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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

Conditional Flow Matching for Visually-Guided Acoustic Highlighting

Hugo Malard, Gael Le Lan, Daniel Wong, David Alon, YI-CHIAO WU, Sanjeel Parekh

Visually-guided acoustic highlighting seeks to rebalance au-dio in alignment with the accompanying video, creating a coherent au-dio–visual experience. While visual saliency and enhancement have beenwidely studied, acoustic highlighting remains underexplored, often leadingto misalignment between visual and auditory focus. Existing approachesuse discriminative models, which struggle with the inherent ambiguityin audio remixing, where no natural one-to-one mapping exists betweenpoorly-balanced and well-balanced audio mixes. To address this limitation,we reframe this task as a generative problem and introduce a ConditionalFlow Matching (CFM) framework. A key challenge in iterative flow-basedgeneration is that early prediction errors — in selecting the correct sourceto enhance — compound over steps and push trajectories off-manifold. Toaddress this, we introduce a rollout loss that penalizes drift at the finalstep, encouraging self-correcting trajectories and stabilizing long-rangeflow integration. We further propose a conditioning module that fusesaudio and visual cues before vector field regression, enabling explicit cross-modal source selection. Extensive quantitative and qualitative evaluationsshow that our method consistently surpasses the previous state-of-the-artdiscriminative approach, establishing that visually-guided audio remixingis best addressed through generative modeling. Qualitative samples areavailable at the project page.

ECCV 2026

Fill2SR: Repurposing Inpainting Diffusion Transformers for Real-World Super-Resolution

Xingfu Yi, Xiaoxue Yu

Recent real-world image super-resolution (SR) methods often adapt text-to-image (T2I) backbones with ControlNet-style branches or spatial conditioning tokens, which increases memory and computes with resolution and often constrains training to a fixed scale. We propose Fill2SR, which repurposes a masked-inpainting Diffusion Transformer for SR without extra spatial branches. Our Inpainting-Interface Evidence Adapter (IIEA) writes the low-quality (LQ) observation into the native masked-image slot under a full-image mask, turning inpainting into a reverse-degradation conditional rectified flow trained with LoRA-only tuning. We further introduce RCDT, an offline pipeline that distills degradation descriptors from unpaired real images and transfers them onto clean targets using frozen open-source models. Fill2SR supports mixed-resolution training up to QHD and yields stable performance across 512/1024/2048 outputs. On synthetic benchmarks, our base model with IIEA achieves the best LPIPS on DIV2K and LSDIR; adding RCDT trades a small LPIPS drop for consistently stronger no-reference quality on RealLQ250 and RealPhoto60. Fill2SR remains memory-predictable, running 15362 inference on a single 32GB GPU and extending to multimegapixel outputs via tiled restoration.

ECCV 2026

Event-LiDAR: 3D Eventification for Efficient Point Cloud Processing

Masatoshi Murakami, Eisho Tsuji, Ken Sakurada

We propose Event-LiDAR* , a 3D eventification frameworkthat converts conventional LiDAR scans into temporally sparse repre-sentations for efficient point cloud processing. Unlike dense multi-scanprocessing, naive frame differencing, or correspondence-based residuals,3D event extraction is formulated as a temporal estimation problemunder sparse, viewpoint-dependent observations. Short-term geometricevolution across consecutive scans is approximated with a first-ordergeometric model, and deviations unexplained by this model are treatedas events. This yields compact, information-preserving representationsthat suppress redundancy while retaining changes unpredictable by thefirst-order model. Event-LiDAR is applied to LiDAR-based 3D objectdetection with existing backbones, and an event-aware network designis further introduced to reallocate modeling capacity toward the inputstage for sparse inputs. On nuScenes, under the 1F+9T setting witha 72% point reduction, Event-LiDAR maintains accuracy comparableto full-scan baselines while achieving 23% faster end-to-end inferenceon PTv3, driven in part by a 32% speedup in its feature extractionbackbone. The gains generalize across backbones, with a 9% end-to-endspeedup on CenterPoint under the same setting. Training is likewiseaccelerated by 17% under the same setting. Event-LiDAR thus serves asa low-latency, architecture-agnostic geometric preprocessor inspired byevent-based sensing, providing a practical front-end for efficient 3D pointcloud perception.

