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ECCV 2026

Diagram-MMU: A Multi-Modal Benchmark for Scientific Diagrams

Weihao Bo, Shan Zhang, Yanpeng Sun, Jie Liu, Yongke Yao, Jinhao Du, Wei He, Kai Zou, Zechao Li, Jingdong Wang

Multimodal Large Language Models (MLLMs) have beengrowing the capability for scienti_x001C_c writing and collaboration. For exam-ple, OpenAI Prism is a free workspace for scienti_x001C_c writing and collab-oration. One important feature in Prism is turning scienti_x001C_c diagramsdirectly into LATEX TikZ code. In this paper, we build a benchmark,Diagram-MMU, a multi-modal benchmark designed to assess MLLMs'ability for scienti_x001C_c diagram parsing and understanding. Diagram-MMUfeatures 3.7k curated diagrams and 18.3k human-validated questionsacross six domains. It evaluates MLLMs on three tasks common in vibewriting workspaces: diagram-to-code parsing, diagram-to-code editing,and diagram question answering, alongside agentic settings per task.The evaluation of 12 MLLMs reveals that diagram-to-code tasks aremore challenging than diagram question answering: models can reasonwell over diagrams but struggle to parse and edit them, underscoringthe need for methods to enhance MLLMs' capability in diagram-to-codegeneration. Under agentic settings, most models improve parsing andediting performance but degrade on question answering, while Claude-4.6 Opus consistently improves across all three tasks. Our benchmark ispublicly available at https://huggingface.co/datasets/AIGrounding/Diagram-MMU.

ECCV 2026

Cross-Species Animal Re-Identification with Semantic Consistency Learning

Shuoyi Chen, Yuejia Li, Mang Ye

Generalizable animal Re-Identification (ReID) aims to recognize individual animals across species with diverse morphologies and ecological contexts. Unlike person ReID, where different domains share similar body structures, animal species often exhibit drastically different anatomical structures and visual patterns, making it difficult to establish shared visual correspondences. As a result, representations learned across species tend to form fragmented embedding spaces, which severely limits cross-species generalization. To address this challenge, we propose Semantic Consistency Learning (SCL), a framework designed to learn representations that remain stable across appearance variations while preserving semantic structures shared across species. SCL consists of two complementary components. Foreground–Background Decoupled Spectral Normalization (FDSNorm) stabilizes feature statistics by suppressing environment-induced style variations in a region-aware manner, while Cross-species Neighborhood Modeling (CNM) captures transferable relational structures across species through dynamic feature neighborhoods. Extensive experiments on 11 public animal ReID datasets demonstrate that SCL consistently outperforms state-of-the-art methods under multiple cross-species evaluation protocols and generalizes effectively to previously unseen species and ecological domains. Code is available at https://github.com/Kemalau/ECCV-26-SCL.

ECCV 2026

AutoWeather4D: Autonomous Driving Video Weather Conversion via G-Buffer Dual-Pass Editing

Tianyu Liu, Weitao Xiong, Kunming Luo, Manyuan Zhang, Peng Li, Yuan Liu, Ping Tan

Generative video models have significantly advanced the photorealistic synthesis of adverse weather for autonomous driving; however, they consistently demand massive datasets to learn rare weather scenarios. While 3D-aware editing methods alleviate these data constraints by augmenting existing video footage, they are fundamentally bottlenecked by costly per-scene optimization and suffer from inherent geometric and illumination entanglement. In this work, we introduce AutoWeather4D, a feed-forward 3D-aware weather editing framework designed to explicitly decouple geometry and illumination. At the core of our approach is a G-buffer Dual-pass Editing mechanism. The Geometry Pass leverages explicit structural foundations to enable surface-anchored physical interactions, while the Light Pass analytically resolves light transport, accumulating the contributions of local illuminants into the global illumination to enable dynamic 3D local relighting. Extensive experiments demonstrate that AutoWeather4D achieves comparable photorealism and structural consistency to generative baselines while enabling finegrained parametric physical control, serving as a practical data engine for autonomous driving.

