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ECCV 2026

Circuit-MLLM: Topological Logic-Guided Latent-Space Visual Reasoning for Circuit Schematic Understanding

Jinyuan Deng, Yuqi Jiang, Wenjing Huang, Xin Li, Qi Sun, Cheng Zhuo

Through pre-training on extensive text and image datasets, current multi-modal large language models (MLLMs) achieve strong performance on general tasks. However, circuit schematics present a unique challenge for MLLMs due to their dense component layouts and distinct topological logic, demanding fine-grained structural parsing to extract the electrical semantics. To address this, we propose Circuit-MLLM, a multimodal reasoning framework that reformulates circuit topology analysis as a process of device localization, path tracing, and sequential reasoning within the latent space. We introduce a circuit knowledge mining mechanism that deeply aligns the model’s latent representations with structurally rich features derived from multi-granularity circuit vision experts, enabling the model to effectively internalize topological semantics. Building upon these internalized semantics, we devise a topology-guided sequencing strategy that decouples reasoning from the rigid raster-scan order, enforcing stepwise inference along the circuit’s topological logic in latent space. Across diverse circuit analysis tasks, Circuit-MLLM consistently outperforms strong baselines, notably achieving a 25% higher average score than GPT-5.1, which demonstrates the effectiveness of our framework in circuit schematic topology analysis. Code is publicly available at https://github.com/IC-Yuan/Circuit-MLLM.

ECCV 2026

SpectralSplats: Robust Differentiable Tracking via Spectral Moment Supervision

Avigail Cohen Rimon, Amir Mann, Mirela Ben-Chen, Or Litany

3D Gaussian Splatting (3DGS) enables real-time, photorealistic novel view synthesis, making it a highly attractive representation for model-based video tracking. However, leveraging the differentiability of the 3DGS renderer “in the wild” remains notoriously fragile. A fundamental bottleneck lies in the compact, local support of the Gaussian primitives. Standard photometric objectives implicitly rely on spatial overlap; if severe camera misalignment places the rendered object outside the target’s local footprint, gradients strictly vanish, leaving the optimizer stranded. We introduce SpectralSplats, a robust tracking framework that resolves this "vanishing gradient" problem by shifting the optimization objective from the spatial to the frequency domain. By supervising the rendered image via a set of global complex sinusoidal features (Spectral Moments), we construct a global basin of attraction, ensuring that a valid, directional gradient toward the target exists across the entire image domain, even when pixel overlap is completely nonexistent. To harness this global basin without introducing periodic local minima associated with high frequencies, we derive a principled Frequency Annealing schedule from first principles, gracefully transitioning the optimizer from global convexity to precise spatial alignment. We demonstrate that SpectralSplats acts as a seamless, drop-in replacement for spatial losses across diverse deformation parameterizations (from MLPs to sparse control points), successfully recovering complex deformations even from severely misaligned initializations where standard appearancebased tracking catastrophically fails.

ECCV 2026

Editing Everything Everywhere All at Once

Fabio Quattrini, Carmine Zaccagnino, Enis Simsar, Marta Gazulla, Rita Cucchiara, Alessio Tonioni, Silvia Cascianelli

Editing multiple elements of an image in a single forward pass is a practical alternative to multi-turn image manipulation, offering improved efficiency and potentially better harmonization. However, when several instructions target different regions, semantic interference often leads to attribute leakage and poor edit disentanglement, especially as the number of edits increases. In this work, we propose MICE (Multi-Instance Concurrent Editing), a training-free strategy for scalable multi-instance image editing with Multimodal Diffusion Transformers. MICE modifies the additive bias of joint attention to regulate interactions between instance-specific edit instructions, latent, and context tokens identified via user-provided segmentation masks. Specifically, MICE allows intra-instance attention, penalizes interactions between neighboring region tokens, and suppresses unrelated cross-instance attention. As a result, our method enforces attribute binding while preserving global visual consistency. We evaluate MICE on LoMOE-Bench and introduce MICE-Bench, a more challenging benchmark with an average of 8.5 concurrent edits per image. The experiments demonstrate that our approach outperforms strong baselines and recent competitors in terms of visual quality preservation and faithfulness to the editing instructions.