ECCV 2026

Error-Driven Scene Editing for 3D Grounding in Large Language Models

Yue Zhang, Zun Wang, Han Lin, Jialu Li, Jianing Yang, Yonatan Bitton, Idan Szpektor, Mohit Bansal

Despite recent progress in 3D-LLMs, they remain limitedin accurately grounding language to visual and spatial elements in 3Denvironments. This limitation stems in part from training data that fo-cuses on language reasoning rather than spatial understanding due toscarce 3D resources, leaving inherent grounding biases unresolved. Toaddress this, we propose 3D scene editing as a key mechanism to gener-ate visual counterfactuals that mitigate these biases through fine-grainedspatial manipulation, without requiring costly scene reconstruction orlarge-scale 3D data collection. Furthermore, to make these edits targetedand directly address the specific weaknesses of the model, we introduceDEER-3D, an error-driven framework that diagnoses grounding failuresand generates targeted counterfactual training supervision via a struc-tured “Decompose, Diagnose, Edit, and Retrain” loop. Specifically, givena grounding failure, DEER-3D first identifies the predicate-level error(e.g., attribute or spatial relation). It then performs minimal predicate-aligned scene edits, such as recoloring or repositioning, and constructsaligned question–answer pairs that explicitly target the failed predicate,forming targeted counterfactual training examples. We evaluate our edit-ing pipeline across multiple benchmarks for 3D grounding and scene un-derstanding tasks, consistently demonstrating improvements across allgrounding datasets through iterative refinement (4-6% gains). DEER-3D underscores the effectiveness of targeted, error-driven scene editingin bridging linguistic reasoning with spatial grounding in 3D LLMs.

ECCV 2026

EmbodiedHead: Real-Time Listening and Speaking Avatar for Conversational Agents

Yu Zhang, Kaiyuan Shen, Yang Li

We present EmbodiedHead, a speech-driven talking headframework that equips LLMs with real-time visual avatars for conver-sation. A practical embodied avatar must achieve real-time generation,unified listening-speaking behavior, and high rendered visual quality si-multaneously. Our framework couples the first Rectified-Flow DiffusionTransformer (DiT) for this task with a differentiable renderer, enablingdiverse, high-fidelity generation in as few as four sampling steps. Priorlistening-speaking methods rely on dual-stream audio, introducing an in-terlocutor look-ahead dependency incompatible with causal user–LLMinteraction. We instead adopt a single-stream interface with explicitper-frame listening-speaking state conditioning and a Streaming AudioScheduler, suppressing spurious mouth motion during listening while en-abling seamless turn-taking. A two-stage training scheme of coefficient-space pretraining and joint image-domain refinement further closes thegap between motion-level supervision and rendered quality. Extensive ex-periments demonstrate state-of-the-art visual quality and motion fidelityin both speaking and listening scenarios.

ECCV 2026

ECTraj: Enhanced Consistency Training for Multi-Agent Trajectory Prediction

Alen Mrdovic, Qingze Liu, Danrui Li, Mathew Schwartz, Kaidong Hu, Sejong Yoon, Mubbasir Kapadia, Vladimir Pavlovic

Diffusion models for multi-agent trajectory prediction arelimited by iterative denoising, which causes inference latency that hinderstheir use in time-critical settings like autonomous driving. Fast-samplingvariants using DDIM and informed initial noise distribution partiallyalleviate this issue, but they either fail to achieve true single-step gen-eration or are constrained by the chosen noise distribution. ConsistencyModels (CMs) offer high-quality one-step generation by mapping noisedirectly to data, but are difficult to train from scratch. We propose EC-Traj , an enhanced CM pipeline with improved training and conditionalgeneration for trajectory prediction. Our framework extends the student-teacher consistency training scheme: the student produces standard out-puts, while the teacher explicitly fuses its predictions with parts of theground truth to give stronger supervision. We also exploit CMs’ directdenoising for top-K multi-shot generation during training. Combiningconditional generation with this enhanced consistency objective yieldsfaster inference and improved prediction accuracy, establishing compet-itive new benchmarks on the large-scale Argoverse 2 dataset.3

ECCV 2026

EAGS: Error-Aware Gaussian Splatting with Dual-Confidence-Guided Modeling for Uncalibrated Driving Scenes

