paperlist.ai

AI conference papers

AI conference paper library

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

SynVAR: Synergizing Spatial and Semantic Alignment in Visual Autoregressive Model

Zhennan Chen, Tianxing Shi, Pengcheng Xu, Kepan Nan, Qian Wang, Zili Yi, Jian Yang, Ying Tai

VAR has gained widespread popularity due to its next-scaleprediction paradigm. However, it faces substantial performance bottle-necks when handling complex scenes with multiple objects and attributes.Existing diffusion-based enhancement methods fail to adequately addressthe unique challenge of cross-scale error propagation and accumulationin VAR. To this end, we propose SynVAR, the first training-free enhance-ment framework specifically tailored for the VAR paradigm, which intro-duces a spatial-semantic collaborative control strategy to effectively sup-press propagation error and improve generation quality. SynVAR com-prises three key components: (1) Global guidance to ensure reasonablespatial structure in the early stages, (2) Receptive field constraints tomitigate early-stage semantic confusion, (3) High-frequency compensa-tion to recover fine-grained details. Extensive quantitative and qualita-tive experiments demonstrate the significant improvements in the abilityof SynVAR to enhance the VAR’s capability for complex scene modeling.

ECCV 2026

Scientific Image Synthesis: Benchmarking, Methodologies, and Downstream Utility

Honglin Lin, Chonghan Qin, Zheng Liu, Qizhi Pei, Yu Li, Zhanping Zhong, Xin Gao, Wei Li, Wentao Zhang, Yanfeng Wang, Conghui He, Lijun Wu

While synthetic data has proven effective for improving sci-entific reasoning in the text domain, multimodal reasoning remains con-strained by the difficulty of synthesizing scientifically rigorous images.Existing Text-to-Image (T2I) models often produce outputs that arevisually plausible yet scientifically incorrect, resulting in a persistentvisual–logic divergence that limits their value for downstream reason-ing. Motivated by recent advances in next-generation T2I models, weconduct a systematic study of scientific image synthesis across genera-tion paradigms, evaluation, and downstream use. We examine both di-rect pixel-based generation and programmatic synthesis, and instantiateImgCoder as a structured implementation of the code-driven workflow,following an explicit “understand → plan → code” prompting strategyto encourage clearer structural specification. To rigorously assess scien-tific correctness, we introduce SciGenBench, which evaluates generatedimages based on information utility and logical validity. Our evaluationreveals systematic failure modes in pixel-based models and highlightsa fundamental expressiveness–precision trade-off. Finally, we show thatfine-tuning Large Multimodal Models (LMMs) on rigorously verified syn-thetic scientific images yields consistent reasoning gains, with potentialscaling trends analogous to the text domain, validating high-fidelity sci-entific synthesis as a viable path to unlocking massive multimodal rea-soning capabilities.

ECCV 2026

SymbOmni: Evolving Agentic Omni Models via Symbolic Concept Learning

Jinxiu Liu, Jianru Li, Tanqing Kuang, Xuanming Liu, Kangfu Mei, Yandong Wen, Weiyang Liu

Visual generation is increasingly ubiquitous in diverse do-mains, from text-to-image/video synthesis to multimodal interactive cre-ation. Yet prevailing monolithic models remain fundamentally constrainedby their inability to learn cumulatively and evolve autonomously, whichis a limitation we term the “perpetual novice” problem. They lack mech-anisms for structuring experience into reusable knowledge and there-fore rely on brittle, “from-scratch” reasoning for each task, resultingin poor compositional generalization and inefficient knowledge reten-tion. Motivated by these limitations, we propose SymbOmni, an agenticomni-model designed for cumulative evolution through Symbolic Con-cept Learning. At its core is the Symbolic Concept Box, an optimizablememory module that abstracts low-level operations into reusable Sym-bolic Workflow Instructions. SymbOmni operates through an induction-transduction cycle: experiences are abstracted into symbolic concepts(induction), which are then adaptively composed to solve novel tasks(transduction). The training is done by verbalized backpropagation withlanguage-based feedback to enable continuous self-improvement withoutgradient-based model fine-tuning. Comprehensive experiments validatethat (I) SymbOmni significantly outperforms existing agent-based sys-tems for iterative creation and also surpasses closed-source models (e.g.,Nano Banana, GPT-Image-1) in both image quality and task successrates; (II) SymbOmni effectively reduces token consumption by over 40%while maintaining competitive generation quality; and (III) SymbOmnienables effective continual learning by achieving cumulative gains acrossmultiple online-learning benchmarks and setting a new state of the art.

