GenAI4ELab/papercli-papers huggingface dataset:
Reconstructing Realistic and Relightable Eyes
Authors: Wesley Khademi; Jogendra Kundu; Yatong An; Alexander Fix; David Colmenares
Venue: WACV 2026 (main)

Abstract: Accurately modeling the eye is a challenging task as it exhibits refraction and reflection at the cornea, complex iris texture, and self-occlusion and shadowing due to eyelids and eyelashes. To address these challenges, we present a system for learning a hybrid relightable eye model which can be relit under near-field point lights. Our hybrid model leverages an eyeball mesh for explicitly representing the cornea surface and the reflections on it while learning the geometry and light transport of the periocular region and eye interior implicitly. To account for refraction, we explicitly handle the refraction of camera rays using Snell's law and predict the refraction of incident light rays using a neural network. Furthermore, we propose an extension of our method which enables us to relight the eye using a fringe projector to simulate structured light. Through experiments, we demonstrate that our method results in higher fidelity rendering under novel viewpoint and lighting conditions, improves learned iris geometry, and more accurately simulates structured light fringe patterns on the eye.
Deep Image Decomposition for Medical Imaging Anonymization and Curation
Authors: Yael Elkin; Gal Ben-Arie; Tammy Riklin-Raviv
Venue: WACV 2026 (main)

Abstract: Medical scans often include patient identifiers and clinical annotations that must be removed prior to data sharing or use in downstream model training. With machine learning now central to clinical imaging analysis, reliable removal of such non-imaging artifacts is essential for preserving patient privacy, reducing bias, and improving data quality. However, this crucial curation step is frequently overlooked or addressed heuristically.We present a deep learning framework that automatically detects and removes overlaid text, markers, and other non-imaging elements from clinical scans while restoring the underlying image content. The model comprises two components: a detection module that localizes non-imaging regions, and a dual-generator architecture for unsupervised image decomposition, where one generator reconstructs the imaging content and the other produces the non-imaging components. Unlike conventional inpainting, our method bypasses explicit segmentation by leveraging explainable AI (XAI) maps from the detection module to guide artifact masking and restoration.We demonstrate robust curation performance on three datasets, one MRI and two ultrasound, for both public and private sources.Results show high visual quality (Turing-test validated) and strong quantitative scores. Importantly, training downstream classification and segmentation models with scans curated by our method substantially improves results compared to models trained on data containing overlaid annotations. In fact, our performance on various metrics (e.g., accuracy, F1 score, IoU, and Dice) is comparable to those obtained with clean, marker-free training data. Code is included with the submission.
An Efficient Multi-Rater Setup Towards Personalized and Diversified Medical Image Segmentation
Authors: Sajed Almorsy; Ayman Khalafallah; Marwan Torki
Venue: WACV 2026 (main)

Abstract: Multi-rater medical image segmentation addresses annotation ambiguities but typically requires costly multiple expert annotations per scan. We propose P-Diverse, a novel two-stage framework that minimizes the annotation needs while achieving state-of-the-art performance. Stage-I trains a modified nnU-Net with expert-specific embeddings throughout the network stages, generating personalized segmentations using as low as one annotation per scan. Stage-II freezes that network to synthesize the missing annotations and trains a diversification model that captures multi-rater variability using the available and synthesized annotations. We evaluated on the public NPC dataset and QUBIQ2021 dataset (where the current SOTA method fails), P-Diverse establishes new SOTA performance using synthetic annotations on the diversification stage, significantly reducing clinical annotation burdens. Code: https://github.com/SajedHassan/P-Diverse.
Clear Sights on Site: A Spatial-Adaptive Channel Network for Deblurring Construction Site Images
Authors: Mahdi Bonyani; Maryam Soleymani; Chao Wang
Venue: WACV 2026 (main)

Abstract: The reliability of computer vision systems in the construction industry is critically undermined by motion blur, a complex and non-uniform degradation that conventional deblurring models with static architectures fail to address effectively. To overcome this challenge, we introduce the Spatial-Adaptive Channel Network (SAC-Net), a dynamic architecture designed specifically for this demanding environment. SAC-Net features three synergistic innovations. First, a Spatial-Adaptive Channel Module (SACM) generates content-aware spatial filters, allowing the network to adaptively focus on the most salient features for restoration. Second, our Hierarchical Feature Transfer with Wavelet (HFTW) method ensures robust propagation of core structural information by refining features in the wavelet domain, effectively suppressing noise. Finally, a Selective Feature Integration (SFI) module intelligently merges multi-scale features, combining semantic context with fine-grained detail. Evaluated on a large-scale, domain-specific construction dataset, SAC-Net significantly outperforms state-of-the-art methods, setting a new benchmark in both quantitative metrics and visual quality.
GRAPE (Gaussian Rendering for Accelerated Pixel Enhancement) Brings Fast and Lightweight Arbitrary Super-Resolution
Authors: Jung In Jang; Kyong Hwan Jin
Venue: WACV 2026 (main)