Cen Zhigang, Ningyan Guo, Yulan Guo, Yifan Ge, Zhiyong Feng

Feed-forward 3D reconstruction for autonomous driving hasdemonstrated strong performance and promising generalization capabil-ities. However, pixel-level methods often suffer from multi-view inconsis-tencies and content duplication. While voxel-level approaches alleviatethese issues by predicting 3D Gaussian primitives in voxel space, theyremain limited by resolution and computational overhead. We introducean efficient feed-forward framework for scene reconstruction from uncal-ibrated images. The key insight is the explicit decoupling of instanta-neous generation necessity and long-term geometric persistence duringGaussian generation. Specifically, we introduce Generation Confidencedriven by reprojection errors to enable demand-aware Gaussian expan-sion, and Reuse Confidence constructed from depth uncertainty and tem-poral decay to model the long-term stability. To balance fidelity andsparsity, we employ differentiable voxelization to aggregate neighboringfeatures and suppress spatial redundancy, followed by confidence-awaresoft-gating pruning. Gaussian distillation is further introduced to enablea compact set of backbone Gaussians to approximate the full scene repre-sentation. Extensive experiments on multiple datasets demonstrate thatour method achieves strong reconstruction quality and robustness whilemaintaining a highly compact scene representation.

ECCV 2026

DriveWeaver: Point-Conditioned Video Inpainting for Controllable Vehicle Insertion in Autonomous Driving Simulation

Junzhe Jiang, Zipei Ma, Zijie Pan, Li Zhang

A pivotal step in autonomous driving simulation involves in-serting foreground vehicles with predefined trajectories into simulatedscenes. This process enhances scene diversity and facilitates the creationof various corner cases for testing and improving autonomous drivingmodels. However, existing methods often rely on pre-reconstructed 3Dassets, which frequently lead to lighting inconsistencies between the in-serted foreground and the background. Moreover, the reliance on limited,manually-curated 3D assets hinders large-scale deployment. To addressthese challenges, we propose DriveWeaver, a novel framework for con-trollable vehicle insertion in autonomous driving simulation. Specifically,for a masked target insertion area, DriveWeaver performs video inpaint-ing conditioned on vehicle point clouds to generate high-quality, tempo-rally consistent vehicles. This video-inpainting-based approach ensuresseamless blending between the foreground and background, while thereadily available point cloud conditions enable superior generalization.To support long-term generation, we further design a global-to-local hier-archical inpainting strategy, ensuring the consistent identity and appear-ance of the inserted vehicles. Meanwhile, we extract explicit 3D Gaussianrepresentations of the inserted vehicles through an urban reconstructionpipeline to enable real-time rendering for autonomous driving simula-tion. Extensive experiments across diverse datasets demonstrate thatour method outperforms existing baselines in visual realism and geomet-ric consistency, providing a robust tool for scalable autonomous drivingscene augmentation.

ECCV 2026

NoiseTilt: Noise-Tilted Reverse Kernels for Diffusion Reward Alignment

Jisung Hwang, Yunhong Min, Jaihoon Kim, I-Chao Shen, Minhyuk Sung

We introduce the Noise-Tilted Reverse Kernel (NTRK), areward-guided diffusion sampler that injects reward gradients through thenoise term, leaving the pretrained reverse kernel unchanged and requiringonly a single sample per step. Reward-guided sampling at inference timehas greatly expanded the versatility of pretrained diffusion models. Yet ex-isting methods face a trade-off. Gradient-based guidance shifts the reversemean, steering generation but pushing intermediate states outside theregion that the model was trained on and degrading quality. Search-basedmethods preserve quality but gain no gradient signal. No prior methodachieves both. NTRK resolves this by keeping the reverse mean fixed andbiasing the noise term toward high reward. This is enabled by a whiteningoperator, the central mechanism behind NTRK, which converts rewardgradients into noise-compatible perturbations without losing their guidingsignal. Across various reward alignment tasks, NTRK outperforms recentstate-of-the-art baselines without losing sample quality. Remarkably, onaesthetic generation, NTRK surpasses the reward of the best baseline at500 NFEs using only 25 NFEs, a 20× reduction in compute.