ECCV 2026

SharpGS: Sharpness-Preserving 3D Gaussian Splatting with Differentiable Blur-Driven Density Control

Moonsoo Jeong, Dongbeen Kim, Minseong Kim, Sungkil Lee

We present SharpGS, a differentiable blur-driven density con-trol, which enhances the quality of 3D Gaussian Splatting (3DGS). Stan-dard 3DGS often struggles to capture intricate details, particularly intextured patterns and object boundaries, due to the limited sensitivityof its density control. Simple finer densification can result in excessiveprimitive counts with marginal quality gain. To address this, we intro-duce blur as an effective perceptual cue, leveraging CUDA-based differ-entiable blur. Our density control estimates per-primitive blur levels bycomparing reconstructions to blurred ground-truth images, and identifieshigh-frequency regions where additional primitives are required. Whilethis naturally produces more primitives, we counterbalance this by sup-pressing redundant primitives. Specifically, we penalize the blur levelsof inherently smooth regions such as sky, and regularize the opacitiesof potential split/clone candidates. We experimentally demonstrate thatSharpGS greatly improves the state-of-the-art 3DGS methods in termsof quality, while keeping learned primitives compact.

ECCV 2026

SGMatch: Semantic-Guided Non-Rigid Shape Matching with Flow Regularization

Tianwei Ye, Xiaoguang Mei, Yifan Xia, Fan Fan, Jun Huang, Jiayi Ma

Establishing accurate point-to-point correspondences betweennon-rigid 3D shapes remains a critical challenge, particularly under non-isometric deformations and topological noise. Existing functional mappipelines suffer from ambiguities that geometric descriptors alone can-not resolve, and spatial inconsistencies inherent in the projection of trun-cated spectral bases to dense pointwise correspondences. In this paper,we introduce SGMatch, a learning-based framework that couples 3D-lifted semantic cues with trajectory-level feature transport regulariza-tion. Specifically, we design a Semantic-Guided Local Cross-Attentionmodule that integrates semantic features from vision foundation modelsinto geometric descriptors while preserving local structural continuity.Furthermore, we adapt conditional flow matching as a time-conditionedfeature transport regularizer that promotes spatially coherent point-wiserecovery. Experimental results on multiple benchmarks demonstrate thatSGMatch achieves competitive performance across near-isometric set-tings and consistent improvements under non-isometric deformations andtopological noise.

ECCV 2026

Seeing Through Circuits: Faithful Mechanistic Interpretability for Vision Transformers

Nina Żukowska, Wolfgang Stammer, Bernt Schiele, Jonas Fischer

Transparency of neural networks’ internal reasoning is atthe heart of interpretability research, adding to trust, safety, and under-standing of these models. The field of mechanistic interpretability hasrecently focused on studying task-specific computational graphs, definedby connections (edges) between model components. Such edge-basedcircuits have been defined in the context of large language models, yetvision-based approaches so far only consider neuron-based circuits. Thesetell which information is encoded, but not how it is routed through thecomplex wiring of a neural network. In this work, we investigate whetheruseful mechanistic circuits can be identified through computational graphsin vision transformers. We propose an effective method for AutomaticVisual Circuit Discovery (Vi-CD) that recovers class-specific circuitsfor classification, identifies circuits underlying typographic attacks inCLIP, and discovers circuits that lend themselves for steering to correctharmful model behaviour. Overall, we find that insightful and actionableedge-based circuits can be recovered from vision transformers, addingtransparency to the internal computations of these models.

