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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

Embed-RL: Reinforcement Learning for Reasoning-Driven Multimodal Embeddings

Haonan Jiang, Yuji Wang, Yongjie Zhu, Xin Lu, Wenyu Qin, Meng Wang, Pengfei Wan, Yansong Tang

Leveraging Multimodal Large Language Models (MLLMs) has become pivotal for advancing Universal Multimodal Embeddings (UME) in addressing diverse cross-modal tasks. Recent studies demonstrate that incorporating generative Chain-of-Thought (CoT) reasoning can substantially enhance task-specific representations compared to discriminative methods. However, the generated reasoning CoTs of existing generative embedding methods are limited to the textual analysis of queries and are irrelevant to the retrieval of the targets. To address these limitations, we propose a reasoning-driven UME framework that integrates Embedder-Guided Reinforcement Learning (EG-RL) to optimize the Reasoner to produce evidential Traceability CoT (T-CoT). Our key contributions are threefold: (1) We design an EG-RL framework where the Embedder provides explicit supervision to the Reasoner, ensuring the generated CoT traces are aligned with embedding tasks. (2) We introduce T-CoT, which extracts critical multimodal cues to focus on retrieval-relevant elements and provides multimodal inputs for the Embedder. (3) With limited computational resources, our framework outperforms the pioneering embedding model on both MMEB-V2 and UVRB benchmarks. The integration of multimodal evidence in structured reasoning, paired with retrieval-oriented alignment, effectively strengthens cross-modal semantic consistency and boosts the model’s fine-grained matching capability as well as its generalization across complex scenarios. Our work demonstrates that targeted reasoning optimization can significantly improve multimodal embedding quality, providing a practical and efficient solution for reasoning-driven UME development. Project page.

ECCV 2026

RegVGGT: Sustainable Visual Geometry Grounding for Streaming via Regulated Memory

Hongbo Mao, Junjun Jiang, Youyu Chen, Jiaxin Zhang, Zhemeng Dong, Xianming Liu

3D reconstruction from a lengthy video stream input poses a dilemma for feed-forward reconstruction models (FFRMs), that a whole-stream inference context cannot be retained under limited GPU memory. Recent studies seek to resolve this problem via a trade-off between the integrity of inference context and GPU memory usage, which either suffer from a rapid memory inflation or degraded context integrity due to artificially capping memory usage. Driven by our key observation that the initial saliency of a token reliably dictates its long-term importance across the stream, we propose RegVGGT, a training-free token regulation method which aggressively regulates the tokens of incoming frames. By admitting at most 1% of tokens per frame to update the context memory, our method dramatically suppresses memory inflation as the stream progresses. Equipped with a FlashAttention-compatible token saliency estimation scheme, RegVGGT is capable of processing thousands of frames on a consumer-grade GPU with negligible compromise to reconstruction quality. Extensive experiments demonstrate that RegVGGT achieves state-of-the-art performance on long-horizon benchmarks across diverse FFRM prediction tasks, surpassing prior FFRMbased stream reconstruction baselines by a large margin.

ECCV 2026

Probing the 3D Object-Level Understanding of Pre-Trained Detection Transformers

Robin Kim, Colin Samplawski, Benjamin Marlin

Detection transformer models, including DETR and its extensions, learn to output a set of object-level embeddings that can be simultaneously decoded into 2D bounding boxes and class distributions. In this paper, we investigate what pre-trained 2D detection transformers understand about the 3D properties of objects. Specifically, we investigate the extent to which properties including the depth of objects from the camera and the 3D location of objects relative to the camera can be recovered from object-level embeddings using linear and non-linear probes. Across a range of detection transformer models, our results show a surprisingly strong and previously unknown ability of 2D DETR models to represent useful information about the 3D properties of objects, despite the complete lack of 3D supervision during model pre-training.