ECCV 2026

Token-Based Affordance Grounding with Large Vision-Language Models

Seung Il Lee, Qinqian Lei, Daguang Xu, Dong Yang, Robby T. Tan, Yixin Chen, Bo Wang

Affordance grounding aims to localize image regions that support a specific action, serving as a core capability for physical intelligence and embodied perception. Previous studies have primarily relied on weakly supervised learning with action labels from exocentric images. However, these methods often struggle with visually ambiguous exocentric images containing co-occurring actions; moreover, they fail to distinguish semantically similar actions because existing methods typically rely on brief action phrases that lack rich semantic details for action-specific localization. Although large vision-language models (LVLMs) encode rich action semantics and their action-conditioned textual outputs implicitly contain spatial cues, they do not directly provide action-specific spatial localization. To address these problems, we propose TokAG, a zero-shot affordance grounding framework that exploits the token-level semanticspatial signals in LVLMs to localize action-relevant regions without external supervision. We observe that attention maps associated with different LVLM output tokens vary significantly, with many attending to irrelevant regions such as the background. Thus, we introduce a spatialaware token-selection mechanism to systematically evaluate each output token and select the one whose attention maps exhibit dominant activation over the target object, instead of relying on arbitrary attention maps. By extracting these object-focused attention maps, we transform the LVLM’s implicit semantic signals into zero-shot affordance heatmaps. Our zero-shot framework consistently outperforms prior weakly supervised approaches across multiple benchmarks, improving NSS by 10.7% on the unseen split of AGD20K and by 29.7% on HICO-IIF. The code and models will be made publicly available.

ECCV 2026

TIDES: Time-Derivative Event Simulation via Deformable Reconstruction

Christopher Thirgood, Dipon Kumar Ghosh, Simon Hadfield

Event cameras emit asynchronous events in response to environmental appearance changes. The scarcity of real-world event datasets makes simulation essential. However, most simulators infer event timestamps from frame sequences, forcing many threshold crossings to share a small set of discrete times; a failure mode we term timestamp batching that worsens under fast motion and occlusion. We present TIDES, a continuous-time event simulator built on dynamic Gaussian splatting. Because TIDES operates on an explicit 3D scene representation with learnt geometry and motion, it can derive per-pixel intensity dynamics directly from the scene, rather than by differencing rendered frames. This enables accurate threshold-crossing prediction, including multiple crossings per rendering step, without temporal upsampling or frame interpolation. The same 3D scene model reveals where objects partially occlude one another; TIDES uses this to guide adaptive time stepping, concentrating computation only in regions where occlusion dynamics make simple models of brightness change unreliable. Finally, we model finite sensor bandwidth using a tile-level arbiter whose throughput, jitter, and event drops reproduce realistic sensor artifacts. Across paired RGB-event benchmarks, TIDES attains state-of-the-art event-stream fidelity. We also show that events simulated by TIDES transfer more effectively to real downstream tasks than competitors’.

ECCV 2026

ReactVAU: A Slow-Fast Decoupled Framework for Streaming Video Anomaly Understanding