ECCV 2026

Qwen-3D: A Generalist 3D Vision-Language Model for Spatial Understanding

Lucy Lin, Ayush Jain, Yifan Liu, Katerina Fragkiadaki

Large Multimodal Models (LMMs) have achieved remark-able success on images and short videos, yet scaling them to long videosremains challenging due to frame-centric tokenization and limited con-text windows. 3D geometry provides a natural compression mechanismfor visual streams: depth and camera pose enable observations from mul-tiple views and time steps to be fused into a persistent, world-aligned rep-resentation. While recent 3D LMMs leverage geometry-aware representa-tions to improve spatial reasoning, they continue to lag behind specialist3D perception systems on grounding and segmentation tasks. We arguethat a key limitation is geometry-aware decoding: existing methods com-municate 3D predictions through language tokens, proposal selection, orlightweight grounding queries, creating a bottleneck between languagereasoning and dense geometric prediction. Building on these insights, weintroduce Qwen-3D, a geometry-aware LMM that compresses visual in-formation within the Qwen backbone using multi-view geometric cues,enabling efficient long-horizon visual reasoning over static scenes. Qwen-3D augments visual tokens with 3D Rotary Positional Embeddings, al-lowing attention to operate directly in 3D scene space rather than acrossindependent image frames and thereby facilitating scalable cross-viewand temporal reasoning. To bridge language and geometry, Qwen-3D in-corporates a query-based segmentation decoder that grounds languagedirectly in the underlying 3D scene representation, unifying referentialgrounding, instance segmentation, and visual question answering acrossboth images and videos. Across a diverse set of benchmarks, Qwen-3Dsurpasses existing 3D LMMs and outperforms several large proprietary2D models. Notably, Qwen-3D achieves these improvements while main-taining strong performance on standard 2D vision–language benchmarksby jointly training on 2D and 3D data. Our code and checkpoints can befound at the project website https://qwen-3d.github.io/.

ECCV 2026

PS-MOT: Cultivating Instance Awareness from Point Seeds for Multi-Object Tracking

Kai Luo, Fei Teng, Mengfei Duan, Wanjun Jia, Xu Wang, Hao Shi, Kunyu Peng, Zhiyong Li, Kailun Yang

We introduce Point-supervised Multi-Object Tracking (PS-MOT) as a cost-effective alternative to traditional bounding box su-pervision, shifting the focus from spatial fitting to topological center-driven representation. However, PS-MOT faces challenges, e.g., spatialambiguity and identity drift due to the lack of explicit geometric struc-ture and scale constraints. To address these, we propose PS-Track, ahierarchical pipeline transitioning from points to instances across data,model, and loss levels. At the data level, we introduce Temporal-FeedbackPrompting (TFP) to evolve points into temporally consistent pseudo-labels using negative spatial cues and motion priors. At the model level,we design the Point-Excited Wavelet Attention (PEWA) module, whichleverages semantic correlations to activate high-frequency components,“hallucinating” object boundaries. At the loss level, Uncertainty-GuidedGaussian Learning (UGL) models pseudo-labels as probabilistic distri-butions, dynamically calibrating supervision intensity. Experiments onDanceTrack, EmboTrack, SportsMOT, and JRDB demonstrate that PS-Track provides a feasible and effective point-supervised alternative acrossdiverse tracking scenarios, establishing a new state-of-the-art for point-supervised tracking. The source code is available at https://github.com/xifen523/PS-MOT.

ECCV 2026

Layout-Conditioned Autoregressive Text-to-Image Generation via Structured Masking

Zirui Zheng, Takashi Isobe, Tong Shen, Xu Jia, Xiaomin Li, Jianbin Zhao, mengmeng Ge, Baolu Li, Qinghe Wang, Haiwen Diao, Dong Li, Yunzhi Zhuge, Dong Zhou, Huchuan Lu, Emad Barsoum