ECCV 2026

Bounding-Box Trajectories Matter for Video Anomaly Detection

Inpyo Song, Jangwon Lee

Video anomaly detection is critical for public safety and security, yet remains highly challenging despite extensive research due to large variations in appearance, viewpoint, and scene dynamics. Among existing approaches, human pose-based methods have emerged as a major line of research, showing strong performance since many anomalies in public datasets involve humans and pose representations are robust to appearance changes while providing compact motion descriptions. However, these methods often overlook bounding-box trajectories, although such information is inherently available in pose-based pipelines. In this paper, we explicitly leverage these trajectories as a primary anomaly cue. We present TrajVAD, a framework that models multi-class boundingbox trajectories using normalizing flows to learn normal kinematic patterns. Its trajectory-only variant, TrajVAD-T, eliminates pose estimation, reaches 87.7 AP on ShanghaiTech, and achieves the best results on MSAD among compared methods. TrajVAD-P adds a reliability-gated pose branch and improves performance to 88.6 AUROC and 90.9 AP on ShanghaiTech, establishing bounding-box trajectories as an effective modality for video anomaly detection.

ECCV 2026

Decoupling Moment from Event for Video Temporal Grounding

Yuda Zou, Boxiang Zhou, Xin Zhou, Yibo Chen, Dejia Song, Xu Tang, Yao Hu, Yongchao Xu

Video temporal grounding (VTG) aims to localize events in untrimmed videos given natural language prompts. Current VTG methods directly inherit the paradigm from 2D object detection, applying rigid IoU-based one-to-one matching between queries and ground truth spans. However, this paradigm fundamentally conflicts with the temporal fluidity of video events: unlike spatial objects, a sub-event (moment) can be semantically aligned with the prompt despite only covering part of the full event span. This matching scheme incorrectly suppresses such moment predictions as negatives, preventing models from learning rich temporal representations. To resolve this, we propose Moment-Event DETR (ME-DETR), a novel framework that embraces the natural part-whole structure of events through dynamic query specialization. During training, after standard Hungarian matching assigns primary event queries to ground truths, we identify other high-confidence predictions within each matched span and designate them as auxiliary moment queries. Through our synergistic supervision strategy, these moment queries are liberated from unreasonable suppression to explore semantically rich sub-events (moments). Extensive experiments demonstrate that ME-DETR establishes new state-of-the-art results, achieving +2.43% mAP improvement on QVHighlights test set without requiring post-processing NMS. Code: https://github.com/zouyuda220/ME-DETR.

ECCV 2026

FoundDP: Revisiting Weak Disparity Observability in Dual-Pixel Depth Estimation

Fengchen He, Hao Xu, Dayang Zhao, Tingwei Quan, Shaoqun Zeng

Dual-pixel (DP) imaging enables metric depth estimation from a single camera using sub-aperture disparity. However, the extremely small e(cid:27)ective baseline limits disparity observability, leading to structural degradation and depth failure in textureless, low-contrast, or downsampled regions. Existing DP-based methods rely primarily on local disparity cues and therefore become unreliable when disparity signals are weak or ambiguous. To address this limitation, we propose FoundDP, a uni(cid:28)ed framework that integrates metric DP depth with global structural priors from a monocular depth foundation model. Our method preserves metric scale through DP-derived depth and leverages Vision Transformer (ViT) features to restore structural consistency in weakdisparity regions. To ensure reliable metric guidance under DP imaging conditions, we identify and mitigate ViT representation degradation induced by DP defocus blur via ViT feature alignment, enabling stable metric-guided depth estimation. Extensive experiments on synthetic and real-world DP benchmarks show that FoundDP delivers superior performance, with consistent gains in structural (cid:28)delity and metric accuracy, especially under reduced disparity observability. Code will be available at: https://github.com/EchoLighting/FoundDP • •

ECCV 2026

Physics Question Scene Graph: Fine-grained Evaluation of Physical Plausibility in Text-to-Video Generation

