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Search papers from 30+ top conferences, including NeurIPS, ICLR, ICML, CVPR, ACL and AAAI. Filter by keyword, then open any paper for its abstract, authors and links.

ECCV 2026

Confidence-Based Mesh Extraction from 3D Gaussians

Lukas Radl, Felix Windisch, Andreas Kurz, Thomas Köhler, Michael Steiner, Markus Steinberger

Recently, 3D Gaussian Splatting (3DGS) greatly acceleratedmesh extraction from posed images due to its explicit representation andfast software rasterization. While the addition of geometric losses andother priors has improved the accuracy of extracted surfaces, mesh ex-traction remains difficult in scenes with abundant view-dependent effects.To resolve the resulting ambiguities, prior works rely on multi-view tech-niques, iterative mesh extraction, or large pre-trained models, sacrificingthe inherent efficiency of 3DGS. In this work, we present a simple and ef-ficient alternative by introducing a self-supervised confidence frameworkto 3DGS: within this framework, learnable confidence values dynam-ically balance photometric and geometric supervision. Extending ourconfidence-driven formulation, we introduce losses which penalize per-primitive color and normal variance and demonstrate their benefits tosurface extraction. Finally, we complement the above with an improvedappearance model, by decoupling the individual terms of the D-SSIMloss. Our final approach delivers state-of-the-art results for unboundedmeshes while remaining highly efficient.

ECCV 2026

ColorFM: An Optimization-to-Learning Framework for Color Transfer via Flow Matching

Yuhang He, Kai Zhang, Xiaoming Li, Du Chen, Jian Yang

Color transfer aims to align the color distribution of a sourceimage with that of a reference image while preserving structural andsemantic consistency. However, existing methods often suffer from inac-curate global mapping, semantic misalignment, and visual artifacts. Toaddress these issues, we propose ColorFM, an optimization-to-learningframework. ColorFM connects online optimization to offline inference byreformulating color transfer as the transport of pixel distributions alongvelocity fields via Flow Matching. Specifically, we introduce ColorFM-O, an instance-specific optimization scheme that fits the velocity fieldthrough hierarchical color coupling guided by semantic priors. By nu-merically integrating the induced flow trajectories, ColorFM-O producesprecise and semantically consistent color transfer results, while generat-ing high-quality paired data as pseudo-supervision. Building upon this,we design ColorFM-L, an efficient feed-forward model trained on thegenerated pairs. Through implicit state modeling, ColorFM-L extractsdeep semantic features to predict flow parameters for bidirectional lin-earized transport, ensuring accurate color transfer. Extensive experi-ments demonstrate that ColorFM-L outperforms state-of-the-art meth-ods in visual quality, structural fidelity, and semantic consistency, suc-cessfully combining the accuracy of optimization with the speed of feed-forward inference.

ECCV 2026

Attention-based Vision-Language Memory for Spatial Reasoning

Zuntao Liu, Taimeng Fu, Shaoshu Su, Cherie Ho, Chen Wang

Spatial reasoning is a critical capability for intelligent robots,yet current vision-language models (VLMs) still fall short of human-levelperformance in video-based spatial reasoning. This gap mainly stemsfrom two challenges: a semantic-geometric misalignment that preventsconsistent 3D understanding, and the absence of persistent memory toretain 3D representation and understanding across frames. To addressthese limitations, we present VLM2 , a Vision-Language Model with per-sistent Memory for spatial reasoning with a view-consistent, 3D-awarerepresentation purely from 2D videos. Specifically, we incorporate a dual-memory module consisting of a working memory that operates as a slid-ing window to focus on immediate context, and an episodic memory thatconsolidates and stores critical information across frames. This designenables bounded and efficient spatial reasoning under a fixed computa-tional cost. Extensive experiments on multiple benchmarks show thatVLM2 achieves state-of-the-art performance among video-based models,significantly advancing the frontier of visual-spatial intelligence.