Although autoregressive (AR) models have demonstrated re-markable success in image generation, extending these models to layout-conditioned generation remains challenging due to the sparse natureof layout conditions and the risk of feature entanglement. We presentStructured Masking for AR-based Layout-to-Image (SMARLI), a novelframework that effectively integrates spatial layout constraints into theAR generation process. To equip AR models with layout control, a struc-tured masking strategy is applied to the attention computation to governthe interaction among the global prompt, layout, and image tokens. Thisdesign prevents the misassociation of different regions with their corre-sponding descriptions while enabling the sufficient injection of layoutconstraints into the generation process. To alleviate the exposure bias ofAR models and further enhance generation quality and layout accuracy,we incorporate a Group Relative Policy Optimization (GRPO) post-training scheme. We adapt it to the next-set-based paradigm and intro-duce a specifically designed layout reward, which is coordinated with animage quality reward to guide policy optimization in a balanced manner.Experimental results demonstrate that SMARLI seamlessly integrateslayout tokens with text and image tokens without compromising genera-tion quality, and the proposed masking strategy and post-training schemecan also be transferred to standard next-token-based AR models. Theproposed framework achieves superior layout control while maintainingthe structural simplicity and generation efficiency of AR models.

ECCV 2026

CoCo: Code as CoT for Text-to-Image Preview and Rare Concept Generation

Li Haodong, Chunmei Qing, Huanyu Zhang, Dongzhi Jiang, Yihang Zou, Hongbo Peng, Dingming Li, Yuhong Dai, ZePeng Lin, Juanxi Tian, Yi Zhou, Siqi Dai

Recent advancements in Unified Multimodal Models (UMMs)have significantly advanced text-to-image (T2I) generation, particularlythrough the integration of Chain-of-Thought (CoT) reasoning. How-ever, existing CoT-based T2I methods largely rely on abstract natural-language planning, which lacks the precision required for complex spa-tial layouts, structured visual elements, and dense textual content. In thiswork, we propose CoCo (Code-as-CoT), a code-driven reasoning frame-work that represents the reasoning process as executable code, enablingexplicit and verifiable intermediate planning for image generation. Givena text prompt, CoCo first generates executable code that specifies thestructural layout of the scene, which is then executed in a sandboxed en-vironment to render a deterministic draft image. The model subsequentlyrefines this draft through fine-grained image editing to produce the fi-nal high-fidelity result. To support this training paradigm, we constructCoCo-10K, a curated dataset containing structured draft–final imagepairs designed to teach both structured draft construction and correc-tive visual refinement. Empirical evaluations on StructT2IBench, OneIG-Bench, and LongText-Bench show that CoCo achieves improvements of+68.83%, +54.8%, and +41.23% over direct generation, while also out-performing other generation methods empowered by CoT. These resultsdemonstrate that executable code is an effective and reliable reasoningparadigm for precise, controllable, and structured text-to-image genera-tion. The code is available at: https://github.com/micky-li-hd/CoCo

ECCV 2026

Progressive Pose-Guided 4D Animal Reconstruction from Monocular Video

Siyuan Li, Weiying Chen, Yilin Wang, Xinxin Zuo, Xingyu Li, Li Cheng

Reconstructing 4D animals from monocular videos is chal-lenging due to large inter-species variation, complex articulations, andthe lack of reliable templates. Existing approaches typically rely on eitherstrict category-specific priors that restrict generalization, or unconstrainedgenerative models that sacrifice input fidelity. To bridge this gap, wepresent a progressive test-time optimization framework built on 3D Gaus-sian Splatting for high-fidelity 4D animal reconstruction from a singlevideo. Our key insight is that a coarse shape prior suffices when coupledwith a progressive strategy that disentangles articulated pose from non-rigid deformation. Specifically, we employ a symmetry-aware temporalencoding that exploits bilateral cues while absorbing camera estimationdrift and a part-conditioned deformation mechanism guided by learn-able part anchors and a learnable skinning field. Extensive experimentsdemonstrate that our approach generalizes robustly across diverse species,achieving superior geometric accuracy, temporal consistency, and visualfidelity compared to existing baselines, even under severe prior mismatch.Project page: https://syl-322.github.io/ReWild4D/

ECCV 2026

PhysPO: Physics-Aware Local Preference Optimization for Physically Consistent Video Diffusion