Chia-Hui Chen, Shih-Ying Yeh, Fu-En Yang, Min-Hung Chen, Shang-Hong Lai

In this paper, we propose ReactVAU, a Slow-Fast Decoupled Framework for real-time streaming Video Anomaly Understanding (VAU). Existing VAU methods rely on o(cid:31)ine inference with global temporal sampling, which violates causality and prevents deployment in live surveillance streams. Conversely, general streaming video models satisfy causal access but dilute rare transient anomalies during memory compression and often invoke heavyweight MLLMs uniformly over long normal intervals. ReactVAU addresses this gap with three synergistic components: a lightweight Fast Detection Module based on Spatial Grid Folding (SGF) for continuous anomaly (cid:28)ltering; an Anomaly-Aware Persistent Memory (AAPM) that protects critical visual cues from temporal decay; and a heavyweight Slow Reasoning Module that remains dormant during normal streams and is awakened only by suspicious events for semantic veri(cid:28)cation and causal description. Extensive experiments on multiple benchmarks demonstrate that ReactVAU operates under strict streaming constraints while simultaneously achieving competitive performance in both anomaly detection and causal reasoning, alongside signi(cid:28)cantly enhanced computational e(cid:30)ciency by minimizing heavyweight MLLM invocations. Project page is available at https://huiyuiui.github.io/ReactVAU/ •

ECCV 2026

PaD-GS: Leveraging Distortion Map for Panoramic Gaussian Splatting

Yihang Xu, Qiulei Dong

Recently, 3D Gaussian Splatting (3DGS), which succeeded in perspective image rendering, has been extended to handle the panoramic image rendering task. However, the performance of the existing methods in literature is generally limited due to the severe distortions involved in panoramic images. To address this problem, we construct a distortion map under the panoramic imaging model, where the value of each pixel reflects its corresponding distortion degree, and accordingly, we propose a novel Panoramic Gaussian Splatting method by utilizing this Distortion map, called PaD-GS. The proposed PaD-GS, consisting of a distortion-aware Gaussian decoding module and a distortion-aware opacity modulation module, employs a whole-to-partial strategy to impose the distortion map on Gaussian representation learning. Specifically, the distortion-aware Gaussian decoding module is designed to decode the ensemble of learnable scene features and the distortion map into a set of Gaussian representations, so that the intrinsic distortion information of the distortion map could be injected into the whole Gaussian representations. The distortion-aware opacity modulation module is designed to further impose the distortion map by adaptively modulating the partial attribute (opacity) of each Gaussian representation. Thanks to the introduced distortion map, the proposed PaD-GS could alleviate the negative influence of severe distortion involved in panoramic images. Extensive experimental results on two panoramic datasets demonstrate that PaD-GS significantly outperforms several state-of-the-art methods for panoramic image rendering in most cases. The code is available at https://github.com/CosyXu/PaD-GS.

ECCV 2026

Mechanistic interventions for explainable digital pathology uncovers adversarial vulnerabilities

Arijit Patra, Samiran Dey, Ajitha Rajan, Greg Slabaugh, Tapabrata Chakraborti

The deployment of deep learning in digital pathology holds transformative potential for cancer diagnosis, yet its clinical adoption is hindered by the opaque and vulnerable nature of black-box models. In this work, we introduce a mechanistic examination framework that combines sparse autoencoders and activation patching to dissect adversarial vulnerabilities in pathology models as several learned features may correspond to non-morphological artifacts rather than clinically meaningful patterns. By isolating the neural circuits responsible for these vulnerabilities, we demonstrate that models often rely on spurious, nongeneralizable cues, particularly in middle layers of Vision Transformers and later convolutional blocks of ResNets. To address this, we propose Mechanism-Informed Adversarial Training, a novel approach that targets vulnerable circuits with precision regularization, achieving state-of-theart robustness while preserving clean performance. Crucially, our framework aligns with rapidly emerging clinical and regulatory demands for alignment, accountability, and safety in AI-driven diagnostics. By providing a mathematically rigorous, interpretable, and actionable method to audit and fortify deep learning models, we seek to bridge the gap between AI research and clinical deployment, offering a pathway to trustworthy, regulation-ready AI in healthcare. Our findings not only inform the field of adversarial machine learning but also set a precedent for mechanistically grounded artificial intelligence in real-world medical applications.