ECCV 2026

FeDepth: Federated Learning for Depth Estimation under Robot Heterogeneity

Ganghyeon Lee, Inha Lee, Junhee Lee, Jeongeon Lee, Sung Whan Yoon, Kyungdon Joo

Although recent robot perception research emphasizes train-ing on data from diverse environments to improve generalization, mostexisting methods still rely on centralized learning, which is inefficient anddifficult to scale across heterogeneous robot platforms. Federated learn-ing (FL) offers an alternative by enabling distributed training withoutraw data transfer, but it suffers from severe performance degradation un-der domain shifts caused by heterogeneity across clients. In real roboticdeployments, data distributions often overlap across platforms, environ-ments, and sensing conditions, making it difficult to partition clientsinto clearly separated domains. However, this characteristic breaks theassumption of clearly separable client domains commonly used in clus-tered FL. To address this gap in robot perception, particularly in depthestimation, we introduce two realistic and unexplored non-IID scenariosthat reflect heterogeneity in terms of platform, environment, and depthdistribution. We then propose FeDepth, a descriptor-based clusteredFL framework that models client relationships through soft clustering.Unlike hard clustering methods that assume clearly separated clusters,FeDepth allows clients to participate in multiple clusters, capturing con-tinuous and ambiguous domain transitions commonly observed in roboticenvironments. Extensive experiments demonstrate that FeDepth con-sistently improves robustness over standard FL and clustered FL base-lines across multiple depth estimation architectures, providing a practicaland effective solution for federated robot perception. Our project pageis available at https://vision3d-lab.github.io/fedepth/.

ECCV 2026

DreamEdit3D: Personalization of Multi-View Diffusion Models for 3D Editing

Jinxin Ai, Matthias Niessner, Ziya Erkoç

While 2D di_x001B_usion models have achieved remarkable successin identity-preserving personalization, extending this capability to 3Dassets remains a signi_x001C_cant challenge due to the complexities of multi-view consistency and spatial control. Inspired by these 2D advance-ments, we present a novel personalization method for text-guided 3Dediting that enables compositional, object-level control through naturallanguage. Given a 3D input, we render orthogonal views and extractobject-level segmentation masks to isolate semantic components. Wethen learn distinct token embeddings for each component through a tai-lored two-phase optimization strategy: multi-view textual inversion withattention alignment, followed by full _x001C_ne-tuning of multi-view di_x001B_usionmodel. During inference, these disentangled tokens seamlessly composewith editing prompts to generate multi-view consistent images, whichare subsequently lifted into high-_x001C_delity textured 3D meshes. Extensiveevaluations across diverse editing scenarios demonstrate that our methodsuccessfully transfers the _x001D_exibility of 2D personalization to 3D, achiev-ing state-of-the-art edit faithfulness and identity preservation comparedto existing baselines.

ECCV 2026

Domain Arithmetic: One-Shot VLA Adaptation under Environmental Shifts

Taewook Kang, Taeheon Kim, Donghyun Shin, Jonghyun Choi

Vision-Language-Action (VLA) models often fail to performthe same learned tasks under environmental shifts, such as changes incamera pose and shifts to a different but similar robot (e.g., from Pandato UR5e). Adapting these models to the shifted environment (i.e., targetdomain) often requires training on multiple demonstrations for each task,which are costly to collect. To reduce the burden of data curation andtraining, we propose an analogy-based method that adapts VLA modelsunder environmental shifts through weight vector arithmetic with domain-specific information addition, named Domain ARiThmetic (DART). Un-like prior approaches, DART requires collecting only a single demonstra-tion, enabling efficient adaptation. To accurately isolate domain-specificinformation for addition, DART performs subspace alignment between sin-gular components in weight vectors to filter out noisy components. In bothsimulated and real-world experiments, DART outperforms existing VLAadaptation methods in one-shot scenarios across diverse visual and embod-iment shifts. Code is available at https://github.com/snumprlab/dart.

ECCV 2026

DeMuS: Learning Decoupled Matching and Scoring for Batch Zero-Shot Industrial Anomaly Detection