Zhuoran Yang, Yanyong Zhang

Despite rapid progress in video diffusion models (VDMs),ensuring semantic adherence and physical commonsense remains a fun-damental challenge, as even state-of-the-art systems frequently violatereal-world physical laws, such as dynamics, thermodynamics, and op-tics. While Direct Preference Optimization (DPO) has become a popu-lar post-training strategy for aligning generative models, existing videoDPO methods to improve physical commonsense suffer from high compu-tational costs, poorly matched preference pairs, and ambiguous global su-pervision that fails to localize physical violations. We propose PhysPO,a physics-aware local preference optimization framework for physicallyconsistent video generation. We first introduce CounterPhyPipe, a physics-aware counterfactual data construction pipeline that forms video pref-erence pairs with consistent global semantics and local physical viola-tions, enabling meaningful comparisons for preference learning. Thenwe leverage high-frequency decomposition to introduce a physical mask,which localizes regions where physical state transitions may occur. Fi-nally, we develop PhysPO, a physics-aware DPO framework that con-centrates supervision on physically relevant regions while enforcing neu-trality on background regions. This mechanism reduces gradient noiseand mitigates shortcut optimization, encouraging the model to focus ongenuine physical discrepancies rather than superficial cues. Extensiveexperiments demonstrate that PhysPO significantly improves physicalcommonsense without compromising semantic adherence. Our projectpage is here3 .

ECCV 2026

Parallelized Autoregressive Decoding for Omni-Modal Dense Video Captioning

Wenzheng Zeng, Siyi Jiao, Chen Gao, Hwee Tou Ng, Mike Zheng Shou

Dense video captioning aims to generate temporally groundeddescriptions of video events, benefiting both event-level video understand-ing and generation. In this domain, autoregressive video large languagemodels have emerged as a prevalent paradigm due to their strong gen-erative and cross-modal modeling capacity. However, generating densecaptions under the token-by-token paradigm severely limits inference effi-ciency and hinders scalability as video length and event density increase.In this work, we propose a parallelized autoregressive framework thatnot only improves generation efficiency but also enhances temporallygrounded captioning performance. Our key insight is to exploit the weaklocal dependencies across temporally distinct events to restructure thecausal dependency graph, thereby enabling lossless parallel generation.Specifically, tokens with weak cross-event dependencies can be decoded inparallel, while tightly coupled tokens within each event retain sequentialdecoding to preserve local semantic coherence. To realize this insight, weintroduce two key components for lossless parallel decoding: (1) a latentglobal planning mechanism that automatically learns the event-level struc-ture and produces compact tokens encoding global inter-event causalitywhile adaptively aggregating event-level audio-visual semantics, guidingsubsequent dependency restructuring and parallel decoding; and (2) anevent-factorized parallel decoding mechanism that effectively balanceslocal focus with global inter-event awareness. Experiments on variousbenchmarks demonstrate the clear advantage of our approach in bothefficiency and performance in omni-modal event grounding and captioning.Project website: https://github.com/showlab/PadCaptioner.

ECCV 2026

PanoGrounder: Bridging 2D and 3D with Panoramic Scene Representations for VLM-based 3D Visual Grounding

Seongmin Jung, Seongho Choi, Gunwoo Jeon, Minsu Cho, Jongwoo Lim

3D Visual Grounding (3DVG) is a critical bridge from vision-language perception to robotics, requiring both language understandingand 3D scene reasoning. Traditional supervised models leverage explicit3D geometry but exhibit limited generalization, owing to the scarcity of3D vision-language datasets and the limited reasoning capabilities com-pared to modern vision-language models (VLMs). We propose a gener-alizable 3DVG framework, PanoGrounder, that couples multi-modalpanoramic representation with pretrained 2D VLMs for strong vision-language reasoning. Panoramic renderings, augmented with 3D semanticand geometric features, serve as an intermediate representation between2D and 3D, and offer two major benefits: (i) they can be directly fed toVLMs with minimal adaptation and (ii) they retain long-range object-to-object relations thanks to their 360-degree field of view. We devise athree-stage pipeline that places a compact set of panoramic viewpointsconsidering the scene layout and geometry, grounds a text query on eachpanoramic rendering with a VLM, and fuses per-view predictions into asingle 3D bounding box via lifting. Our approach achieves state-of-the-artresults on ScanRefer and Nr3D, and demonstrates strong generalizationto unseen 3D datasets and text rephrasings.