ECCV 2026

Steering 3D Generations: Preference Alignment via Direct Reward and Preference Optimization

Yuanhang Wang, Lizhe Qi, Wenqiang Zhang

Generating high-fidelity and controllable 3D assets from a single image remains a significant challenge. We propose a novel framework that integrates preference optimization into a rectified flow-based diffusion model operating on a latent set representation. This approach enhances geometric fidelity and user alignment, enabling the efficient synthesis of diverse 3D formats. Our core contribution is a versatile reward-based optimization strategy for aligning generation with human-centric criteria like surface quality. We introduce Direct Reward Optimization (DRO), which fine-tunes the model using only absolute quality feedback, removing the need for pairwise data. The framework also seamlessly incorporates Direct Preference Optimization (DPO) when paired data is available. Parameter-efficient control is achieved via Low-Rank Adaptation (LoRA). To ensure data quality, we also develop a robust preprocessing pipeline using a neural SDF reconstruction method with Gaussian curvature constraints to produce high-quality watertight meshes. Extensive experiments show our framework achieves state-of-the-art performance. Quantitative evaluations demonstrate significant improvements in Chamfer Distance and F-Score, while qualitative results confirm superior geometric fidelity and user preference alignment compared to existing methods.

ECCV 2026

WorldWander: Bridging Egocentric and Exocentric Worlds in Video Generation

Quanjian Song, Yiren Song, Kelly Peng, Yuan Gao, Mike Zheng Shou

Recent advances in video world models enable interactive environments with free navigation, making translation between first-person (egocentric) and third-person (exocentric) perspectives increasingly important. However, existing studies focus on unidirectional exocentric-toegocentric translation, overlooking reference-guided exocentric perspective synthesis. This capability is crucial for gaming and embodied AI applications. Motivated by this, we present WorldWander, an in-context learning framework tailored for translating between egocentric and exocentric worlds in video generation. Building upon advanced video diffusion transformers, WorldWander integrates (i) In-Context Perspective Alignment and (ii) Collaborative Position Encoding to model cross-view synchronization and character consistency. To support our task, we curate EgoExo-8K, a dynamic and scene-rich dataset containing synchronized egocentric–exocentric triplets from both synthetic and real-world scenarios. Experiments demonstrate that WorldWander achieves superior perspective synchronization, character consistency, and generalization, setting a new benchmark for egocentric-exocentric video translation.

ECCV 2026

Source-Agnostic Image Translation Based on Latent Aware Adaptive Masking

Tomislav Dobrički, Byung-Woo Hong

In this work, we propose a source-agnostic framework that dynamically refines a binary mask throughout the reverse diffusion process by computing the discrepancies of a pretrained diffusion model’s prediction for each latent time step. Rather than relying on a fixed threshold, our method introduces a time-dependent statistical thresholding scheme derived from the empirical mean and standard deviation of prediction discrepancies across the latent noisy images from the target distribution. This allows the mask to adapt to the model’s varying predictive confidence at different noise levels, effectively isolating domain-specific regions while preserving global structural coherence. Experimental results on the AFHQ and Celeba-HQ datasets demonstrate that our approach outperforms state-of-the-art unsupervised Image-to-Image methods in both realism (FID, KID) and faithfulness (SSIM, LPIPS). By requiring only a pretrained model of the target domain, our approach enables precise, automated localization and seamless translation across diverse source distributions without any specialized training. The project source code is available at: https://github.com/dtoma95/PM-Edit

ECCV 2026

LiveK12Bench: Have Large Multimodal Models Truly Conquered High School-level Examinations?