ECCV 2026

ReMoMask: Retrieval-Augmented Masked Motion Generation

Zhengdao Li, Siheng Wang, Zeyu Zhang, Hao Tang

Retrieval-Augmented Text-to-Motion (RAG-T2M) modelshave demonstrated superior performance over conventional T2M ap-proaches, particularly in handling uncommon and complex textual de-scriptions by leveraging external motion knowledge. Despite these gains,existing RAG-T2M models remain limited by two closely related factors:coarse-grained text-motion retrieval that overlooks the hierarchical struc-ture of human motion, and underexplored mechanisms for effectivelyfusing retrieved information into the generative process. In this work,we present ReMoMask, a structure-aware RAG framework for text-to-motion generation that addresses these limitations. To improve retrieval,we propose Hierarchical Bidirectional Momentum (HBM) contrastivelearning, which employs dual objectives to jointly align global motionsemantics and fine-grained part-level features with text. To bridge thegap between structured retrieval and generation, we introduce TopologyStructured Masking (TSM), a training strategy that adaptively masksmotion tokens based on semantic relevance, forcing the model to learnrobust part-level grounding. Furthermore, we design Semantic Spatial-Temporal Attention (SSTA), a topology-aware fusion module that inte-grates retrieved knowledge via an asymmetric attention mechanism. Ex-tensive experiments on HumanML3D, KIT-ML, and SnapMoGen demon-strate that ReMoMask consistently outperforms prior methods on bothtext-motion retrieval and text-to-motion generation benchmarks.

ECCV 2026

AMCI: Unlock the Potential of Large Multimodal Models for Fine-grained Open-world Classification via Adaptive Memory Context Injection

Xiao Liu, Haiyang Zheng, Nan Pu, Wenjing Li, Nicu Sebe, Zhun Zhong

Open World Classification (OWC) with Large MultimodalModels (LMMs) is an emerging and promising task that moves beyondthe closed-world setting by enabling direct answer generation in responseto open-ended queries. However, we observe that the OWC performanceof LMMs significantly degrades on fine-grained tasks. Empirically, wefind that their performance is sensitive to contextual information in thequery. To address this limitation, we propose Adaptive Memory ContextInjection (AMCI), a training-free and model-agnostic framework that dy-namically restores contextual information during OWC inference. AMCImaintains a non-parametric memory that aligns visual indices with theircorresponding attribute-augmented descriptions. By employing an adap-tive similarity threshold to preserve memory diversity and utilizing arefinement LLM to transform accumulated context into a structuredprompt, AMCI transforms the initial zero-shot description into a cali-brated fine-grained prediction without parameter updates. Furthermore,we construct a specialized low-resource fine-grained benchmark to sim-ulate extreme data scarcity and introduce a new metric that penalizesoverly generic predictions, tailored for fine-grained OWC evaluation. Ex-tensive experiments demonstrate that AMCI consistently improves per-formance by a substantial margin while remaining computationally effi-cient and compatible with various LMMs.

ECCV 2026

360° Image Perception with MLLMs: A Comprehensive Benchmark and a Training-Free Method

Huyen Thi Thanh Tran, Van-Quang Nguyen, Farros Alferro, Kang-Jun Liu, Takayuki Okatani

Multimodal Large Language Models (MLLMs) have shownimpressive abilities in understanding and reasoning over conventionalimages. However, their perception of 360° images remains largely un-derexplored. Unlike conventional images, 360° images capture the en-tire surrounding environment, enabling holistic spatial reasoning butintroducing challenges such as geometric distortion and complex spa-tial relations. To comprehensively assess MLLMs’ capabilities to per-ceive 360° images, we introduce 360Bench, a Visual Question Answering(VQA) benchmark featuring 7K-resolution 360° images, seven represen-tative (sub)tasks with annotations carefully curated by human annota-tors. Using 360Bench, we systematically evaluate seven MLLMs and sixenhancement methods, revealing their shortcomings in 360° image per-ception. To address these challenges, we propose Free360, a training-free scene-graph-based framework for high-resolution 360◦ VQA. Free360decomposes the reasoning process into modular steps, applies adaptivespherical image transformations to 360° images tailored to each step,and seamlessly integrates the resulting information into a unified graphrepresentation for answer generation. Experiments show that Free360consistently improves its base MLLM and provides a strong training-free solution for 360◦ VQA tasks. The source code and dataset will beavailable at https://tranhuyen1191.github.io/360Bench-Free360/.