ECCV 2026

ORFC: Orthogonal Reparameterization for Low-Bitrate ViT Feature Coding

Letian Zhang, Wenhan Yang, LINGYU DUAN

Existing feature coding methods typically minimize feature-space reconstruction error, implicitly assuming that preserving featuresimilarity ensures downstream task performance. However, in VisionTransformers (ViTs), global correlation mixing makes downstream pre-dictions more sensitive to small feature perturbations, so similar fea-ture distortion can lead to significantly different task performance. Inparticular, our analysis reveals that under fixed-group product quanti-zation, task sensitivity is highly uneven across channel subspaces. Con-sequently, near-uniform per-group rates may waste bits on insensitivedirections while leaving critical ones underrepresented, causing rapidtask-performance degradation at low bitrate. To address this issue, wepropose an Orthogonal Reparameterized product quantization for inter-mediate Feature Coding (ORFC). It introduces a learnable orthogonaltransform that rotates the representation before grouped quantization,enabling fixed-size groups to better align with task-relevant directions.We further use the output deviation of the remaining layers as a proxydistortion, and optimize an entropy-constrained rate-distortion objectiveto jointly learn the orthogonal transform and grouped quantization in anend-to-end manner. Extensive experiments across multiple ViT architec-tures and downstream tasks demonstrate that ORFC achieves a superiorrate-task trade-off, especially in the low-bitrate regime. The code is avail-able at https://github.com/zhangletian2/ORFC.

ECCV 2026

Optimizing Mesh Animation from Video via Shape Flow Guidance

Jingqiao Xiu, Yicong Li, Angela Yao

Optimizing vertex deformations from video for mesh anima-tion is constrained by rendering-based reconstruction losses. While ex-isting approaches improve mesh representations, supervision signals, oranimation paradigms, their supervision remains confined to the 2D do-main. Such 2D supervision is limited: it provides no signal for occludedregions and only indirect cues for visible areas. Consequently, these meth-ods often suffer from severe shape and motion artifacts. To address thislimitation, we propose Shape Flow Guidance (SFG), a sequence of 3Dshapes derived from videos, which serves as explicit 3D supervision formesh animation. Specifically, SFG is elicited by intervening in the sam-pling process of a pretrained mesh generator in a training-free manner.We further tailor a skeletal animation model that separates local defor-mation from global transformation. This model enables SFG to guidecomplex local motion while reserving rendering-based losses for simpleglobal motion. Extensive experiments confirm that our method signifi-cantly outperforms prior work qualitatively, quantitatively, and in termsof processing speed. Qualitative results are available on our project page:https://sfgmesh.pages.dev/.

ECCV 2026

NeuralGarSim: Geometry-agnostic Garment Simulation with Neural Fields

Arihant Gaur, Navami Kairanda, Christian Theobalt, Vladislav Golyanik

Most existing mesh-based methods for garment simulationsuffer from sensitivities tied to mesh discretisation and resolution. Re-cent approaches such as NeuralClothSim employ continuous neural fieldsas a promising alternative, but remain constrained by a 2D curvilinearparameterisation that models a single cloth panel, limiting their abil-ity to represent garments with multiple panels stitched together. Thispaper introduces NeuralGarSim, a quasistatic garment simulator for-mulated directly in 3D Euclidean space. Our method accepts diverseinput representations—including distance fields, meshes, point clouds,and Gaussians—as undeformed garment states, making it compatible withoff-the-shelf reconstruction frameworks without additional preprocessing.We then represent the garment deformation as a neural field and define anonlinear Kirchhoff–Love shell model directly in R3 by applying tangentialdifferential calculus on the garment surface. This ensures that the neuralgarment simulation is parameterisation-space-free and naturally extendsto arbitrary topologies. By minimising a potential energy functional, ourmethod learns a 3D neural deformation field that predicts physicallyconsistent deformations across garments with multiple panels, seams,and holes. It supports multiple material models and trains 2–4× fasterthan its predecessor, NeuralClothSim, while retaining the continuous,consistent, and memory-adaptive behaviour1 .

