Past ArXiv / July 24, 2026

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10 papers

Vision-Language Reasoning

1 Engine-Native Editable 3D World Reconstruction with Objects and Lighting Junhao Chen, Xinghao Chen, Henghaofan Zhang, Zihao Qiao, Saining Zhang, Yongzhi Li, Ruqi Huang, Sisi Li, Yimin Sheng, Jianyi Zhu, Hao Zhao

Vision-Language Reasoning Benchmark & Evaluation Embodied Interaction Generative Modeling

ArXiv

Paper 1 / arXiv:2607.20889 Open arXiv

Relevance 9 Novelty 8

Why selected: Matches criterion 3 very closely: an embodied/3D scene benchmark and method for engine-native editable reconstruction with object instances and parametric lights; also touches criterion 2 via VLM-based parsing.

Editable 3D scene creation requires object instances and lights that can be inspected, moved, and imported into standard engines, yet existing single-image methods largely stop at room-scale geometry, baked/global illumination, or text-driven generation. We introduce Lumera (Light-aware Unified Engine-native Reconstruction and Assembly), a benchmark and reference pipeline for engine-native, light-aware 3D scene parsing from a single image. Lumera-2K is built from 2,513 UE5 projects and provides 3.73M components, 63M object instances, 102.6K engine-native parametric lights, and 95.1K camera views. On this data, Lumera-Box and Lumera-Light adapt VLM to parse object boxes and parametric light tuples (x,y,z,r,g,b,I), which are assembled with per-object mesh reconstruction, HDR environment estimation, and a bounded agentic refinement loop. In a sanitized box benchmark against DetAny3D, SpatialLM, N3D-VLM, and WildDet3D, Lumera-Box obtains the strongest overall detection, geometry, semantic, and layout scores (merged mAP 0.1141, IoU-B 0.2472, F-score 0.2762), while WildDet3D remains stronger on anchor recall. For lights, Lumera-Light recovers almost all non-empty scenes (recall 0.998) but remains limited at individual-light localization (F1 0.209 at 0.5 m); matched lights have median position error 0.261 m, median {\Delta}E2000 4.59, and intensity Pearson r=0.628. These results establish parametric lights as a measurable editable-scene target and expose remaining bottlenecks in relation structure, light recall/intensity, and cross-engine generalization.

2 Do Pathology Vision-Language Models Truly See Pathology? Chengyang Zhang, Wenchuan Zhang, Bo Li, Xinyu Liu, Jiaming Yang, Mengran Li, Chenxun Deng, Jie Chen, Yang Zhang, Wei Ju, Yuhao Yi, Hong Bu, Jiancheng Lv

Vision-Language Reasoning Multimodal LLM Benchmark & Evaluation Medical AI

ArXiv

Paper 2 / arXiv:2607.21065 Open arXiv

Relevance 8 Novelty 6

Why selected: Matches criterion 2 very closely: it studies pathology vision-language models and introduces PathBind to benchmark visual-semantic binding and grounding.

Pathology vision-language models (VLMs) have recently progressed rapidly and are commonly evaluated by answer accuracy on pathology VQA benchmarks. However, we dig into current evaluations and identify three overlooked issues: 1) Visual evidence is not always necessary. For instance, Gemini-3-Pro achieves 53.5% average accuracy across 5 VQA benchmarks without any visual input. 2) Domain training can improve accuracy without proportional gains in visual binding. Compared with Qwen2.5-VL-7B, Patho-R1-7B exhibits a 5.8-point lower multimodal gain and a 3.7-point lower attention IoU. 3) Entity-level attention is diffuse and weakly query-specific. On PathVG, attention maps remain highly correlated across different entity queries. These issues can lead to substantial misjudgments of pathology VLMs' actual multimodal capabilities. To this end, we present PathBind, a benchmark comprising 2,600 samples: PathBind-VQA with 1,500 questions across six dimensions, PathBind-PTA with 600 questions from a private pathology teaching atlas, and PathBind-Grounding with 500 expert-curated region-level samples. Each component undergoes task-specific automated filtering and expert review to reduce textual shortcuts and improve entity-region correspondence. We evaluate 18 representative VLMs on VQA samples of PathBind and five existing pathology VQA benchmarks, and further evaluate 10 VLMs on PathBind-Grounding and PathVG. Results show that current pathology VLMs still exhibit a substantial gap between answer-side performance and visual-semantic binding.