ECCV 2026

VisWordBench: Bridging the Gap in Cross-modal Reasoning for Multimodal Large Language Models

TIANSHU ZHANG, Junzhe Chen, Yean Cheng, Demin Zhu, Haoze Zheng, Lijie Wen

Although recent multimodal large language models (MLLMs)have advanced rapidly in vision–language reasoning, their capability bound-aries, particularly in cross-modal integration and reasoning, remain un-derexplored. Existing benchmarks primarily focus on evaluating uni-modal or loosely coupled multimodal abilities, leaving a gap in assess-ing complex cross-modal reasoning. To address this gap, we introduceVisWordBench, a benchmark comprising 2,625 English and 2,000 Chi-nese visual word puzzles with detailed human annotations. The bench-mark is designed to evaluate not only fundamental perceptual under-standing and world knowledge but also deep cross-modal reasoning skillsthat require consistently integrating visual and linguistic information.Through evaluations on VisWordBench, we find that current MLLMs ex-hibit under-diversified hypothesis search during reasoning. We thereforepropose a data construction pipeline and a training-free inference-timesteering strategy that promotes more diverse hypothesis exploration dur-ing CoT reasoning. Experimental results show consistent improvementsin reasoning quality and hypothesis diversity, supporting the utility ofVisWordBench for studying multimodal reasoning. The benchmark isavailable at: https://zenodo.org/records/20957955.

ECCV 2026

SGP2: Coarse-to-Fine Controllable Multimodal Remote Sensing Image Generation

Pengyu Chen, Xi Yang, Nannan Wang

Generating high-fidelity remote sensing images is severelychallenged by their specific overhead perspectives, complex structures,and rigorous physical attributes. Although recent controllable RS gen-eration methods have made progress, they are still limited by two keybottlenecks. The first is semantic conflict, where dominant global conceptsoften overshadow fine-grained details. The second is physical inconsis-tency, which easily leads to structural collapse and radiometric distortionduring the generation process. To address these issues, we propose SGP2(Synergizing Geometric and Physical Priors), a novel coarse-to-fine frame-work for controllable multimodal RS image generation. Specifically, toresolve semantic conflicts, we introduce the Grassmann Miner, whichconstructs dynamic geodesic trajectories on the Grassmann Manifold,enabling the model to gradually shift its focus from global backgrounds tofine-grained local details during the denoising process. To ensure physicalconsistency, we propose Physical Prior Expert, a module that dynamicallyaligns multi-level features from Visual Foundation Models and employsa frequency-aware Spectral Loss to enforce both amplitude and phaseconsistency. Furthermore, to bridge RS data gaps, we present MMEarth-1.5M, a 1.5 million text-image dataset featuring strictly co-registeredoptical and SAR pairs for robust cross-modal simulation. Our approach,without bells and whistles, achieves favorable performance in controllablemulti-modal remote sensing image generation task. The dataset will beavailable in https://github.com/cpy0029/MMEarth-1.5M.

ECCV 2026

Unlocking Few-Shot Capabilities in LVLMs via Prompt Conditioning and Head Selection

Adhémar de Senneville, Xavier Bou, Jeremy Anger, Rafael Grompone von Gioi, Gabriele Facciolo

Current Large Vision Language Models (LVLMs) excel atmany zero-shot tasks like image captioning, visual question answeringand OCR. However, these same models suffer from poor performance atimage classification tasks, underperforming against CLIP-based meth-ods. Notably, this gap is surprising because many LVLMs use CLIP-pretrained vision encoders. Yet LVLMs are not inherently limited byCLIP’s architecture with independent vision and text encoders. In CLIP,this separation biases classification toward class-name matching ratherthan joint visual–text reasoning. In this paper we show that, despite theirpoor raw performance, LVLMs can improve visual feature class separa-bility at inference using prompt conditioning, and LVLMs’ internal rep-resentations, especially attention heads, can outperform the model itselfat zero-shot and few-shot classification. We introduce Head EnsembleClassifiers (HEC) to bridge the performance gap between CLIP-basedand LVLM-based classification methods. Inspired by Gaussian Discrimi-nant Analysis, HEC ranks the most discriminative vision and text headsand combines them into a training-free classifier. We show that HECachieves state-of-the-art performance in few-shot and zero-shot classifi-cation across 12 datasets. Code: github.com/AdhemarDeSenneville/HEC