ECCV 2026

NaP-Control: Navigating Diffusion Prior for Versatile and Fast Character Control

Chia-Wen Chen, Yan Wu, Korrawe Karunratanakul, Siyu Tang

Achieving precise, versatile whole-body character control inphysics-based animation remains challenging. Recent diffusion-based poli-cies generate rich and expressive motions but typically rely on gradient-based test-time guidance to satisfy task objectives, which is slow andcan reduce robustness. We introduce NaP-Control (Navigating Diffu-sion Prior for Versatile and Fast Character Control), abbreviated asNaP. Our method uses reinforcement learning to manipulate the latentnoise of a task-agnostic diffusion policy prior, steering it toward task-specific behaviors for fast, robust control with high motion fidelity. Incontrast to methods that rely solely on offline training, NaP interactswith the environment during training to correct motions and optimizetask rewards, improving success rates and enabling adaptation to chal-lenging scenarios. By directly predicting task-optimized diffusion noise,NaP eliminates iterative guidance during denoising and enables efficientinference. Experiments show that NaP attains higher success rates andfaster inference while preserving natural motion across diverse tasks.

ECCV 2026

MotionChain: Fine-Grained Video Motion Understanding via Structured Decomposition

Hao Yang, bo xu, Jun Dan, Sijia Chen, Baigui Sun, Yang Liu

Recent Vision-Language Models (VLMs) have demonstrated significant progress in video understanding. However, we notice that VLMs perform coarse perception and reasoning when answering finegrained motion questions. We investigate the reasons for this phenomenon and discover that motions in video change rapidly, so fixed frame rate sampling easily misses inter-frame motion details. Moreover, when answering motion-centric questions, VLMs tend to introduce motion-irrelevant contextual information and lack a structured and precise representation of motions, making it difficult to distinguish what motion the subject performs and when. To address these problems, we propose MotionChain, a training-free method that decouples motion question answering into a sequence of ⟨time range, subject, motion, environment⟩ tuples. Concretely, MotionChain leverages optical flow variations to calibrate when motions occur and extracts motion-relevant key frames as supplementary input, effectively alleviating the problem of missing critical motion information. For fine-grained motion question answering (QA), each tuple element in MotionChain can be converted into a visually grounded claim, providing the model with visual evidence. Experiments on motion-centric video QA benchmarks demonstrate the superiority of our MotionChain.

ECCV 2026

LIIFusion: Coarse-to-fine Framework for Generative MEF via Implicit Neural Representation

Sangmin Han, Jinho Kim, Jinwoo Kim, Dongyoung Kim, Seon Joo Kim

Multi-exposure fusion (MEF) expands the luminance rangebeyond what a single exposure can capture. Combining images takenat different exposure levels requires handling geometric differences whilenaturally merging their complementary brightness information. It of-ten demands generative completion where details are missing. Diffusion-based generative methods address these challenges, however, they arecomputationally expensive and struggle to preserve fine structures in sat-urated regions. We propose LIIFusion, a coarse-to-fine framework thatbalances fusion quality and efficiency in generative MEF. The coarsestage performs low resolution generative fusion, enhanced by an adaptiveexposure correction that recovers structure lost in saturated over-exposedareas. The fine stage adapts a local implicit image function into a multi-exposure fusion function: conditioned on the HR OE/UE sources and thecoarse output, it queries arbitrary target coordinates and fuses sourceevidence regard- less of the HR input resolution. LIIFusion achieves upto 3.5× speed-up over existing generative methods while maintainingor improving structural fidelity and perceptual quality. We believe thisframework provides an effective pathway toward making generative MEFmore practical in real-world applications.

Keep browsing

More papers from top AI conferences

30+ top conferences, by conference, year, and topic.