4 HyperImageNet: A Large-Scale High-Spatial Resolution Hyperspectral Imagery Classification Benchmark Chuguang Zeng, Jingtao Li, Yinhe Liu, Yanfei Zhong

Vision-Language Reasoning Benchmark & Evaluation

ArXiv

Paper 4 / arXiv:2607.21050 Open arXiv

Relevance 7 Novelty 6

Why selected: Matches criterion 4 closely: a new large-scale hyperspectral imagery benchmark with an associated foundation model and open-environment evaluation.

We present HyperImageNet, a large-scale benchmark for fine-grained hyperspectral land-cover understanding. The dataset contains 26,084 airborne hyperspectral image patches with 224 spectral bands and 138 fine-grained land-cover categories. Unlike existing datasets, HyperImageNet provides raw imagery, pixel-level semantic labels, and object-level instance masks, supporting both semantic and instance segmentation. Furthermore, we establish an open-environment benchmark with strict spatial separation to evaluate representative methods and the HyperFree foundation model. Experimental results demonstrate the effectiveness of HyperImageNet for fine-grained hyperspectral understanding and open-environment remote sensing research.

5 Self-Supervised Learning of Structured Dynamics from Videos Lukas Knobel, Andrew Zisserman, Yuki M. Asano

Vision-Language Reasoning Benchmark & Evaluation LLM Interpretability Time-Series LLM

ArXiv

Paper 5 / arXiv:2607.21576 Open arXiv

Relevance 5 Novelty 6

Why selected: Matches criterion 1 moderately well: it proposes a new representation for structured dynamics from videos, which is useful for spatial/motion understanding in visual learning.

Understanding motion in video is a fundamental challenge for visual learning, as frame-to-frame change entangles two sources of dynamics: camera motion and object motion. This decomposition has remained underexplored in representation learning, partly because these factors are tightly coupled in natural videos and difficult to supervise separately. Yet recovering it is important for learning robust motion representations that separate meaningful object dynamics from camera-induced variation. We study whether such structured motion representations can be recovered from frozen features of a pretrained image vision transformer. We propose the Structured Dynamics Model (SDM), which explicitly separates the dominant source of temporal change from residual dynamics through future-feature prediction, rather than representing video change with a single entangled latent or with unstructured, spatially dense transition tokens. Training combines self-supervised learning on real video with weak supervision of scene dynamics on synthetic Kubric data. We evaluate SDM on ProbeMotion, a new evaluation suite spanning synthetic and real videos with camera motion, object motion, and combined dynamics. SDM outperforms backbone baselines using global CLS or average-pooled features, and compares favorably to strongly supervised representations such as VGGT on several probes, despite using substantially weaker supervision. These results suggest that pretrained image models can be readily repurposed into structured video-dynamics representations, providing a useful inductive bias for learning and analyzing latent video dynamics.

6 MagicMakeup: A Region-Controllable Diffusion Transformer for High-Fidelity Makeup-Transfer Ziyi Wang, Siming Zheng, Yang Yang, Shusong Xu, Hao Zhang, Bo Li, Changqing Zou, Peng-Tao Jiang

Vision-Language Reasoning Benchmark & Evaluation Generative Modeling

ArXiv

Paper 6 / arXiv:2607.20924 Open arXiv

Relevance 5 Novelty 6

Why selected: Matches criterion 2 moderately: a diffusion transformer for region-controllable makeup transfer, which is a multimodal image-editing model with explicit spatial control.