ECCV 2026

IRG-MotionLLM: Interleaving Motion Generation, Assessment and Refinement for Text-to-Motion Generation

Yuanming Li, Qize Yang, Nan Lei, Shenghao Fu, Ling-An Zeng, Jian-Fang Hu, Xihan Wei, Weishi Zheng

Recent advances in motion-aware large language models haveshown remarkable promise for jointly learning motion understandingand generation knowledge. However, these models typically treat un-derstanding and generation separately, limiting the mutual benefits thatcould arise from interactive feedback between tasks. In this work, wereveal that motion assessment and refinement tasks can act as crucialbridges to enable knowledge flow from motion understanding to genera-tion. Specifically, we propose Interleaved Reasoning for Motion Genera-tion (IRMoGen), a novel paradigm that tightly couples motion genera-tion with assessment and refinement through iterative text-motion dia-logue. To realize this, we introduce IRG-MotionLLM, the first model thatseamlessly interleaves motion generation, assessment, and refinement toimprove the alignment between generated motion and goal text. IRG-MotionLLM is developed progressively with a novel three-stage train-ing scheme, initializing and subsequently enhancing native IRMoGencapabilities. To facilitate this development, we construct an automateddata engine to synthesize interleaved reasoning annotations from existingtext-motion datasets. Extensive experiments demonstrate the propertiesbrought by IRMoGen training, and the advanced cross-benchmark andcross-evaluator performance of IRG-MotionLLM. Code and models areavailable at https://github.com/HumanMLLM/IRG-MotionLLM.

ECCV 2026

CoverPrune: Coverage-Driven Token Pruning for 3D VLMs via Optimal Transport

Peng Ling, Yingda Yin, Lingting Zhu, Weikai Chen, Shengju Qian, Zeyu HU, Xin Wang, Wenming Yang

While 3D Vision-Language Models (3D VLMs) have demon-strated remarkable spatial reasoning capabilities, they suffer from mas-sive visual token counts that create severe computational bottlenecksduring inference. Existing token pruning methods primarily rely on di-versity-based selection, discarding similar tokens to maximize dispersion.However, in 3D environments, this approach frequently drops represen-tative prototype tokens in favor of outliers, breaking the multi-view con-sistencies and geometric structures essential for spatial reasoning. In thispaper, we propose a paradigm shift for 3D VLM token pruning: frommaximizing diversity to preserving visual evidence coverage. We intro-duce CoverPrune, a training-free framework that formulates inference-time token pruning as an Optimal Transport (OT) problem. To over-come the intractable combinatorial subset selection inherent in this for-mulation, we design the Feature-Spatial-Temporal (FST) transport costand target capacity, along with an efficient Spatial-Guided Greedy Selec-tion (SGS) algorithm to approximate the OT objective. Furthermore, wepropose CoverPrune-Lite, an accelerated variant utilizing spatially struc-tured local matching for minimal overhead. Extensive experiments acrossmultiple 3D visual-spatial reasoning benchmarks demonstrate that ourmethods achieve state-of-the-art token efficiency, maintaining robust rea-soning performance even under highly aggressive pruning budgets. Visitour project website at https://github.com/Brucess/CoverPrune.