Xiaohan Wang, Mingze Yin, Yilin Zhao, sinbadliu sinbadliu, Dian Li

Advanced Large Multimodal Models (LMMs) have demonstrated impressive performance in K-12 reasoning tasks, exhibiting great promise as intelligent tutors. Realizing this potential requires models to navigate real-world examinations effectively, yet most existing benchmarks fail to capture the complexity of authentic testing environments. Specifically, most datasets are static, prone to data contamination, and are often confined to restricted modalities, disciplines, and evaluation criteria. To address these issues, we introduce LiveK12Bench, a dynamic, holistic, multi-disciplinary benchmark designed to evaluate the reasoning abilities of LMMs in realistic examination scenarios. LiveK12Bench comprises 2K+ verified questions spanning Mathematics, Physics, Chemistry, and Biology, sourced from the latest real-world exam papers and designed to grow over time. Our framework features several core innovations: 1) featuring an automated pipeline that continuously ingests and parses latest examination papers to mitigate data leakage; and 2) proposing a novel ’Mock Exam’ evaluation scheme, which assesses the ability to complete end-to-end exams autonomously with accurate and efficient reasoning paths. Extensive experiments on 12 LMMs reveal that advanced models suffer substantial performance degradation under exam-realistic constraints: GPT-5’s score drops from 79 to 53 (out of 100) when process rigor and efficiency are jointly evaluated. Our findings expose critical vulnerabilities, such as sensitivity to complex visual layouts, highlighting the gap between idealized reasoning capabilities and true educational readiness. Both code and dataset are publicly available.

ECCV 2026

Verifying Cancer Segmentation in Vision Transformers via Internal Concepts

Mengmeng Ma, Yunxiang Peng, Tang Li, Lu Lin, Binsheng Zhao, Oguz Akin, Xi Peng

Cancer segmentation models can fail silently, generating plausible but incorrect masks that risk missed findings or unnecessary biopsies. A critical question arises: Do AI models “know” when they are wrong, and if so, can we use the signal to predict their own failures? Humans do have a “Feeling of Error” (FOE): a spontaneous sense of unease that flags a potential error during thinking. We investigate whether cancer segmentation models exhibit an analogous internal signal. Unlike output-level cues (e.g., prediction confidence or uncertainty), which offer no insight into why a failure occurs and suffer from a sensitivity–quality tradeoff where high detection sensitivity could degrade overall segmentation quality. We instead propose to capture the model’s FOE from its inner workings. Using mechanistic interpretability tools, specifically Sparse Autoencoders, we decompose internal neural activations into a dictionary of human-interpretable concepts and show that failure cases exhibit a distinct latent signature: fewer active concepts with lower activation magnitudes compared to successful segmentation. By training a classifier on these concept activations, we achieve accurate failure detection along with explanations for the model’s mistakes. Experiments on prostate, pancreatic, and brain cancer segmentation demonstrate that our approach outperforms output-based methods in failure detection while preserving segmentation quality. Code is abailable at https://github.com/deep-real/CancerSegFailure.

ECCV 2026

DICE: Disentangled Instance-Class knowlEdge prompt tuning via SAE for Vision-Language Models

Daeun Lee, Seungwoo Jang, Kwangsu Kim

CLIP, known for its strong zero-shot generalization, has received significant attention in prompt tuning for its ability to adapt effectively to new tasks in few-shot settings. Recent studies have employed descriptions generated by Large Language Models (LLMs) as predefined prompts to obtain class-level semantics. However, because these predefined descriptions are not directly grounded in visual information, they often misalign with an image’s visual semantics. They tend to generate generic class-level descriptions rather than instance-specific ones. As a result, LLM-based prompts are structurally biased toward class-level semantics, making it difficult to capture and reflect instancelevel cues, which in turn limits generalization in few-shot settings where both levels of knowledge are crucial. To address this limitation, we propose Disentangled Instance-Class knowlEdge (DICE) prompt tuning, a framework that restructures LLM-derived class-level priors to capture instance-level semantics. Specifically, DICE decomposes LLM-derived priors into instance-level components using a Sparse Autoencoder (SAE), which selects instance-specific concept vectors. These concept vectors are then fused with class embeddings to form enriched representations. This synergy preserves class-level semantic coherence while capturing instance-level details, improving generalization to unseen classes. Our approach achieves competitive performance across 11 few-shot recognition benchmarks, while additionally offering a plug-and-play solution that enhances interpretability through SAE.