Makeup-transfer applies the reference makeup to the source face while preserving the source identity. Despite advances in full-face editing by diffusion-based methods, strong regional controllability, makeup fidelity, and identity preservation remain challenging. The reasons are (i) pixel-to-attention misalignment that causes spillover into non-target areas and weakens regional control; (ii) unclear transfer/preservation concept separation under two-image conditioning, leading to coupling between makeup attributes and identity; and (iii) the lack of a high-resolution dataset that is identity-consistent and region-labeled for fine-grained supervision. In this paper, we propose MagicMakeup, a diffusion transformer-based framework for region-controllable and high-fidelity makeup transfer, built on spatial constraints and concept disentanglement. To enable precise region-specific editing while preserving identity, we propose Token-Aligned Region Gating, which aligns pixel masks with attention and applies region-specific logit gating. To clarify the concepts of transfer and preservation, we further introduce Cross-Modal Perception Guidance, which aligns text and image features to enhance cross-modal concept perception. We also design a pipeline for the generation of 1024 x 1024 data pairs through region-specific makeup removal and establish a unified benchmark in synthetic and real settings. Extensive quantitative and qualitative experiments show that MagicMakeup improves regional controllability, makeup fidelity, and identity preservation, with strong robustness across styles, races, and poses.

10 Sidewalk Moments: Are Richer Representations Always More Human-Aligned? Evidence from City-Walk Videos Liu Liu, Freya Huying Tan, F\'abio Duarte

Vision-Language Reasoning Benchmark & Evaluation Time-Series LLM Retrieval & Grounding

ArXiv

Paper 10 / arXiv:2607.20903 Open arXiv

Relevance 3 Novelty 5

Why selected: No close match to criteria 1-4; it studies human-aligned visual representations for city-walk engagement, which is interesting but not a direct match to the requested vision-language or embodied-AI criteria.

We examine whether richer visual representations yield more human-aligned measures of urban engagement, using 61 first-person city-walk videos from YouTube segmented into over 50,000 ten-second clips and represented across four modalities: spatiotemporal video features, temporally averaged images (TAIs), audio embeddings, and text-based semantic descriptions. Spearman correlation analysis reveals the expected ordering along the temporal-richness continuum, with video features showing the strongest continuous alignment. However, this ordering breaks down under binary classification of high- versus low-engagement moments (the paradigm most commonly used to train perceptual scoring models), where TAIs consistently match or outperform video across most classifiers and quantile thresholds. An independent two-alternative forced-choice study on Amazon Mechanical Turk confirms that this parity reflects human judgment: participants identified engaging moments with comparable accuracy from TAIs and full video clips, while text performed substantially worse and audio remained near chance. Gap analysis reveals a functional dissociation: video features are advantaged in activity-driven scenes with dynamic content, whereas TAIs better align with human judgments in composition-driven scenes dominated by stable spatial structure. These findings challenge the assumption that richer representations are inherently more human-aligned, and suggest that perceptually grounded temporal compression can be a principled alternative to full video encoding.

Benchmark & Evaluation

3 Future Rendering $\neq$ Future Surface: A Benchmark and Dataset for Dynamic Surface Reconstruction Beyond the Observed Window Yukun Shi, Minglun Gong

Benchmark & Evaluation Embodied Interaction Generative Modeling LLM Interpretability

ArXiv

Paper 3 / arXiv:2607.21471 Open arXiv

Relevance 6 Novelty 7

Why selected: Matches criterion 3 very closely: it introduces a new benchmark and dataset for dynamic surface reconstruction beyond the observed window, a novel embodied/3D geometry evaluation angle.