ECCV 2026

Selective Synergistic Learning for Video Object-Centric Learning

WonJun Moon, Jae-Pil Heo

Typical video object-centric learning (VOCL) approachesemploy slot-based frameworks that rely on reconstruction-driven en-coder–decoder architectures, where learning is mediated by two spatialmaps: attention maps from the encoder and object maps from the decoder.As these two distinct maps exhibit different properties, a recent densealignment strategy attempted to reconcile this discrepancy by enforcingagreement across all spatio-temporal patches via contrastive learning.However, this indiscriminate alignment inadvertently propagates the in-herent weaknesses of each module, such as noisy encoder predictionsand blurred decoder boundaries. Moreover, computing dense similaritiesacross all pairs incurs a computational cost quadratic in the total numberof spatio-temporal patches, severely limiting scalability. Motivated bythis, we propose Selective Synergistic Learning (SSync). Instead of ex-haustive patch-to-patch alignment, SSync prevents error propagation byselectively distilling only the most reliable cues: leveraging the encoderstrictly for boundary refinement and the decoder for interior denoising.This is realized via a pseudo-labeling with linear complexity, eliminatingthe need for quadratic spatial comparisons. Also, to prevent the rein-forcement of architectural biases like slot redundancy, we introduce atransitive pseudo-label merging that consolidates overlapping slots basedon spatio-temporal activation consistency. Extensive studies demonstratethat SSync improves decomposition quality and serves as a versatile,plug-and-play module while also exhibiting exceptional robustness to slotconfigurations. Code is available at github.com/wjun0830/SSync.

ECCV 2026

UniREditBench: A Unified Reasoning-based Image Editing Benchmark

feng han, Yibin Wang, Chenglin Li, Zheming Liang, Dianyi Wang, Yang Jiao, Zhipeng Wei, Chao Gong, Cheng Jin, Jiaqi Wang

Recent advances in multimodal generative models have drivensubstantial improvements in image editing. However, current generativemodels still struggle with handling diverse and complex image editingtasks that require implicit reasoning, underscoring the need for a compre-hensive benchmark to systematically assess their performance across var-ious reasoning scenarios. Existing benchmarks primarily focus on single-object attribute transformation in realistic scenarios, which, while effec-tive, encounter two key challenges: (1) they largely overlook multi-objectinteractions as well as game-world scenarios that involve human-definedrules, which are common in real-life applications; (2) they only rely ontextual references to evaluate the generated images, potentially leadingto systematic misjudgments, especially in complex reasoning scenarios.To this end, this work proposes UniREditBench, a unified benchmarkfor reasoning-based image editing evaluation. It comprises 2,700 metic-ulously curated samples, covering both real- and game-world scenariosacross 8 primary dimensions and 18 sub-dimensions. To improve evalua-tion reliability, we introduce multimodal dual-reference evaluation, pro-viding both textual and ground-truth image references for each sampleassessment. Furthermore, we design an automated multi-scenario datasynthesis pipeline and construct UniREdit-Data-100K, a large-scalesynthetic dataset with high-quality chain-of-thought (CoT) reasoning an-notations. We fine-tune Bagel on this dataset and develop UniREdit-Bagel, demonstrating substantial improvements in both in-domain andout-of-distribution settings. Through thorough benchmarking of bothopen-source and closed-source image editing models, we reveal theirstrengths and weaknesses across various aspects.