Atin Pothiraj, Jaemin Cho, Yue Zhang, Elias Stengel-Eskin, Mohit Bansal

Video generation models are increasingly capable of producing realistic videos, but they still struggle to generate videos that follow basic physical laws. Compounding this is a lack of reliable granular evaluation methods for localizing and specifying physical law violations in videos. We address this by introducing Physics Question Scene Graph (PQSG), a hierarchical question-based evaluation pipeline. PQSG evaluates generated videos by checking their faithfulness to a prompt across objects, actions, and adherence to physical laws using a graph-based hierarchy of questions generated by a vision-language model (VLM), guided by high-quality in-context examples. By representing questions as a graph, PQSG introduces logical dependencies within questions, ensuring that each query is contextually valid. Moreover, PQSG provides granular assessments of which qualities of the video violate physical plausibility constraints. We validate PQSG by creating FinePhyEval, a dataset with physics-based prompts and corresponding generated videos from diverse state-of-the-art video generation models (Sora 2, Veo 3, and Wan 2.1), with each video annotated across multiple categories by humans. Using FinePhyEval, we measure the correlation between PQSG’s fine-grained scores and human judgments, showing higher overall correlations than prior work. We also find that PQSG ranks closedsource models higher than Wan 2.1 on physical realism. Lastly, we show that the annotations we provide in FinePhyEval can also be used for subtask evaluation: we benchmark two strong VLMs on generating and answering questions, finding that while models can create human-like questions, they still fall short of human performance in answering them.

ECCV 2026

Render-in-the-Loop: Vector Graphics Generation via Visual Self-Feedback

Guotao Liang, Zhangcheng Wang, Juncheng Hu, Haitao Zhou, Ziteng Xue, Jing Zhang, Dong Xu, Qian Yu

Multimodal Large Language Models (MLLMs) have shown promising capabilities in generating Scalable Vector Graphics (SVG) via direct code synthesis. However, existing paradigms typically adopt an open-loop “blind drawing” approach, where models generate symbolic code sequences without perceiving intermediate visual outcomes. This methodology severely underutilizes the powerful visual priors embedded in MLLMs’ vision encoders, treating SVG generation as a disjointed textual sequence modeling task rather than an integrated visuo-spatial one. Consequently, models struggle to reason about partial canvas states and implicit occlusion relationships, which are visually explicit but textually ambiguous. To bridge this gap, we propose Render-in-the-Loop, a novel generation paradigm that reformulates SVG synthesis as a stepwise, visual-context-aware process. By rendering intermediate code states into a cumulative canvas, the model explicitly observes the evolving visual context at each step, leveraging on-the-fly feedback to guide subsequent generation. However, we demonstrate that applying this visual loop naively to off-the-shelf models is suboptimal due to their inability to leverage incremental visual-code mappings. To address this, we first utilize fine-grained path decomposition to construct dense multi-step visual trajectories, and then introduce a Visual Self-Feedback (VSF) training strategy to condition the next primitive generation on intermediate visual states. Furthermore, a Render-and-Verify (RaV) inference mechanism is proposed to effectively filter degenerate and redundant primitives. Our framework, instantiated on a multimodal foundation model, outperforms strong open-weight baselines on the standard MMSVGBench. This result highlights the remarkable data efficiency and generalization capability of our Render-in-the-Loop paradigm for both Text-to-SVG and Image-toSVG tasks.

ECCV 2026

Variational Patch Gating for Training-Free Few-Shot Classification

Ahmed Radwan, Ahmad Abdel-Qader, Islam Osman, Mohamed Shehata

Few-shot classification from a frozen backbone collapses when the target domain lies far from the pre-training distribution, because global embeddings map unrelated classes to nearly identical CLS tokens. Local patch descriptors are more resilient: an edge or texture gradient activates similar features whether it appears in a photograph or an X-ray. Yet part-level matching remains largely unexplored for few-shot recognition. The few methods that attempt it formulate matching as optimal transport or bipartite assignment, requiring iterative solvers whose cost grows rapidly with shots. We take a different approach. Instead of solving a matching optimization, we formulate patch-based classification as probabilistic evidence accumulation under a discriminative variational model. Each query patch carries a Bernoulli latent variable; minimizing the resulting assignment free energy yields a closed-form sigmoid gate and a softplus evidence score whose per-patch contribution saturates at −log(1−π), bounding clutter influence without iterative solvers or learned parameters. We evaluate extensively across twelve datasets spanning satellite, medical, fine-grained, and natural-image domains. Without any gradient updates, VPG outperforms the best fine-tuned baseline on five of ten dataset-settings and exceeds the previous training-free state of the art by +5.35 points on CDFSL. With lightweight episodic fine-tuning, VPG reaches 71.06% on CDFSL and 81.8% on four unseen Meta-Dataset domains.