Zhengyang Zhao, Hailong Sun, Binhang Qi, Hongrui Yu, Zhongchi Wang, Hang Xu

Zero-Shot Industrial Anomaly Detection (IAD) is criticalfor rapid deployment in agile manufacturing. We investigate a highlypractical Batch Zero-Shot IAD setting, strictly constrained by smallintra-batch references and common pose variations. Directly applyingexisting mutual-scoring methods fails because training-free features lackgeneralized anomaly semantics and spatial robustness, resulting in am-biguous scoring distances and an “anomaly-to-anomaly shortcut” duringmatching, which can lead to missed detections. To address this, we pro-pose DeMuS (Decoupled Mutual Scoring), a novel two-stage learningframework. By training two lightweight adapters via a two-stage aux-iliary process, our method extracts highly discriminative anomaly se-mantics for accurate scoring (how-to-score), while explicitly decouplingrobust part-level correspondence learning (where-to-match) to guaranteereliable physical alignment. This synergistic mechanism effectively pre-vents semantic confusion under unaligned inputs. Extensive experimentson MVTec AD, VisA, and Real-IAD demonstrate DeMuS achieves state-of-the-art performance and superior robustness against pose shifts, main-taining efficient inference for online inspection. Our code is available athttps://github.com/evoLonation/DeMuS.

ECCV 2026

Comprehensive language–image pre-training for 3D medical image understanding

Tassilo Wald, Ibrahim Ethem Hamamci, Yuan Gao, Sam Bond-Taylor, Harshita Sharma, Maximilian Ilse, Cynthia Lo, Olesya Melnichenko, Anton Schwaighofer, Noel Codella, Maria Teodora Wetscherek, Klaus Maier-Hein, Panagiotis Korfiatis, Valentina Salvatelli, Javier Alvarez-Valle, Fernando Pérez-García

In the 3D medical image domain, vision-language pre-training is used to create vision-language encoders (VLEs) that can support radiologists by retrieving patients with similar abnormalities, predicting likelihoods of abnormality, or, with downstream adaptation, generating radiological reports. While the methodology holds promise, three challenges limit the capabilities of current 3D VLEs: data scarcity due to privacy concerns, high computational costs resulting from the volumetric nature of the images, and a domain shift between the long reports used for training and the short prompts used during inference for, e.g., zeroshot classification. As a consequence, natural-image VLE recipes do not directly transfer to 3D medical imaging. In this paper, we overcome these challenges by injecting additional supervision via a report generation objective and combining vision-language with vision-only pre-training, allowing us to leverage both image-only and paired image-text 3D datasets. Further, we propose a novel loss that addresses the domain shift between long reports and short textual prompts. Through these additional objectives, paired with best practices of the 3D medical imaging domain, we develop the Comprehensive Language–Image Pre-training (COLIPRI) encoder family. Our COLIPRI encoders achieve state-of-the-art performance in report generation, semantic segmentation, classification probing, and zero-shot classification. The model weights and inference code are freely available at https: //huggingface.co/microsoft/colipri.

ECCV 2026

CiQi-Agent: Aligning Vision, Tools and Aesthetics in Multimodal Agent for Cultural Reasoning on Chinese Porcelains

Wenhan Wang, Zhixiang Zhou, Zhongtian Ma, Yanzhu Chen, Ziyu Lin, Hao Sheng, Pengfei Liu, Wenqi Shao, Qiaosheng Zhang, Yu Qiao

The connoisseurship of antique Chinese porcelain demands extensive historical expertise, material understanding, and aesthetic sensitivity, making it difficult for non-specialists to engage. To democratize cultural-heritage understanding and assist expert connoisseurship, we introduce CiQi-Agent—a domain-specific Porcelain Connoisseurship Agent for intelligent analysis of antique Chinese porcelain. CiQi-Agent supports multi-image porcelain inputs and enables vision tool invocation and multimodal retrieval-augmented generation, performing finegrained connoisseurship analysis across six attributes: dynasty, reign period, kiln site, glaze color, decorative motif, and vessel shape. Beyond attribute classification, it captures subtle visual details, retrieves relevant domain knowledge, and integrates visual and textual evidence to produce coherent, explainable connoisseurship descriptions. To achieve this capability, we construct a large-scale, expert-annotated dataset CiQi-VQA, comprising 29,596 porcelain specimens, 51,553 images, and 557,940 visual question–answering pairs, and further establish a comprehensive benchmark CiQi-Bench aligned with the previously mentioned six attributes. CiQi-Agent is trained through supervised fine-tuning, reinforcement learning, and a tool-augmented reasoning framework that integrates two categories of tools: a vision tool and multimodal retrieval tools. Experimental results show that CiQi-Agent (7B) outperforms all competitive openand closed-source models across all six attributes on CiQi-Bench, achieving on average 12.2% higher accuracy than GPT-5.