ECCV 2026

Unified Multi-plane Autoregressive Diffusion for 3D Multi-Contrast MRI Synthesis

Yejee Shin, Geonhui Son, Jinglu Wang, Minwoo Jung, Yan Lu, Dosik Hwang

Acquiring a complete set of magnetic resonance imaging (MRI)contrasts is time-intensive and uncomfortable for patients, despite thediagnostic value of multi-contrast imaging. This motivates synthesizingmissing contrasts from those already acquired, which is an inherently3D problem requiring anatomical coherence across axial, sagittal, andcoronal planes. However, fully 3D generative models are often impracti-cal under computational resources that scale cubically with volume size.We propose a unified Multi-Plane Autoregressive Diffusion (MPAD), alatent diffusion framework that achieves full-volume 3D synthesis usingefficient plane-wise 2D operations while preserving volumetric coherence.A 3D autoencoder first compresses MRI scans into an isotropic 3D la-tent representation. A 2D diffusion model is then trained to reconstructmasked latent slices of the target contrast, conditioned on both source-contrast slices and unmasked target-contrast slices. During inference,we introduce plane-wise autoregressive synthesis with inter-plane priors.Slices are generated autoregressively in random order within one planeorientation to maintain intra-plane continuity, then propagated as con-ditioning priors to orthogonal plane orientations to enforce inter-planeconsistency. Compared to 3D latent diffusion baselines, MPAD reducestraining and inference FLOPs by 7× and 3×, respectively, while alsolowering inference time and peak memory consumption. Experimentson multiple datasets demonstrate that MPAD achieves superior perfor-mance, generating high-fidelity 3D volumes and supporting one-to-manytranslation within a single unified model.

ECCV 2026

UHD-MFF: Shattering Barriers in Multi-Focus Ultra-High-Definition Image Fusion via Learnable Lookup Tables

Yibing Zhang, Xunpeng Yi, Qinglong Yan, Yeda Wang, Han Xu, Jiayi Ma

With the advancement of imaging technology, ultra-highdefinition images have become increasingly essential in modern visual applications. However, existing multi-focus image fusion remains largely confined to low-resolution images and faces three major barriers in UHD scenarios, namely data availability, model adaptability, and deployment feasibility, which severely hinder its practical application. To shatter these barriers, first, we propose the UHD-MFF dataset, the first largescale ultra-high-resolution multi-focus fusion dataset. Second, we propose a scale-specialized lookup-table framework tailored for ultra-highresolution images, termed as UMF-LUT. It consists of Coarse-Region Lookup Table (C-LUT) and Detail-Edge Lookup Table (D-LUT). Specifically, C-LUT performs joint queries of multiple gradient cues and semantic cues at low-resolution scales to enable region-level decision-making. Also, D-LUT operates at high-resolution scales, leveraging efficient Laplacian cues to provide complementary edge-level decision information. Such a design makes the model particularly well-suited for ultra-high-resolution multi-focus image fusion. Finally, it offers strong deployability with minimal computational overhead, enabling real-time 4K multi-focus fusion and showing promising potential for smartphone. Extensive experiments demonstrate that it outperforms SOTA methods in both visual fidelity and quantitative metrics. It effectively advances the development of multifocus image fusion toward ultra-high-resolution imaging scenarios. The code is available at https://github.com/zyb5/UHD-MFF.

ECCV 2026

Towards Unified World Models for Visual Navigation via Memory-Augmented Planning and Foresight

Yifei Dong, Fengyi Wu, Guangyu Chen, Lingdong Kong, Xu Zhu, Qiyu Hu, Yuxuan Zhou, Jingdong Sun, Jun-Yan He, Qi Dai, Alexander Hauptmann, Zhi-Qi Cheng

Enabling embodied agents to imagine future states is essen-tial for robust and generalizable visual navigation. Yet, state-of-the-artsystems typically rely on modular designs that decouple navigation plan-ning from visual world modeling, which often induces state–action mis-alignment and weak adaptability in novel or dynamic scenarios. We pro-pose UniWM, a unified, memory-augmented world model that integratesegocentric visual foresight and planning within a single multimodal au-toregressive backbone. UniWM explicitly grounds action selection in vi-sually imagined outcomes, tightly aligning prediction with control. Mean-while, a hierarchical memory mechanism fuses short-term perceptual cueswith longer-term trajectory context, supporting stable and coherent rea-soning over extended horizons. Extensive experiments on four challeng-ing benchmarks (Go Stanford, ReCon, SCAND, HuRoN) and the 1XHumanoid Dataset show that UniWM improves navigation success ratesby up to 30%, substantially reduces trajectory errors against strong base-lines, generalizes zero-shot to the unseen TartanDrive dataset, and scalesnaturally to high-dimensional humanoid navigation. These results po-sition UniWM as a principled step toward unified, imagination-drivenembodied navigation. All materials are committed to be open-sourced.

