable con-tinuous control remains a core challenge for quadruped vision-language-action (VLA) systems due to the gap between high-level semantic reason-ing and low-level locomotion actuation. Existing approaches often relyon implicit reasoning or purely behavioral supervision, leading to unsta-ble long-horizon grounding and limited robustness in real-world deploy-ment. To address these issues, we present MobileVLA-R1, a unifiedquadruped VLA framework that explicitly aligns hierarchical reason-ing with continuous control. Our method introduces a multi-granularitychain-of-thought (CoT) supervision scheme for embodied trajectories, in-stantiated in MobileVLA-CoT, together with a two-stage training paradigmthat combines supervised CoT alignment and reinforcement learning forreasoning-to-control consistency. We conduct comprehensive evaluationson VLN and quadruped VLA benchmarks, including VLN-CE, QUARD,and real-world Unitree Go2 deployment. MobileVLA-R1 consistently out-performs strong baselines, achieving about 5% gains on key metrics whiledemonstrating deployment-time closed-loop execution under a fixed hy-brid inference setup.

ECCV 2026

From Local Geometry to Global Pseudo-Labeling for Robust Positive–Unlabeled Learning under Covariate Shift

Firas Gabetni, Alexandre Rocchi--Henry, Ziyi Liu, Nacim Belkhir, Gianni Franchi

Detecting covariate shift is critical for building reliable vi-sion systems. While most prior work focuses on improving robustness toshift, explicitly detecting covariate shift remains underexplored. Existingapproaches typically rely on fully supervised training, requiring labeledexamples from both original and shifted distributions, which is often im-practical. In this paper, we show that covariate shift detection can beeffectively addressed with weaker supervision using Positive–Unlabeled(PU) learning. However, under covariate shift, in-distribution and shifteddata overlap significantly, making classical PU methods unstable and sen-sitive to noise. To overcome this challenge, we introduce Spectral PUNeighborhood Annotation (S-PUNA), a geometry-aware frame-work that progressively discovers shifted data by leveraging the localmanifold structure of visual features. Extensive experiments show thatS-PUNA achieves state-of-the-art performance in PU settings and re-markably matches the performance of fully supervised methods. More-over, our approach transfers robustly across different types of shifts,demonstrating strong generalization capabilities. Code is available athttps://github.com/fira7s/S-PUNA.

ECCV 2026

Memory Tree Guided Key Frame Querying for Efficient 3D Question Answering

Hsiang-Wei Huang, Fu-Chen Chen, Li-Wu Tsao, Cheng-Han Lee, Che-Chun Su, Lu Xia, Ronghui Peng, Jenq-Neng Hwang, Min Sun, Cheng-Hao Kuo

Answering questions accurately and efficiently in embodiedscenarios presents significant challenges due to limited computationaland memory resources for Vision Language Model (VLM) inference. Ex-isting methods adopt visual search key frame retrieval method to se-lect critical question-related key frames for VLM input. However, visualsearch methods are inefficient because they require visual search amongthousands of video frames for each individual user query. In this work, wepropose a memory tree guided key frame selection paradigm for efficient3D question answering in embodied scenarios. Our method leveragesa compact and reusable 3D scene representation, termed MemTree3D,which supports real-time online construction leveraging camera 6-DoFposes. MemTree3D captures multi-level 3D scene information, enabling aLarge Language Model to efficiently query and retrieve question-relevantkey frames through our scoring-based frame selection without repro-cessing the entire video stream. On OpenEQA, our method improvesthe LLM-Match of GPT-4o by 17.4%, LLaVA-OneVision-7B by 5.8%,outperforms existing visual search methods. Our code is available athttps://github.com/hsiangwei0903/MemTree3D.

ECCV 2026

ME-IQA: Memory-Enhanced Image Quality Assessment via Re-Ranking

Kanglong FAN, Tianhe Wu, Wen Wen, Jianzhao Liu, Le Yang, Yabin ZHANG, Yiting Liao, Junlin Li, Li Zhang

Reasoning-induced vision-language models (VLMs) advanceimage quality assessment (IQA) with textual reasoning, yet their scalarscores often lack sensitivity and collapse to a few values, so-called discretecollapse. We introduce ME-IQA, a plug-and-play, test-time memory-enhanced re-ranking framework. It (i) builds a memory bank and re-trieves semantically and perceptually aligned neighbors using reason-ing summaries, (ii) reframes the VLM as a probabilistic comparator toobtain pairwise preference probabilities and fuse this ordinal evidencewith the initial score under Thurstone’s Case V model, and (iii) per-forms gated reflection and consolidates memory to improve future deci-sions. This yields denser, distortion-sensitive predictions and mitigatesdiscrete collapse. Experiments across multiple IQA benchmarks showconsistent gains over strong reasoning-induced VLM baselines, existingnon-reasoning IQA methods, and test-time scaling alternatives.

ECCV 2026

Map2World: Segment Map Conditioned Text to 3D World Generation

Jaeyoung Chung, Suyoung Lee, Jianfeng Xiang, Jiaolong Yang, Kyoung Mu Lee

High-quality world-scale 3D data is scarce, making completeand generalizable 3D world generation difficult. Existing approaches typi-cally rely on either generated images/videos or domain-specific 3D worlddatasets, resulting in worlds that are often incomplete or restricted tonarrow domains. In this paper, we present Map2World, a map-conditionedframework that converts semantic layouts into complete and generalizablelarge-scale 3D worlds. Given a segment map with per-region text prompts,Map2World uses the map as a controllable world-level interface and lever-ages TRELLIS, a powerful pretrained 3D asset generator, as a general3D prior. To scale asset-level priors to world-level synthesis, Map2Worldenables arbitrary spatial expansion via MultiDiffusion, controls globalscale through initial noise optimization, and enhances local geometry andappearance with a dedicated enhancer. Experiments demonstrate thatMap2World generates complete, layout-controllable, scale-consistent, anddetailed 3D worlds across diverse layouts and semantics.

ECCV 2026

Local-to-global Cross-modal Coordination for Self-supervised RGB-T Tracking

Yueying Zhang, Timing Li, Bing Cao, Pengfei Zhu

Self-supervised RGB-T tracking seeks to learn robust cross-modal representations without dense manual annotations. A commonsupervision strategy relies on forward-backward tracking loops, but inpractice, it is highly vulnerable to error accumulation and modality driftcaused by the inconsistent reliability of visible and thermal cues. To ad-dress this issue, we propose LGCTrack, a self-supervised RGB-T trackerunderpinned by a local-to-global coordination strategy within a closed-loop verification paradigm. At the local level, a hierarchical coordina-tion module performs layer-wise, reliability-aware token interaction toenable adaptive cross-modal fusion. At the global level, a turning-phasecoordination module refines the target representation during the criticalforward-to-backward transition, anchoring target semantics and improv-ing backward tracking. By jointly coordinating cross-modal interactionat two granularities, LGCTrack intrinsically filters pseudo-label noise andmitigates drift. Experiments on standard RGB-T tracking benchmarksshow that LGCTrack consistently outperforms existing self-supervisedmethods and substantially narrows the gap to fully supervised trackers.Our code is available at https://github.com/zyyyya/LGCTrack.

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