ECCV 2026

DAP: Doppler-aware Point Network for Heterogeneous mmWave Action Recognition

Jiaying Lin, Shiman Wu, Jinfu Liu, Can Wang, Mengyuan Liu

Millimeter-wave (mmWave) radar provides privacy-preserving sensing and is valuable for human action recognition (HAR). Existing mmWave point cloud datasets are limited in scale and mostly collected under homogeneous single-source settings, preventing current methods from handling real-world distribution shifts caused by heterogeneous radar sources, such as different devices and frequency bands. To address this, we introduce UniMM-HAR, the largest and first mmWave point cloud HAR dataset for heterogeneous multi-source scenarios, standardizing three distinct radar configurations to realistically evaluate crosssource generalization. We further propose the Doppler-aware Point Cloud Network (DAP-Net) to tackle heterogeneity challenges. DAP-Net enhances intra-modal representations and performs cross-modal alignment to learn source-invariant action semantics. Leveraging action-consistent spatio-temporal Doppler patterns as anchors, the Dual-space Doppler Reparameterization (D²R) module performs sample-adaptive geometric densification and Doppler-guided feature recalibration, while the Text Alignment Module (TAM) provides stable semantic anchors via a pretrained textual space. Experiments show that DAP-Net significantly outperforms existing methods under heterogeneous radar settings, achieving state-of-the-art accuracy and strong cross-source robustness. Code and dataset are publicly available at https://github.com/jolin830/DAP-Net.

ECCV 2026

Taming LLMs for Codematic Indoor Scene Generation

Yixun Liang, Qianyi Wu, Chuan Fang, Rui Chen, Jiahang Liu, Jianfeng Zhang, Ping Tan

The generation of plausible layouts is a critical step in textdriven 3D scene synthesis. While Large Language Models (LLMs) have shown promise in high-level organization in this task, their native finegrained spatial placement remains a significant bottleneck, often requiring in-context guidance or complex agentic reflections to fix errors. This paper focuses directly on strengthening the core spatial ability by finetuning LLM to generate structured scene code (codematic indoor scene generation). We identify and address two fundamental issues hindering the LLM from doing this: data scarcity and poor instruction following. First, to mitigate data scarcity, we consolidate multiple heterogeneous datasets, spanning procedural, real-world, and professionally designed scenes, into a unified, large-scale training corpus. This dataset comprises 52K rooms and 280k data pairs, featuring diverse descriptions at both the scene and object levels. Second, even with abundant data, LLMs exhibit poor instruction following due to an information imbalance, where the tokenheavy layout history overwhelms the concise user prompt. To resolve this, we propose SceneSpinner, a framework that introduces a language-based planning stage to provide high-level reasoning and a novel Conditional Mutual Information (CMI) regularization to explicitly force the model to focus on user instructions. Experiments demonstrate that our approach significantly improves the ability of LLMs to generate plausible, diverse, and instruction-aligned 3D layouts.

ECCV 2026

Rdm: Re-conceptualizing Distribution Matching as a Reward for Diffusion Distillation

Linqian Fan, Peiqin Sun, Tiancheng Wen, Shun Lu, Chengru Song

Diffusion models achieve state-of-the-art generative performance but are fundamentally bottlenecked by their slow, iterative sampling process. While diffusion distillation techniques enable high-fidelity, few-step generation, traditional objectives often restrict the student’s performance by anchoring it solely to the teacher. Recent approaches have attempted to break this ceiling by integrating Reinforcement Learning (RL), typically through a simple summation of distillation and RL objectives. In this work, we propose a novel paradigm by re-conceptualizing distribution matching as a reward, denoted as R . This unified perspective bridges the algorithmic gap between Diffusion Matching Distillation (DMD) and RL, providing several primary benefits: (1) Enhanced Optimization Stability: We introduce Group Normalized Distribution Matching (GNDM), which adapts standard RL group normalization to stabilize R estimation. By leveraging group-mean statistics, GNDM establishes a more robust and effective optimization direction. (2) Seamless Reward Integration: Our reward-centric formulation inherently supports adaptive weighting mechanisms, allowing for the fluid combination of DMD with external reward models. (3) Improved Sampling Efficiency: By aligning with RL principles, the framework readily incorporates Importance Sampling (IS), leading to a significant boost in sampling efficiency. Extensive experiments demonstrate that GNDM outperforms vanilla DMD, reducing the FID by 1.87. Furthermore, our multi-reward variant, GNDMR, surpasses existing baselines by striking an optimal balance between aesthetic quality and fidelity, achieving a peak HPS of 30.37 and a low FID-SD of 12.21. Ultimately, R provides a flexible, stable, and efficient framework for real-time, high-fidelity synthesis. Code is available at: https://github.com/Flq2002/Rdm.