ECCV 2026

CoT-PL: Chain-of-Thought Pseudo-Labeling for Open-Vocabulary Object Detection

Hojun Choi, Youngsun Lim, Jaeyo Shin, Hyunjung Shim

Open-vocabulary object detection (OVD) aims to recognizeand localize object categories beyond the training set. Recent approachesleverage vision-language models to generate pseudo-labels using image-text alignment, allowing detectors to generalize to unseen classes withoutexplicit supervision. However, these methods depend heavily on single-step image-text matching, neglecting the intermediate reasoning stepscrucial for interpreting semantically complex visual contexts, such ascrowding or occlusion. In this paper, we introduce MSPL, a frameworkthat incorporates multi-step visual reasoning into the pseudo-labelingprocess for OVD. It decomposes complex scene understanding into threeinterpretable steps—object localization, category recognition, and back-ground grounding—where these intermediate reasoning states serve asrich supervision sources. Extensive experiments on standard OVD evalu-ation protocols demonstrate that MSPL achieves state-of-the-art perfor-mance with superior pseudo-labeling efficiency, outperforming the strongHuman Pseudo PseudoVLMs MLLMAnnotation Annotation AnnotationImage-Text RPN🔥 A boy sits near a dog Visual SAMMatching while watching television. ReasoningImage-Captions Input ImageSingle-step Assignment Multi-step Assignment🔥 🔥 OV Noisy PLs 🔥 OV Accurate PLsBase Detector Novel Large-Scale Novel Require captions Open-Vocab. Novel No captionsDetector DetectorClasses Classes Base Classes Classes BG collapse Base Classes Classes BG grounding(a) Manual Pseudo-Labeling Designs (b) Single-Step OV Pseudo-Labeling Methods (c) Our Method: Multi-Step OV Pseudo-LabelingFig. 2: (a) Manual pseudo-labels for novel classes is costly and does not scale. (b) Re-cent approaches automate this process via single-step semantic assignment with vision-language models and image captions, struggling in complex scenes. (c) Our caption-freemethod leverages multi-step reasoning to interpret semantically complex scenes.baseline by 9.4 AP50 for novel classes on OV-COCO and improving boxand mask APr by 3.2 and 2.2, respectively, on OV-LVIS.

ECCV 2026

Beyond Linear Shortcuts: Rectifying Diffusion Preference Optimization with Intrinsic Generative Geometry

Pin Wang, Huaibo Huang, Jiayang Sun, Hongbo Wang, Wentao Jiang, Tiezheng Ge, Jie Cao, Ran He

Direct Preference Optimization (DPO) has proven effectivefor aligning diffusion models with human preferences. However, by re-lying solely on endpoint supervision, existing DPO fine-tuning practicesignore the curved trajectory of the pretrained generation process. Thisforces a linear shortcut in the latent space, disregarding the intrinsicgeometry and leading to off-trajectory and off-manifold generation. Toaddress this, we propose Trajectory-Consistent Preference Optimization(TCPO), a fine-tuning paradigm respecting the model’s intrinsic genera-tive geometry. TCPO first performs Curvature-Adaptive Sampling (CAS)to dynamically select a curvature-aware intermediate latent as a geo-metric anchor. Then, Trajectory-Rectified Fusion (TRF) combines thisgeometric anchor with the conventional endpoint target which acts as asemantic anchor to form a trajectory-consistent supervision signal. Ex-tensive experiments show that TCPO achieves superior visual fidelityand text-to-image alignment on standard benchmarks, with improvedtraining efficiency. 4

ECCV 2026

Automatic Method Illustration Generation for AI Scientific Papers via Drawing Middleware Creation, Evolution, and Orchestration

Zhuoling Li, Jiarui Zhang, Ping Hu, Jason Kuen, Jiuxiang Gu, Hossein Rahmani, Jun Liu

Method illustrations (MIs) play a crucial role in conveyingthe core ideas of scientific papers, yet their generation remains a labor-intensive process. Here, we take inspiration from human authors’ drawingpractices and correspondingly propose FigAgent, a novel multi-agentframework for high-quality automatic MI generation. Our FigAgent dis-tills drawing experiences from similar components across MIs and encap-sulates them into reusable drawing middlewares that can be orchestratedfor MI generation, while evolving these middlewares to adapt to dynami-cally evolving drawing requirements. Besides, a novel Explore-and-Selectdrawing strategy is introduced to mimic the human-like trial-and-errormanner for gradually constructing MIs with complex structures. Exten-sive experiments show the efficacy of our method. Project page is here.