ECCV 2026

Towards Long-Form Spatio-Temporal Video Grounding

Xin Gu, Bing Fan, Jiali Yao, Zhipeng Zhang, Yan Huang, Cheng Han, Heng Fan, Libo Zhang

In real scenarios, the videos can span several minutes or evenhours, yet existing research on spatio-temporal video grounding (STVG),given a textual query, mainly focuses on localizing the target from a videoof tens of seconds, typically less than one minute, hindering its applica-tions. In this paper, we explore Long-Form STVG (LF-STVG), whichaims to locate the target from long-term videos. In LF-STVG, long-termvideos encompass a much longer temporal span and more irrelevant infor-mation, making it challenging for current short-form STVG approachesthat process all the frames at once. Addressing these, we propose a novelAutoRegressive Transformer architecture for LF-STVG, dubbed ART-STVG. Unlike current STVG methods requiring seeing the entire videosequence to make a full prediction at once, our ART-STVG regards thevideo as a streaming input and processes its frames sequentially, makingit capable of easily handling the long videos. To capture spatio-temporalcontext in ART-STVG, spatial and temporal memory banks are devel-oped and applied to decoders of ART-STVG. Considering that memoriesat different moments are not always relevant for localizing the target incurrent frame, we introduce simple yet effective memory selective strate-gies that enable the more relevant information for decoders, greatly im-proving the performance. Moreover, rather than parallelizing spatial andtemporal localization as done in existing approaches, we introduce a novelcascaded spatio-temporal design that connects spatial decoder to tempo-ral decoder during grounding, which allows ART-STVG to leverage morefine-grained target information to assist with complicated temporal lo-calization in complex long videos, further boosting performance. On thenewly extended datasets for LF-STVG, ART-STVG largely outperformscurrent approaches, while showing competitive results on Short-FormSTVG. Our code is at: xxx.

ECCV 2026

One-Step Flow Policy: Self-Distillation for Fast Visuomotor Policies

Shaolong Li, Lichao Sun, Yongchao Chen

Generative flow and diffusion models provide the continu-ous, multimodal action distributions needed for high-precision roboticpolicies. However, their reliance on iterative sampling introduces severeinference latency, degrading control frequency and harming performancein time-sensitive manipulation. To address this problem, we proposethe One-Step Flow Policy (OFP), a from-scratch self-distillation frame-work for high-fidelity, single-step action generation without a pre-trainedteacher. OFP unifies a self-consistency loss to enforce coherent transportacross time intervals, and a self-guided regularization to sharpen predic-tions toward high-density expert modes. In addition, a warm-start mech-anism leverages temporal action correlations to minimize the generativetransport distance. Evaluations across 56 diverse simulated manipulationtasks demonstrate that a one-step OFP achieves state-of-the-art results,outperforming 100-step diffusion and flow policies while accelerating ac-tion generation by over 100×. We further integrate OFP into the π0.5model on RoboTwin 2.0, where one-step OFP surpasses the original 10-step policy. These results establish OFP as a practical, scalable solutionfor highly accurate and low-latency robot control.

ECCV 2026

SynCity 3000: Bootstrapping Scene-Scale 3D Diffusion

Paul Engstler, Iro Laina, Christian Rupprecht, Andrea Vedaldi

We present SynCity 3000, a framework for generating 3Dscenes that are globally coherent while enabling fine-grained layout con-trol. Building on the ability of current image-to-3D generators to pro-duce complex 3D assets from a single image, we extend this capabilityto the scale of entire scenes by adapting the generator to be applicableas a convolutional operator. We achieve this by fine-tuning the modelon scene-like data generated by a new synthetic data engine, which wepropose to address the scarcity of 3D scene data for training. The convo-lutional generator is then applied to a dimetric image of the entire scene,generated from the user prompt, resulting in 3D scenes of arbitrary sizeand complexity. Across diverse prompts and layouts, SynCity 3000 pro-duces large, coherent, and detailed scenes, addressing the shortcomingsof prior approaches to 3D scene generation.