Dynamic-scene reconstruction is almost always evaluated inside the observed time window, yet deployment settings such as AR overlays, robot interaction, and anticipatory planning need the future surface: the geometry at times beyond those captured. No standard benchmark measures this. We introduce FutureSurf, a controlled diagnostic benchmark and dataset for future-time surface reconstruction that trades scene diversity for exact future ground truth and falsification controls. A method trains on the observed first 75% of a sequence; we score its extracted per-frame surface on the held-out future by Chamfer distance, reporting absolute future CD as the primary score and the future/observed gap as a diagnostic. The dataset contains eight analytically defined controlled motions, including three falsification controls, with exact per-frame ground-truth meshes. We also provide a ground-truth-side recoverability oracle. The release includes split files, scoring code, a benchmark card, and Croissant metadata. On the controlled motions, the DG-Mesh backbone leaves a 2.7-4.1$\times$ gap even for futures predictable in principle (four of five recoverable from observed motion by a fixed rule), while the falsification controls behave as designed (the surface-invariant motion shows no gap). Beyond the contributed dataset, the gap persists across six animated DG-Mesh asset scenes and a second backbone, Deformable-3DGS (2.0-6.6$\times$; both share a deformation-MLP temporal model). The benchmark also shows that future rendering quality and future-surface accuracy are statistically decoupled, so the novel-view-synthesis metrics the field reports do not track future geometry. The future error is structured, concentrating where the surface moves. The dataset, evaluation toolkit, and scoring code are available on Hugging Face and GitHub (https://github.com/Ricky-S/futuresurf).

7 RealVDeblur: One-Step Diffusion for Generalizable Real-World Video Deblurring Renbiao Jin, Mingxin Yang, Yutian Chen, Junhao Zhuang, Xin Cai, Mulin Yu, Linning Xu, Wenxian Yu, Danping Zou, Shi Guo, Tianfan Xue

Benchmark & Evaluation Generative Modeling LLM Interpretability Time-Series LLM

ArXiv

Paper 7 / arXiv:2607.20628 Open arXiv

Relevance 4 Novelty 6

Why selected: Matches criterion 4 moderately: a generative restoration method with a physically grounded data pipeline and strong downstream impact on 3D reconstruction, though it is not a vision foundation model itself.

Real-world video deblurring remains challenging due to diverse motion patterns, complex degradations, and the scarcity of realistic training data, yet robust restoration is critical for downstream pipelines such as mobile imaging and 3D reconstruction. This work presents \textbf{RealVDeblur}, an efficient generative framework designed to improve in-the-wild robustness under diverse real capture conditions. First, a large-scale, physically grounded blur synthesis pipeline is constructed from scene-level 3D Gaussian Splatting (3DGS) assets and high-frame-rate videos, providing realistic training data covering both camera-induced and object-motion blur. Second, a video diffusion prior is leveraged for restoration; to better accommodate frame-dependent blur variations, temporal compression in the VAE is disabled and a frame-wise encoding scheme is adopted. For practical deployment on long videos, multi-step diffusion sampling is distilled into an efficient one-step generator, and a training-free Temporal Window Mask stabilizes inference beyond the training horizon with constant memory usage. Extensive experiments on diverse real-world benchmarks demonstrate strong perceptual quality, semantic fidelity, and temporal consistency on unseen videos, as well as improved robustness in downstream 3D reconstruction under severe motion blur. Project page: https://rbjin.github.io/RealVDeblur