ECCV 2026

ADAPT: Attention Dynamics Alignment with Preference Tuning for Faithful MLLMs

Zhiyuan Yao, Zheren Fu, Zhixiao Zheng, Jiajun Li, Yi Tu, Zhendong Mao

Multimodal Large Language Models (MLLMs) are critically hampered by hallucination—generating content inconsistent with the provided image. In this paper, we identify an internal signature of hallucination: progressive degradation of text-to-image cross-attention during generation, leading to specific failure patterns like unfocused or biased attention. Existing mitigation strategies are largely outcome-driven and do not explicitly target this failure mode. To address this problem, we propose ADAPT (Attention Dynamics Alignment with Preference Tuning), an attention-based framework that intervenes directly on text-toimage cross-attention dynamics. We propose ADAPT with three key contributions: a cross-attention visual anchor refined from early decoding to provide stable spatial grounding, an attention-supervised inference mechanism that detects and corrects attention drift online, and a Visual Attention Guidance DPO that aligns preferences toward visually grounded responses. Experiments show that each component of ADAPT contributes to hallucination reduction, and the full framework achieves new best results across multiple hallucination benchmarks, reducing hallucination rates by 40–60% across mainstream backbones while preserving general multimodal capabilities. Our work provides an attentionbased perspective on mitigating hallucinations by exploring the model’s internal text-to-image cross-attention behaviors. Code is available at: https://github.com/yao-ustc/ADAPT.

ECCV 2026

AC3S: Adaptive Conditioning for 3D-Aware Synthetic Data Generation

Eric Ji, Qiran Hu, Wufei Ma, Sarthak Jain, Yingying Li, Minh Do, Yaoyao Liu

Synthetic data generation has emerged as a powerful tool for improving data scalability in computer vision. Recent diffusion-based pipelines have demonstrated strong photorealism. However, enforcing precise 3D structure and pose consistency in generated images remains challenging. Existing methods leverage visual prompts such as edge maps to guide diffusion models, but often suffer from over-conditioning artifacts that degrade image realism and limit dataset quality. In this paper, we present a diffusion-based image generation framework that enforces 3D structural alignment while preserving photorealism through adaptive conditioning. Our framework, Adaptive Conditioning for 3D-Aware Synthetic Data Generation (AC3S), introduces a self-supervised visual prompt modulator that dynamically adjusts the strength of ControlNet conditioning, preventing over-conditioning and enabling the diffusion model to retain its generative expressiveness. To further enhance diversity and semantic consistency, we develop a multi-agent vision language model framework that composes detailed and 3D-aware prompts aligned with the underlying geometric structure. Together, these components enable the scalable generation of high-quality synthetic datasets with accurate 2D and 3D annotations. Extensive experiments demonstrate that our method significantly improves image quality and downstream utility.1

ECCV 2026

3D-Layout-R1: Structured Reasoning for Language-Instructed Spatial Editing

Haoyu Zhen, Xiaolong Li, Yilin Zhao, Han Zhang, Sifei Liu, Kaichun Mo, Chuang Gan, Subhashree Radhakrishnan

Large Language Models (LLMs) and Vision Language Mod-els (VLMs) have shown impressive reasoning abilities, yet they strugglewith spatial understanding and layout consistency when performing fine-grained visual editing. We introduce a Structured Reasoning frameworkthat performs text-conditioned spatial layout editing via scene-graph rea-soning. Given an input scene graph and a natural-language instruction,the model reasons over the graph to generate an updated scene graphthat satisfies the text condition while maintaining spatial coherence. Byexplicitly guiding the reasoning process through structured relationalrepresentations, our approach improves both interpretability and controlover spatial relationships. We evaluate our method on a new text-guidedlayout editing benchmark encompassing sorting, spatial alignment, androom-editing tasks. Our training paradigm yields an average 15% im-provement in IoU and 25% reduction in center-distance error comparedto Chain of thought Fine-tuning (CoT-SFT) and vanilla GRPO base-lines. Compared to SOTA zero-shot LLMs, our best models achieve upto 20% higher mIoU, demonstrating markedly improved spatial precision.