ECCV 2026

ARGENT: Adaptive Hierarchical Image-Text Representations

Chuong Huynh, Hossein Souri, Abhinav Kumar, Vitali Petsiuk, Deen Dayal Mohan, Suren Kumar

Large-scale Vision–Language Models (VLMs) such as CLIP learn powerful semantic representations but operate in Euclidean space, which fails to capture the inherent hierarchical structure of visual and linguistic concepts. Hyperbolic geometry, with its exponential volume growth, offers a principled alternative for embedding such hierarchies with low distortion. However, existing hyperbolic VLMs use entailment losses that are unstable: as parent embeddings contract toward the origin, their entailment cones widen toward a half-space, causing catastrophic cone collapse that destroys the intended hierarchy. Additionally, hierarchical evaluation of these models remains unreliable, being largely retrieval-based and correlation-based metrics and prone to taxonomy dependence and ambiguous negatives. To address these limitations, we propose an adaptive entailment loss paired with a norm regularizer that prevents cone collapse without heuristic aperture clipping. We further introduce an angle-based probabilistic entailment protocol (PEP) for evaluating hierarchical understanding, scored with AUC-ROC and Average Precision. This paper introduces a stronger hyperbolic VLM baseline ARGENT, Adaptive hieRarchical imaGe-tExt represeNTation. ARGENT improves the SOTA hyperbolic VLM by 0.7, 1.1, and 0.8 absolute points on image classification, text-to-image retrieval, and proposed hierarchical metrics, respectively.

ECCV 2026

Causal Intervention in Concept Bottleneck Models

Zhiyu Zhu, Jiayu Zhang, Zhibo Jin, Xinyi Wang, Fang Chen, Przemyslaw Biecek, Jianlong Zhou

Concept Bottleneck Models (CBMs) are a class of interpretable AI models that enable concept-level control over the decisionmaking process, allowing humans to directly intervene in model predictions. As a result, CBMs have been widely adopted in tasks requiring interpretability and controllability. Existing research has demonstrated that human-model interaction can signi(cid:28)cantly enhance CBM performance. However, in practical applications, each intervention requires manual human e(cid:27)ort and explicit data provision. Therefore, a central challenge is how to improve model performance while minimizing intervention e(cid:27)ort. In this paper, we show that trained CBM parameters encode usable concept-correlation structure. We leverage this structure during interaction by propagating user feedback through the shared inputto-concept pathway, thereby adjusting non-intervened concepts without training an additional realignment model. Experimental results demonstrate that our method improves low-intervention performance across multiple models and datasets. Compared with all tested baselines, our approach improves early-intervention performance by approximately 9%; compared with Concept Realignment, it improves AUC@10 and AUC@20 by 3.90% and 2.88%, respectively, while avoiding extra training. Code is available at https://github.com/LMBTough/CI. • •

ECCV 2026

DE2TR: Dual Evidence Detection Transformer for Video Temporal Grounding

Yifan Zhang, Chengxu Liu, Yujie Dun, Xueming Qian

Video temporal grounding (VTG) aims to localize relevant moments and predict saliency scores in untrimmed videos based on natural language queries. Recently, DETR-inspired methods have achieved instance-level moment predictions through query-based semantic evidence aggregation. However, unlike spatial objects with clear physical edges, video moments often exhibit ambiguous temporal boundaries due to the semantic gradient. Consequently, relying solely on global semantics can lead to severe boundary misalignment, yielding sub-optimal grounding results. To address this issue, we propose a novel Dual Evidence Detection Transformer (DE2TR) framework for better boundary localization. In particular, we introduce a dual-branch decoder that simultaneously captures global semantic evidence and fine-grained boundary cues. To prevent feature conflict between these distinct objectives, we channel-wise decouple the moment queries, facilitating independent evidence learning under the supervision of evidence alignment losses. Building upon this dual-branch design, we introduce a prior-guided refinement mechanism that employs explicit prior maps to steer queries toward potential regions of interest. Furthermore, a boundary augmentation strategy synthesizes ideal boundary patterns via simple temporal splicing, effectively enhancing the model’s boundary awareness. Extensive experiments on multiple popular benchmarks demonstrate that DE2TR significantly outperforms state-of-the-art approaches.