ECCV 2026

BrepCoder: A Unified Multimodal Large Language Model for Multi-task B-rep Reasoning

Mingi Kim, Yongjun Kim, Jungwoo Kang, Hyungki Kim

Recent advancements in deep learning have actively addressedcomplex challenges within the Computer-Aided Design (CAD) domain.However, most existing approaches rely on task-specific models requir-ing structural modifications for new tasks, and they predominantly focuson point clouds or images rather than the industry-standard BoundaryRepresentation (B-rep) format. To address these limitations, we proposeBrepCoder, a unified Multimodal Large Language Model (MLLM) thatperforms diverse CAD tasks from B-rep inputs. By leveraging the codegeneration capabilities of Large Language Models (LLMs), we convertCAD modeling sequences into Python-like code and align them withB-rep. We then adopt a two-stage training strategy: First, pre-trainingon reverse engineering to learn geometric features and design logic. Sec-ond, effectively extending the model to various downstream tasks such ascompletion, error correction, and CAD-QA. Consequently, by interpret-ing B-rep as structural code, BrepCoder achieves superior generalizationacross diverse tasks, demonstrating its potential as a general-purposeCAD agent.

ECCV 2026

Boosting Text-Driven Video Segmentation via Geometry-Aware Distillation

Tianyu Zhu, Yingping Liang, Hesong Li, Ying Fu

Text-driven Referring Video Object Segmentation (RVOS)aims to locate and segment target objects in videos given natural lan-guage. However, existing models are typically trained on 2D image orvideo datasets with naive segmentation losses, which overlooks the geo-metric consistency across frames and leads to weak spatial understand-ing. In this paper, we propose Geometry-enhanced Language-guidedVideo segmentation (GeoLaV), a two-stage framework that distills 3Dgeometric knowledge from images to enhance text-driven video segmenta-tion. In the first stage, we perform monocular geometry pretraining withmonocular novel-view synthesis, enabling the model to acquire geometry-consistent visual representations via spatial alignment on large-scale single-image datasets. In the second stage, we introduce geometry-aware distil-lation and fine-tune the model on video segmentation datasets, transfer-ring 3D structural knowledge from a general 3D prior model. This processreinforces 3D awareness and improves both spatiotemporal coherence andlanguage grounding in segmentation. Extensive experiments show thatour method using only image segmentation data already provides notablezero-shot generalization in RVOS. When combined with geometry-awaredistillation for fine-tuning on videos, our method achieves state-of-the-artperformance across multiple RVOS benchmarks. The code is available athttps://github.com/Tony1882880/GeoLaV.

ECCV 2026

Beyond Isolated Scans: Cross-Phase Alignment of Structure and Topology for 3D Medical Pretraining

Wenzhuo Xu, YANJIE ZHOU, Yujian Hu, Hongkun Zhang, Minfeng Xu

Self-supervised learning (SSL) has significantly advanced 3Dmedical image analysis. However, existing pretraining methods typicallytreat multi-phase scans as isolated samples, neglecting the rich anatom-ical correspondence inherent in paired acquisitions. Clinically, contrast-enhanced CT (CECT) offers a structure-amplified view of the exactanatomy captured in a paired non-contrast CT (NCCT), yet the drasticintensity shifts caused by contrast agents make direct voxel-wise align-ment challenging. To harness this naturally occurring supervisory signalwhile decoupling structural topology from phase-specific appearance, wepropose CAST (Cross-phase Alignment of Structure and Topology),a novel paired pretraining framework. CAST employs a 3D CNN archi-tecture to explicitly align NCCT representations with CECT targets.Moving beyond conventional reconstruction, we introduce two feature-level constraints: (1) a Spectral Consistency module that utilizes3D wavelet decomposition to align frequency-aware boundaries whilesuppressing contrast-induced noise; (2) a Geometry-Aware Topo-logical Consistency module that preserves local relational graphsamong salient anatomical keypoints via dynamic top-hat sampling. Tosupport this, we construct a large-scale dataset comprising 13,850 pairedvolumetric CT scans. Extensive evaluations on public benchmarks andreal-world clinical cohorts demonstrate that CAST achieves state-of-the-art performance. Notably, our method delivers substantial gains inboundary-sensitive segmentation tasks and low-data regimes, proving itsefficacy in learning robust, phase-invariant anatomical representationsfor both NCCT and CECT downstream applications. Code is availableat https://github.com/alibaba-damo-academy/CAST.