ECCV 2026

Wat3R: Underwater 3D Geometry Learning without Underwater Annotations

Jiangwei Ren, Xingyu Jiang, Zijie Song, Wei Xu, Hongkai Lin, Dingkang Liang, Xiang Bai

Estimating 3D geometry in underwater environments presentsunique challenges due to light attenuation, scattering, and the absenceof large-scale, high-quality 3D annotations. Pioneering methods rely onmassive dense annotations that are impractical in underwater settings.In this paper, we propose Wat3R, a cross-domain semi-supervised learn-ing framework designed to adapt feed-forward 3D reconstruction modelsfrom air to underwater scenes. Uniquely, our method eliminates the needfor any annotated underwater data following a teacher-student architec-ture, that learns robust geometry representations merely on abundantunlabeled real underwater video footage. We also design a cross-viewconsistency loss that leverages geometric cues from other views to com-pensate for the information degradation in the current view caused bywater attenuation and scattering. Furthermore, considering the lack ofcomprehensive evaluation benchmarks, we construct Water3D, a di-verse dataset covering various water bodies and underwater scenarios, de-signed for geometric task evaluation. Experimental results demonstratethat Wat3R outperforms current state-of-the-art methods in underwatermulti-view depth estimation and point cloud reconstruction. The datasetand code are available at https://github.com/LSXI7/Wat3R .

ECCV 2026

UniTranslator: A Unified Multi-modal framework for End-to-end In-Image Machine Translation

Jiahao Lyu, Pei Fu, Zhenhang Li, Shaojie Zhang, Jiahui Yang, Yu Zhou, Can Ma, Zhenbo Luo, Jian Luan

In-Image Machine Translation (IIMT) aims to translate scenetext in an image and render the translated text back into the original re-gions while preserving the overall visual appearance. Recent unified mul-timodal models provide a promising solution by combining visual-textunderstanding and image generation within a single framework. However,directly adapting such models to IIMT remains challenging. In particu-lar, they often suffer from understanding-generation conflicts, where thetranslation inferred during understanding is inconsistent with the textsupervision used in generation, and spatial position misalignment, wherethe rendered text does not accurately match the target text regions.To address these issues, we present UniTranslator, a unified multimodalframework for IIMT that tightly couples translation understanding andtext editing. Specifically, we introduce an Understand-Generation Align-ment Module (UGAM) to bridge the representation gap between un-derstanding and generation, encouraging semantic consistency betweentranslated content prediction and text rendering. We further propose aSpatial Mask Decoder (SMD) with pixel-level supervision over text re-gions to improve spatial grounding, geometric alignment, and layout con-trollability during generation. Extensive experiments on multiple bench-marks demonstrate that UniTranslator achieves state-of-the-art perfor-mance across diverse language directions and complex real-world layouts.Moreover, our results reveal a strong mutual reinforcement effect betweentranslation understanding and image generation, highlighting the advan-tage of unified translation multimodal learning. Code is available here.

ECCV 2026

UniFusion: Sparse-View 4D Reconstruction via Unified Spatio-temporal Depth Alignment

Yongzhe Lyu, Shaofei Wang, Yixin Chen, Siyuan Huang

In this paper, we address the challenging problem of 4D re-construction from sparse-view videos. This setup usually relies on monoc-ular depth estimation to provide priors for the reconstruction model. Akey challenge arises from limited cross-view overlap and temporal vari-ation, making monocular depth predictions inconsistent across viewsand time. Existing methods align spatial and temporal dimensions inseparate stages, requiring foreground segmentation masks while failingto leverage temporal cues for cross-view alignment. Contrary to thesemethods, we propose a unified spatial-temporal depth alignment frame-work that jointly resolves cross-view and cross-time inconsistencies with-out distinguishing foreground/background. Our method represents depthmaps across views and time as a set of spatio-temporal neural fields.This representation not only yields fast convergence, but also capturesspatio-temporal correlation among depth maps implicitly, without de-pendence on external segmentation/tracking models. We also propose amulti-view depth-order loss while leveraging the classic scale-and-shift-invariant loss to further improve the final depth quality. The aligneddepths initialize and supervise Gaussian splatting models for 4D recon-struction. Experiments on Ego-Exo4D and EgoHuman demonstrate thatour improved depth alignment substantially benefits dynamic Gaussian-splatting-based reconstruction methods for novel-time/view synthesis andgeometry accuracy/consistency.