ECCV 2026

4D-VGGT: A SpatioTemporal Foundation Model for Dynamic Scene Geometry Estimation

Haonan Wang, Hanyu Zhou, Haoyue Liu, Luxin Yan

We investigate a challenging task of dynamic scene geometry estimation, which requires representing both spatial and temporal features. Typically, existing methods align two features into a unified latent space to model scene geometry. However, this unified paradigm suffers from potentially mismatched representations due to the heterogeneous nature between spatial and temporal features. In this work, we propose 4D-VGGT, a general foundation model with divide-and-conquer spatiotemporal representation for dynamic scene geometry. Our model is divided into three aspects: 1) Multi-setting input. We design an adaptive visual grid that supports input sequences with arbitrary numbers of views and time steps. 2) Multi-level representation. We propose a cross-view global fusion for spatial representation and a cross-time local fusion for temporal representation. 3) Multi-task prediction. We append multiple task-specific heads to spatiotemporal representations, enabling a comprehensive visual geometry estimation for dynamic scenes. Under this unified framework, these components enhance the feature discriminability and application universality of our model for dynamic scenes. In addition, we integrate multiple geometry datasets to train our model and conduct extensive experiments to verify the effectiveness of our method across various tasks on multiple dynamic scene geometry benchmarks. Our code is released at https://github.com/Haonan-Wang-aurora/4D-VGGT.

ECCV 2026

MedQ-Deg: A Multidimensional Benchmark for Evaluating MLLMs Across Medical Image Quality Degradations

Jiyao Liu, Junzhi Ning, Chenglong Ma, Wanying Qu, Jianghan Shen, Siqi Luo, Jinjie Wei, Jin Ye, Pengze Li, Tianbin Li, Jiashi Lin, Hongming Shan, Xinzhe Luo, Xiaohong Liu, Lihao Liu, Junjun He, Ningsheng Xu

Despite impressive performance on standard benchmarks, multimodal large language models (MLLMs) face critical challenges in real-world clinical environments where medical images inevitably suffer various quality degradations. Existing benchmarks exhibit two key limitations: (1) absence of large-scale, multidimensional assessment across medical image quality gradients and (2) no systematic confidence calibration analysis. To address these gaps, we present MedQ-Deg, a comprehensive benchmark for evaluating medical MLLMs under image quality degradations. MedQ-Deg provides multi-dimensional evaluation spanning 18 distinct degradation types, 30 fine-grained capability dimensions, and 7 imaging modalities, with 24,894 question-answer pairs. Each degradation is implemented at 3 severity degrees, calibrated by expert radiologists. We further introduce Calibration Shift metric, which quantifies the gap between a model’s prediction-consistency-based perceived confidence and actual performance to assess metacognitive reliability under degradation. Our comprehensive evaluation of 40 mainstream MLLMs reveals several critical findings: (1) overall model performance degrades systematically as degradation severity increases, (2) models exhibit a Dunning-Kruger-like overconfidence pattern, maintaining inappropriately high confidence despite severe accuracy collapse, and (3) models display markedly differentiated behavioral patterns across capability dimensions, imaging modalities, and degradation types. We hope MedQ-Deg drives progress toward medical MLLMs that are robust and trustworthy in real clinical practice.