ECCV 2026

Surprise Forcing: What to Remember, When to Skip in Long Video Generation

Shuwei Shi, Zhen Li, Muyao Niu, Chuanhao Li, Bo Zheng, Kaipeng Zhang, zheng yinqiang

Autoregressive diffusion models have shown remarkable prog-ress in long video generation by producing frames causally. To boundmemory and computational cost, these models rely on a fixed-size sliding-window KV cache. While attention sinks retain a few initial frames astemporal anchors, the vast majority of historical context is irrecover-ably discarded as the window advances, severing long-range dependen-cies essential for scene consistency and narrative coherence. Meanwhile,the fixed multi-step denoising schedule applies uniform effort to everychunk, ignoring the varying demands imposed by diverse scene dynamicsacross temporal segments. In this paper, we propose Surprise Forcing,a training-free framework that addresses both bottlenecks. We introducea Surprise-Gated Memory Bank that scores each evicted frame by† Corresponding authors.its aggregate divergence from the bank and nearest-neighbor novelty,admitting only the most informative frames via a budget-normalizedgate that adapts to video dynamics. A priority-based eviction policykeeps the bank compact yet informative, while dynamic routing retrievesthe most relevant historical frames at attention time. We further pro-pose Surprise-Aware Denoising, which uses intra-chunk cosine dis-tance among adjacent frames after a single denoising step as a self-referential difficulty predictor, enabling adaptive step reduction for easychunks while preserving full refinement for complex ones. Experimentson VBench show that Surprise Forcing maintains robust narrative coher-ence in long videos without compromising real-time responsiveness.

ECCV 2026

StereoEdit: A Diffusion-Based Framework for Stereo-Consistent Image Editing

Baolin Liu, Zongyuan Yang, Yingde Song, yongping xiong

While recent diffusion models have achieved remarkable suc-cess in visual content editing, extending them to stereo image editingremains highly challenging due to the lack of explicit stereo geomet-ric consistency alignment, which leads to cross-view inconsistencies andgeometric distortions. We propose StereoEdit, a training-free diffusionframework designed to achieve geometrically consistent and structurallycoherent stereo editing. StereoEdit introduces two complementary mod-ules that explicitly enforce geometric reliability during the denoising pro-cess. The Geometrically-Gated Attention Reference (GGAR) adaptivelyfilters cross-view attention based on disparity-aware confidence, ensur-ing stable correspondences across epipolar regions, while the Geometry-Aware Latent Alignment (GALA) performs confidence-weighted, bidi-rectional latent corrections to maintain parallax alignment throughoutthe diffusion process. Together, these modules enable robust and consis-tent stereo edits. Extensive experiments on diverse stereo benchmarksdemonstrate that StereoEdit significantly improves cross-view coherenceand visual realism, establishing a new foundation for controllable andimmersive stereoscopic content editing.

ECCV 2026

SIFT: Self-Imagination Fine-Tuning for Physically Plausible Motion in Video Diffusion Models

Ruoyu Wang, Jialun Liu, Huayang Huang, Haibin Huang, Jiepeng Wang, Chi Zhang, Xuelong Li, Yu Wu

Recent advances in video diffusion models have greatly im-proved visual fidelity, yet their generated motions often violate physicalplausibility. We observe a common kinematic failure, “motion entangle-ment”, the unintended coupling of independent motion sources, such ascamera movement and object motion. We identify that this issue stemsfrom data bias and the reconstruction-based training design of diffusionmodels. Training on noisy videos that still retain coarse motion cues in-advertently encourages the model to replicate existing motion without anincentive to learn how to model kinematically-grounded motions. To ad-dress this, we propose a Self-Imagination Fine-Tuning (SIFT) paradigm,which enables the model to learn from its own generated videos ratherthan directly reconstructing real ones, breaking the reconstruction short-cut. We further employ motion-aware discriminative supervision and aprogressive hard-case replay strategy to stabilize and accelerate learning.By leveraging freely-generated text prompts, our method can denselycover a broad motion space, including rare or finely-disentangled scenar-ios that would be costly to collect as video data. Extensive experimentsdemonstrate that our approach substantially improves the physical real-ism, motion disentanglement, and controllability of generated videos.

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