ECCV 2026

Rule-VLN: Bridging Perception and Compliance via Semantic Reasoning and Geometric Rectification

Jiawen Wen, Penglei Sun, Wenjie Zhang, Suixuan QIU, Weisheng Xu, Xiaofei Yang, Xiaowen Chu

As embodied AI transitions to real-world deployment, thesuccess of the Vision-and-Language Navigation (VLN) task tends to evolvefrom mere reachability to social compliance. However, current agentssuffer from a “Goal-driven trap”, prioritizing physical geometry (“can Igo?”) over semantic rules (“may I go?”), frequently overlooking subtleregulatory constraints. To bridge this gap, we establish Rule-VLN, the firstlarge-scale urban benchmark for rule-compliant navigation. Spanning amassive 29k-node environment, it injects 177 diverse regulatory categoriesinto 8k constrained nodes across four curriculum levels, challenging agentswith fine-grained visual and behavioral constraints. We further proposethe Semantic Navigation Rectification Module (SNRM), a universal, zero-shot module designed to equip pre-trained agents with safety awareness.SNRM integrates a coarse-to-fine visual perception VLM framework withan epistemic mental map for dynamic detour planning. Experimentsdemonstrate that while Rule-VLN challenges state-of-the-art models,SNRM significantly restores navigation capabilities, reducing CVR by19.26% and boosting TC by 5.97%. The project page is available athttps://jeffry-wen.github.io/Rule-VLN/.

ECCV 2026

UBone3D: Physics-Rectified Conditional Flow Matching for Anatomical 3D Shape Completion from Ultrasound

Weiying Chen, Yuchong Gao, Siyuan Li, Marek Reformat, Rui Zheng, Edmond Lou

Three-dimensional ultrasound (US) is a safe, radiation-freecomplementary modality to CT and X-rays for longitudinal monitoring,yet its segmentation-derived partial point clouds are extremely artifact-laden. Consequently, it is challenging to recover a clean and completeanatomical structure from such US point clouds. In this paper, we presentUBone3D, a novel framework based on physics-rectified conditional flowmatching (CFM) that performs point cloud completion directly from par-tial US observations. UBone3D models deterministic physics artifacts(e.g., surface thickening, streaking, dropouts) via a simulated physicsproxy, and introduces test-time physics rectification to steer the shapecompletion. At inference, the completion is jointly steered by two decou-pled forces: (1) anatomical plausibility enforced by a CT-trained gen-erative shape prior, BoneFM, and (2) physics consistency enforced byUSimNet in the ultrasound formation space. Extensive experimentson simulated and in-vivo data demonstrate significant improvements inreconstruction accuracy and anatomical fidelity over existing baselines.Project page: https://answerrtx.github.io/UBone3D-Proj/.

ECCV 2026

Trust Your Instincts: Confidence-Driven Test-Time RL for Vision-Language-Action Models

Chen Siyao, Jiakang Yuan, Jiaxin Wang, Tao Chen

Reinforcement learning (RL) has become indispensable forpushing Vision-Language-Action Models (VLA) beyond static imitationlearning. However, existing RL methods typically necessitate externalenvironmental feedback, relying on predefined success signals to guidepolicy updates. In this work, we demonstrate that VLA models possessstrong internal evaluative capabilities: in discrete-action VLAs, trajecto-ries with higher generation confidence are significantly more likely to suc-ceed. Based on the observation, we introduce T2 VLA (Test-time VLA),an architecture-agnostic test-time RL framework that enables VLA mod-els to achieve self-bootstrapping policy improvement. Instead of relyingon external rewards, T2 VLA leverages the trajectory-level similarityto high-confidence expert demonstrations as an intrinsic reward signal.In addition, we propose a Confidence-Driven Dual Expert Bootstrappingmechanism. By dynamically balancing a Local Pseudo-Expert for aggres-sive exploration and a Global Expert Pool for training stability, T2 VLAprevents policy collapse while discovering further breakthroughs. Exten-sive experiments on the LIBERO and RoboTwin benchmarks show thatT2 VLA consistently outperforms supervised baselines and approachesoracle RL performance with ground-truth rewards, achieving effectiveimprovement without external reward feedback. Furthermore, T2 VLAcan adapt to distinct VLA paradigms, including both OpenVLA-OFTand the π series.