ECCV 2026

iSyncTab: Learning Cross-Modal Feature Sequencing for Image-Tabular Data via Neural Synchrony

Al Zadid Sultan Bin Habib, Md Younus Ahamed, Prashnna Gyawali, Gianfranco Doretto, Donald Adjeroh

Multimodal learning of image and tabular data is a challenging problem, especially given the unstructured feature representations involved. We address this by framing feature sequencing as a Column Permutation Problem and showing how reordering columns can reduce dispersion and improve training stability for multimodal learning of image and tabular data. We propose iSyncTab, an approach that enforces these learned sequential constraints through a joint classification and order-consistency objective. We introduce Neural Synchrony-guided Paired Feature Sequencing (NS-PFS), a neuroscience-inspired NeuroAI approach that aligns image and tabular feature clusters through a Hungarian assignment of their synchrony matrix, derived from cluster energy and centroid similarity. The resulting cross-modal feature sequence provides a coherent feature alignment that preserves structural synchrony between modalities. We then introduce an Order-aware Memoryaugmented Transformer (OMT) with a custom loss function that enforces these learned feature sequence constraints. Experiments on multimodal benchmarks demonstrate that systematically derived feature sequences consistently boost predictive performance and model robustness. Our findings underscore feature sequencing as an important inductive bias for structured multimodal learning of image and tabular data. Neural synchrony-driven sequencing enhances cross-modal coherence and representation quality for image and tabular data.

ECCV 2026

Fourier Compressor: Frequency-Domain Visual Token Compression for Vision-Language Models

Huanyu Wang, Jushi Kai, Haoli Bai, Lu Hou, Bo Jiang, Ziwei He, Zhouhan Lin

Vision-Language Models (VLMs) incur substantial computational overhead and inference latency due to the large number of visual tokens introduced by high-resolution image and video inputs. Existing parameter-free token compression methods typically rely on token selection or merging, yet they risk discarding substantial visual information or distorting the original representation distribution, resulting in pronounced performance degradation at high compression ratios. In response, we aim to explore a more effective and efficient visual token compression strategy, with a promising direction in the frequency domain. Motivated by the success of frequency-domain transforms in image compression (e.g., JPEG), we systematically analyze the frequency redundancy in visual representations and uncover a nonuniform distribution of semantic information across frequency bands. Building upon this, we introduce Fourier Compressor, an effective, parameter-free, and highly generalizable module that removes redundancy from visual representations within the frequency domain. Implemented via FFT with O(n2 logn) complexity and no additional parameters, Fourier Compressor introduces negligible computational overhead while preserving semantic fidelity. Extensive experiments on imagebased benchmarks demonstrate that our method achieves a favorable performance-efficiency trade-off, retaining over 96% of the original accuracy while reducing inference FLOPs by up to 83.8% and boosting generation speed by 31.2%. It consistently outperforms existing parameterfree methods and even surpasses some parameterized approaches. Importantly, Fourier Compressor generalizes consistently across both LLaVA and Qwen-VL architectures, and further extends to video understanding tasks, highlighting its practical applicability for efficient VLMs.

ECCV 2026

UF0-6D: Unified Flow-based Zero-Shot 6D Object Pose Estimation without Refinement

Yingnan Guo, Chun Lim, Yu Feng, Yu Zhang

Existing zero-shot 6D object pose estimation methods predominantly rely on multi-stage pipelines with iterative render-and-compare refinement or coarse-to-fine hypothesis selection, leading to substantial computational overhead and implementation complexity. Generative modeling on geometric manifolds offers an attractive alternative by directly representing pose posteriors. In this work, we present UF0-6D, a Unified Flow-based Zero-shot framework for 6D object pose estimation without refinement. UF0-6D reformulates pose estimation as conditional Riemannian flow matching on SE(3), learning an instance-conditioned posterior via geodesic-consistent bridge velocities and symmetry-aware Riemannian optimal transport supervision. A single ODE integration pass replaces coarse-to-refine cascades and iterative alignment, unifying modelbased and model-free settings via a geometry-conditioning design that maps both CAD renders and reference-view renders into a shared pointtoken representation. Our RGB-D variant achieves a mean AR of 81.2 on BOP benchmark datasets, surpassing the previous state-of-the-art by +6.0 AR, while our RGB variant reaches 70.6 AR at only 0.379s per image, establishing a new accuracy–speed Pareto frontier.