ECCV 2026

DiverseAD: A Large-Scale Driving Dataset with Diverse Atmospheric Conditions

Haoyu Wang, Baorui Ma, Donglin Di, Suhang Xuan, Hao Li, Shiliang Zhang

The strong generalization capability to diversified atmospheric conditions is crucial for autonomous driving models. However, models trained on existing datasets struggle with real-world complexity due to the homogeneous weather, limited maneuvers, and insufficient scale in training data. To overcome this issue, we introduce DiverseAD, a largescale driving dataset comprising 150K scenes that feature a great diversity in atmospheric condition, road types, and driving actions. Based on this dataset, we further propose a novel end-to-end autonomous driving model robust across diverse atmospheric conditions. More specifically, an atmospheric-invariant feature learning mechanism is proposed, which spots and disentangles atmospheric-agnostic features from visual inputs by using driving intent and scene structure cues as stable anchors. Our method thus allows the extracted features to be more invariant to changes in atmospheric conditions. Experiments show that DiverseAD is superior to existing public datasets in diversity, hence is valuable for training and benchmarking. Extensive comparisons also illustrate the superior performance of our proposed method.

ECCV 2026

Implicit Neural Representation for Spherical Harmonics Reconstruction of Motion-Corrupted Fetal Diffusion MRI

WENXUAN WU, Irina Grigorescu, Ruowen Qu, Jo Hajnal, J-Donald Tournier, Maria Deprez

Diffusion MRI of the fetal brain has potential to provide important insights into early development of white matter fibers. However, in vivo fetal acquisitions are severely corrupted by unpredictable motion, making the data unusable for downstream analysis. To address this challenge, we propose an implicit neural representation framework for fetal diffusion MRI reconstruction. The proposed method models the diffusion signal as a spatially and angularly continuous field by predicting direction-independent spherical harmonics (SH) coefficients from continuous spatial coordinates. Based on this representation, we formulate a physics-based forward slice acquisition model that links the underlying clean diffusion volume with the observed motion-corrupted slices, enabling joint optimization of the diffusion signal, fetal motion and acquisition artefacts. Furthermore, the single slice orientation acquisition typically used for each diffusion gradient introduces an ill-posed reconstruction problem along the slice-select direction. To mitigate this issue, we introduce a novel Through-Plane Continuity regularization loss that enforces smoothness in through-plane direction to suppress stripe artifacts, while preserving genuine anatomical details. Experiments on one in vivo fetal dataset and two simulated neonatal datasets demonstrate that the proposed framework achieves improved reconstruction fidelity, sharper anatomical features, and stronger robustness to acquisition artifacts. The code is available at: https://github.com/baby-MedIA/INR-dMRI-Recon

ECCV 2026

CanoVerse: 3D Object Scalable Canonicalization and Dataset for Generation and Pose

Li Jin, Yuchen Yang, Weikai Chen, Yujie Wang, Dehao Hao, Tanghui Jia, Yingda Yin, Zeyu HU, Runze Zhang, keyang luo, Li Yuan, Long Quan, Xin Wang, Xueying Qin

3D learning systems implicitly assume that objects occupy a coherent reference frame. Nonetheless, in practice, every asset arrives with an arbitrary global rotation, and models are left to resolve directional ambiguity on their own. This persistent misalignment suppresses pose-consistent generation, and blocks the emergence of stable directional semantics. To address this issue, we construct CanoVerse, a massive canonical 3D dataset of 320K objects over 1,156 categories – an order-ofmagnitude increase over prior work. At this scale, directional semantics become statistically learnable: CanoVerse improves 3D generation stability, enables precise cross-modal 3D shape retrieval, and strengthens zero-shot point-cloud orientation estimation even for out-of-distribution data. This is achieved by a new canonicalization framework that reduces alignment from minutes to seconds per object via compact hypothesis generation and lightweight human discrimination, transforming canonicalization from manual curation into a high-throughput data generation pipeline. Project page: https://github.com/123321456-gif/Canoverse

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