ECCV 2026

PRISM3D: Probabilistic Refinement and Robust Initialization for Physically Consistent Scene Modeling under Extreme Motion Blur

Gopi Raju Matta, Reddypalli Trisha, Divya Madhuri Vemunuri, Kaushik Mitra

We address the inverse problem of blind 3D scene recon-struction from extremely motion-blurred images, a scenario where tradi-tional Structure-from-Motion (SfM) pipelines fail. Existing approachestypically circumvent this bottleneck by relying on impractical sharp-imagesupervision. In this work, we introduce PRISM3D, a unified frameworkenabling robust reconstruction directly from severely degraded inputs.To overcome the lack of a reliable starting point, we propose a RobustInitialization strategy utilizing deep dense tracking method (VGGSfM)to recover global topology where feature matching fails. To the best of ourknowledge, we are the first to effectively leverage this paradigm to boot-strap 3D Gaussian Splatting from extreme motion blur. However, whilerobust, this initialization yields sparse and noisy geometry that causes de-terministic optimization to diverge. To resolve this, we propose a coupledsolution driven by probability and physics: we adopt a probabilisticformulation for geometric densification via Markov Chain Monte Carlo(MCMC) to robustly populate the sparse priors, while simultaneouslymodeling physical image formation via continuous Bézier Trajectories.Furthermore, while PRISM3D establishes a highly robust standalonepipeline, the availability of complementary event streams offers an op-portunity to push the reconstruction fidelity further. To exploit this, weintroduce PRISM3D-E, a multi-modal (RGB + Events) extension thatseamlessly integrates high-temporal-resolution events as structural priorsto maximize geometric recovery. Because existing datasets lack pairedevent streams under such severe degradation, we concurrently contributethe PRISM3D-E Benchmark to facilitate rigorous evaluation. Exten-sive experiments demonstrate that both our standalone RGB frameworkand its multi-modal extension establish new state-of-the-art performance.To facilitate future research in extreme motion deblurring, our projectpage, featuring extensive qualitative comparisons, video results, and theupcoming releases of our codebase and benchmark dataset, is availableat https://gopirajumatta.github.io/PRISM3D/.

ECCV 2026

ATOMIC: A Domain-Specific Vision-Language Model for Transmission Electron Microscopy

Chong-ren Tu, HUNG-WEI HSUEH, Shu-han Hsu

Vision–language models (VLMs) achieve strong performanceon general-domain tasks but can degrade when visual distributions, ter-minology, and reasoning requirements diverge from general-domain pre-training assumptions, including in scientific imaging. We study this chal-lenge in materials-science transmission electron microscopy (TEM), adata-scarce setting that requires nanoscale visual grounding, modality-specific interpretation, and specialized materials-science knowledge. Weintroduce ATOMIC (Assistant for TEM Oriented Multimodal Instruc-tion and Conversation), a TEM-oriented vision–language model adaptedusing peer-reviewed, literature-derived data without manual annotation.Our pipeline extracts figure–caption pairs, isolates TEM subfigures, andgenerates multimodal instruction data via GPT-based instruction gen-eration. The training strategy separates two complementary supervisionsignals: VisionGround, which enforces image-only grounding, and Do-mainContext, which injects caption-conditioned scientific context. Theircombination, Blend, yields consistent improvements across perception-and knowledge-intensive queries. ATOMIC-7B-Blend achieves 75.2% ac-curacy on TEM-MCQ and 37.5% recall-oriented Answer Word Cover-age (AWC) on TEM-VQA, narrowing the gap to GPT-4o on AWC(40.4%) while enabling local deployment. On the TEM subset of MatCha,an independent external benchmark, ATOMIC-7B-Blend+FT outper-forms LLaVA-v1.5-7B+FT by 18.1 percentage points under identicalfine-tuning conditions. We release TEM-MCQ/TEM-VQA annotations,article URLs, figure IDs, normalized crop coordinates for sub-figure re-construction, the data-curation pipeline, and model weights to supportreproducibility within copyright constraints. These results show thatstructured literature-derived supervision offers an effective data-centricstrategy for adapting VLMs to materials-science TEM and may provide areproducible foundation for future studies of VLM adaptation in relateddata-scarce scientific-imaging domains.

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