ECCV 2026

DRIFT: Difficulty-aware Rectified Flows for Through-plane MRI Super-Resolution

Yoonseok Choi, Eun-Gyu Ha, Daniel Kim, Mohammed Al-masni, Ming-Hsuan Yang, Dong-Hyun Kim

Magnetic Resonance Imaging (MRI) is often acquired with anisotropic resolution to reduce scan time, producing stair-step artifacts along the through-plane direction. In through-plane MRI superresolution, an efficiency–fidelity trade-off arises: feed-forward regressors are fast but oversmooth at large slice-thicknesses, while sampling-based methods improve fidelity at high inference cost. We propose DRIFT, a two-stage thickness-conditioned rectified flow framework for throughplane MRI super-resolution with continuous input slice-thickness. Stage 1 employs an Anatomical Projection Network (APN) to map low-resolution patches to a coarse high-resolution manifold, providing a deterministic anatomical initialization that shortens the residual transport of Stage 2 and stabilizes slice-wise refinement. Stage 2 refines details via rectified flow and introduces a Physics-Aware Difficulty (PAD) metric derived from slice-thickness induced through-plane bandwidth deficit to guide an Adaptive Integration Scheduler (AIS), allocating ODE steps by thickness. A Consistent Endpoint Trajectory Alignment (CETA) loss enforces thickness-consistent reconstructions. Experiments show that DRIFT outperforms super-resolution baselines while reducing inference cost. Code, models, and interactive demos are available at https://yoonseokchoiai.github.io/drift-eccv2026/.

ECCV 2026

OmniLife360: A Benchmark for 3D Reconstruction from In-the-Wild 360° Captures

Zonglin Zhao, Bowen Zhang, Yatai Li, Chao.Liang Chao.Liang, Zhipeng Zhang

The widespread adoption of consumer 360° cameras has created a growing demand for robust in-the-wild panoramic 3D reconstruction. Existing methods remain fragile on long-horizon trajectories with strong ego-motion and dynamic interference. Quantifying this gap at scale is challenging because available benchmarks are often short, constrained, or captured in controlled settings, limiting evaluation and progress. To address this, we introduce OmniLife360, a benchmark for 3D reconstruction from in-the-wild 360° captures. It spans diverse scenes, environments, and motion dynamics captured by individuals across real-world activities, enabling rigorous evaluation of reconstruction robustness in complex natural conditions. Extensive evaluations reveal that state-ofthe-art methods excel in controlled settings but suffer significant degradation in dynamic scenarios. Motivated by these findings, we propose Omni4DGS, a motion-aware Gaussian Splatting framework that combines motion decomposition with lightweight supervision to better handle motion variations, dynamic distractors, and long-horizon pose drift. Experiments show that Omni4DGS consistently improves reconstruction quality and stability in dynamic 360° scenarios, reducing motion-induced artifacts and producing cleaner renderings under strong scene motion. The benchmark is available at https://huggingface.co/datasets/ AutoLab-SJTU/OmniLife360.

ECCV 2026

Beyond Absolute Scores: Relative Edit-induced Difference for Generalizable Image Aesthetic Assessment

Qifei Jia, Xintong Yao, Minghao Li, Yajie Chai, Qiming Lu, Baoyue Shen, Yasen Zhang, Runyu Shi, Ying Huang, Yue Zhang

Traditional Image Aesthetic Assessment (IAA) methods mainly rely on regressing absolute Mean Opinion Scores (MOS). However, such a paradigm overlooks the inherently dynamic nature of human aesthetic perception, which relies on subconscious comparison against implicit visual references. Consequently, the lack of causal reasoning regarding aesthetic differences prevents models from learning generalizable aesthetic principles, thus limiting their generalization across diverse scenarios. In this work, we rethink the IAA task and propose Relative Editinduced Difference Aesthetic learning (RED-Aes), a novel framework that leverages controllable image editing models to simulate the human aesthetic reasoning process. Instead of fitting absolute score distributions, RED-Aes explicitly learns the visual factors that drive aesthetic changes. To support this paradigm, we construct the RED-20k dataset, which comprises editing-based image pairs, quantitative aesthetic differences, and Chain-of-Thought (CoT) reasoning. Furthermore, we introduce a three-stage training strategy guided by a relative ranking consistency reward, optimizing the model solely via relative supervision. Extensive experiments demonstrate that RED-Aes achieves state-of-the-art performance on multiple public benchmarks, exhibiting superior generalization capabilities.

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