8 The Second LoViF 2026 Challenge on Real-World All-in-One Image Restoration: Methods and Results Xiang Chen, Hao Li, Jiangxin Dong, Jinshan Pan, Xin Li, Hongbo Ding, Junpeng Jiang, Xingyu Qiu, Yilian Zhong, Yuxiang Chen, Shibo Yin, Zixuan Huang, Yushun Fang, Xilei Zhu, Yahui Wang, Chen Lu, Xiaodong Zhou, Qingyue Cao, Changwei Gong, Jingyun Liu, Xingchen Yi, Hansen Shi, Ruiyi Liu, Jirui Xie, Tao Liu, Wenzhuo Ma, Hongzhen Li, Yongyong Chen, Zheng Zhou, Jingyong Su, Jie Liu, Haijin Zeng, Cheng Li, Peishuai Zha, Ziyi Wang, Jian Tang, Yan Chen, Long Bao, Heng Sun, Jiyuan Zhang, Shuai Liu, Wei Ding, Chengjun Guo, Yibin Huang, Xiaotao Wang, Dongqing Zou, Lei Lei, Xiaofeng Wang, Xiao Liu, Yulin Wu, Yuhan Zhao, Shurui Peng, Chao Ren, Yu-Kai Wang, Kosuke Shigematsu, Asuka Shin, Rong-Lin Jian, Cheng-Jun Kang, Jin-Hui Jiang, Jialin Zhou, Kuo Yuan, Songyu Zhang, E B Benson, Ashfaq Hussain, Pruthvikanth AC, Qirui Chen, Jinyuan Chen, Jun Zhang, Xu Zhang, Xuhui Cao, Jiaqi Ma, Laibin Chang, Yuchun Miao, Yichu Xu, Yuanzhi Yao, Shi Chen, Yuning Cui, Huan Zhang, Lefei Zhang, Saeed Ahmad, Ik Hyun Lee, Jun Young Park, Ji Hwan Yoon, Shangquan Sun, Behrooz Nobahar-Moghanlou, Majid Edalatjou, Karim Shahi-Niyar, Ruibo Zhang, Dexiang Hong, Xinyan Liu, Shengeng Tang, Weidong Chen

Benchmark & Evaluation

ArXiv

Paper 8 / arXiv:2607.21118 Open arXiv

Relevance 5 Novelty 3

Why selected: Matches criterion 4 moderately well: a vision foundation-model-adjacent image restoration benchmark/challenge focused on real-world all-in-one restoration.

This paper presents a review of the second LoViF Challenge on Real-World All-in-One Image Restoration. The challenge aims to advance unified image restoration under diverse real-world degradation conditions, including blur, low-light, haze, rain, and snow. It provides a common benchmark for evaluating the restoration accuracy, robustness, and generalization capability of models across multiple degradation categories within a unified framework. The competition attracted 158 registered participants, and 20 teams were included in the final ranking after their submitted results were successfully reproduced and verified. This report provides a comprehensive analysis of the submitted solutions and corresponding results, highlighting recent advances in real-world all-in-one image restoration. The summarized methods and empirical findings reveal effective design strategies and establish an updated benchmark for future research in real-world low-level vision.

9 Distribution-Alignment Bridge for Uncertainty-Aware Text-to-Video Retrieval Kyeongmo Chae, Jihoon Lee, Sangtae Ahn

Benchmark & Evaluation Generative Modeling Retrieval & Grounding

ArXiv

Paper 9 / arXiv:2607.20984 Open arXiv

Relevance 3 Novelty 5

Why selected: No close match to criteria 1-4; it is a text-to-video retrieval method with uncertainty modeling, relevant to multimodal learning but not one of the specified paper types.

This paper proposes the Distribution-Alignment Bridge (DAB), a framework that reconceptualizes text-to-video retrieval as a distribution alignment task rather than traditional deterministic point matching. By modeling both text and video embeddings as Gaussian distributions defined by mean and variance, DAB explicitly accounts for modality-specific uncertainty. We employ a deterministic, diffusion-inspired bridge to iteratively refine text distributions toward their target video distributions through a truncated refinement process. This approach unifies probabilistic embedding and distributional transformation into a cohesive, end-to-end trainable system. To optimize cross-modal similarity, we introduce a distribution-aware contrastive loss based on Kullback-Leibler divergence. Extensive evaluations on MSR-VTT, MSVD, and VATEX benchmarks confirm that DAB significantly outperforms existing probabilistic and diffusion-based baselines, while providing calibrated uncertainty-aware ranking through bridge-induced distributional margins.

Archived papers 10

Top score 17

Average score 11.3