ECCV 2026

TRINITY: A Multi-Perspective Benchmark for Personal-Style Video Highlight Detection

Qianqian Chen, Hyun Bin Kim, Denzel Wijaya, Yang Yi, Bo Liu, Yangkai Ding

Traditional video highlight detection relies on a narrow, event-centric definition of saliency, which often fails to generalize to uncon-strained personal videos where highlights are heterogeneous and perspective-dependent. To address this, we introduce TRINITY, a multi-perspectivebenchmark that decomposes highlight saliency into three complemen-tary dimensions, Event, Emotion, and Nature, within a unified tempo-ral framework. Leveraging this multi-faceted view, we propose a shared-backbone multi-branch architecture designed for parallel multi-perspectiveprediction via view-specific experts. Comprehensive experiments demon-strate that our method significantly outperforms state-of-the-art base-lines, achieving gains of +7.15/ + 3.62 mAPρ=15%/50% on Mr. HiSumand +10.82 mAP on YouTube Highlights. These results validate thatmulti-perspective modeling provides a more robust and comprehensiveformulation of video saliency, especially for complex real-world scenar-ios. The benchmark and relevant codes will be released upon acceptance.The benchmark is available at https://huggingface.co/datasets/vanilladucky/TRINITY.

ECCV 2026

Toward Interpretable Analysis of Whole-slide Pathology Images via Large Language Model-based Agentic Reasoning

Jingyun Chen, Linghan Cai, Zhikang Wang, Yi Huang, Songhan Jiang, Shenjin Huang, Hongpeng Wang, Yongbing Zhang

Analyzing whole-slide pathology images (WSIs) requires aniterative, evidence-driven reasoning process that parallels how pathol-ogists dynamically zoom, refocus, and self-correct while collecting ev-idence. However, existing computational pipelines often lack this rea-soning trajectory, resulting in opaque and unjustifiable predictions. Tobridge this gap, we present PathAgent, a training-free, large languagemodel (LLM)-based agent framework that emulates the reflective, step-wise analytical approach of human experts. PathAgent can autonomouslyexplore WSIs, iteratively and precisely locating significant micro-regionsusing the Navigator module, extracting morphological visual cues usingthe Perceptor, and integrating these findings into the continuously evolv-ing natural language trajectories in the Executor. The entire sequenceof observations and decisions forms a structured evidence trajectory,yielding traceable and evidence-grounded analyses. Evaluated across fivechallenging datasets spanning both whole-slide and patch-level settings,PathAgent exhibits strong zero-shot generalization and achieves perfor-mance comparable to human experts, surpassing task-specific baselinesacross diverse pathology tasks, including molecular subtype classifica-tion, histological grading and tumor type diagnosis. Human collaborativeevaluations demonstrate that PathAgent produces diagnostic decisionshighly consistent with expert-selected regions. These results demonstratethat PathAgent enables interpretable and clinically grounded WSI analy-sis. The code is available at https://github.com/G14nTDo4/PathAgent.

ECCV 2026

Sparse-View Surface Reconstruction using Gaussian Splatting through High-Confidence Depth Propagation with Normal Priors

Liang Han, Bangcai Wei, Junsheng Zhou, Yushen Liu, Zhizhong Han

3D reconstruction from sparse views is a challenging task in3D computer vision. Recent studies on 3D Gaussian Splatting (3DGS)have achieved remarkable results with sparse views in novel view syn-thesis, yet reconstructing high-quality geometric surfaces from sparseviews remains a challenge, due to the limited geometry clues and thediscreteness of Gaussians. In this paper, we propose a novel 3DGS-basedmethod for high-fidelity surface reconstruction from sparse views. Ourkey insight is to introduce a normal-guided depth propagation approach,which can extend depth information from high-confidence regions to con-strain the depth in low-confidence areas. Additionally, we propose an ab-normal depth edge-aware regularization to address depth discontinuitiescaused by the discreteness of Gaussians. Extensive experiments on DTUand Tanks-and-Temples datasets demonstrate that our method outper-forms the state-of-the-art methods in sparse view surface reconstruction.Project page: https://hanl2010.github.io/DP-GS.

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