Abstract: We present GRAPE--Gaussian Rendering for Accelerated Pixel Enhancement, a fast, lightweight method for arbitrary-scale super-resolution (ASSR) based on 2D Gaussian splatting. Lookup-table (LUT) schemes are limited to preset scale factors and struggle with varied textures, while implicit neural representations (INRs) slow down because they require per-coordinate queries; moreover, prior Gaussian-splatting approaches rely on heavy networks or complex processing. GRAPE overcomes these limitations with a compact design in which a single point-wise layer predicts anisotropic Gaussian parameters--RGB value, rotation, scale, and offset--and a differentiable rasterizer then renders the high-resolution image in one pass. The entire model, including both encoder and decoder, contains just 1.56 M parameters and requires only 1.10 GB of GPU memory, yet achieves 69.33 FPS on Urban100 at x4 whose average image size is 985 x 798. This is more than 315x faster than GSASR, a 20.45 M-parameter model that runs at 0.22 FPS. Although GRAPE does not further improve perceptual fidelity over heavier networks, it remains competitively close, providing an attractive quality-efficiency trade-off across Set5, Set14, BSD100, DIV2K and Urban100. Consequently, GRAPE is ideal for resource-limited deployments or interactive applications that require rapid screen updates. The source code will be made publicly available at github.com/mulkkog/GRAPE.
HodgeFormer: Transformers for Learnable Operators on Triangular Meshes through Data-Driven Hodge Matrices
Authors: Akis Nousias; Stavros Nousias
Venue: WACV 2026 (main)

Abstract: Currently, prominent Transformer architectures applied to graphs and meshes for shape analysis tasks employ traditional attention layers that heavily utilize spectral features requiring costly eigenvalue decomposition-based methods. To encode the mesh structure, these methods derive positional embeddings that heavily rely on eigenvalue decomposition-based operations, e.g. on the Laplacian matrix, or on heat-kernel signatures, which are then concatenated to the input features. This paper proposes a novel approach inspired by the explicit construction of the Hodge Laplacian operator in Discrete Exterior Calculus as a product of discrete Hodge operators and exterior derivatives. We adjust the Transformer architecture in a novel deep learning layer that utilizes the multi-head attention mechanism to approximate Hodge matrices and learn families of discrete operators that act on mesh vertices, edges and faces. Our approach results in a computationally-efficient architecture that achieves comparable performance in mesh segmentation and classification tasks, through a direct learning framework, while eliminating the need for costly eigenvalue decomposition operations or complex preprocessing operations.
Revisiting Vision-Language Foundations for No-Reference Image Quality Assessment
Authors: Ankit Yadav; Ta Duc Huy; Lingqiao Liu
Venue: WACV 2026 (main)

Abstract: Large-scale vision-language pre-training has recently shown promise for no-reference image-quality assessment(NR-IQA), yet the relative merits of modern Vision Transformer foundations remain poorly understood. In this work.We present the first systematic evaluation of six prominent pretrained vision-language backbones, CLIP, SigLIP2, DINOv2, DINOv3, Perception, and ResNet--for the task of No-Reference Image Quality Assessment (NR-IQA), each finetuned using an identical lightweight MLP head. Our study uncovers two previously overlooked factors: (1) SigLIP2 consistently achieves strong performance; and (2) the choice of activation function plays a surprisingly crucial role, particularly for enhancing the generalization ability of imagequality assessment models. Notably, we find that simple sigmoid activations outperform commonly used ReLU and GELU on several benchmarks. Motivated by this finding, we introduce a learnable activation selection mechanism that adaptively determines the nonlinearity for each channel, eliminating the need for manual activation design. achieving new state-of-the-art SRCC on CLIVE, KADID, and AGIQA-3K. Extensive ablations confirm the benefits across architectures and regimes, establishing strong, resource-efficient NR-IQA baselines.
PS3: Part Level Instance Segmentation in 3D
Authors: Hong-Xuan Yen; Chiamin Chen; Yanqing Wang; Yu-Lun Liu; Min Sun
Venue: WACV 2026 (main)

Abstract: Open-vocabulary 3D segmentation allows exploration of 3D environments using unrestricted natural language queries. Current approaches to open-vocabulary 3D instance segmentation largely concentrate on recognizing object-level instances but face difficulties when dealing with more fine-grained elements of a scene, such as object parts. Some previous work constructs hierarchical open-vocabulary 3D scene representations by geometric over-segmentation, which can't identify parts with similar geometry. In this work, we introduce PS3, an approach to generate 3D part proposals from multi-view 2D masks. PS3 outperforms baselines that rely on geometric over-segmentation in scene-scale open-vocabulary 3D part segmentation.
UNO: Unifying One-stage Video Scene Graph Generation via Object-Centric Visual Representation Learning
Authors: Huy Le; Nhat Chung; Tung Kieu; Jingkang Yang; Ngan Le
Venue: WACV 2026 (main)

Abstract: Video Scene Graph Generation (VidSGG) aims to represent dynamic visual content by detecting objects and modeling their temporal interactions as structured graphs. Prior studies typically target either coarse-grained box-level or fine-grained panoptic pixel-level VidSGG, often requiring task-specific architectures and multi-stage training pipelines. In this paper, we present UNO (UNified Object-centric VidSGG), a single-stage, unified framework that jointly addresses both tasks within an end-to-end architecture. UNO is designed to minimize task-specific modifications and maximize parameter sharing, enabling generalization across different levels of visual granularity. The core of UNO is an extended slot attention mechanism that decomposes visual features into object and relation slots. To ensure robust temporal modeling, we introduce object temporal consistency learning, which enforces consistent object representations across frames without relying on explicit tracking modules. Additionally, a dynamic triplet prediction module links relation slots to corresponding object pairs, capturing evolving interactions over time. We evaluate UNO on standard box-level and pixel-level VidSGG benchmarks. Results demonstrate that UNO not only achieves competitive performance across both tasks but also offers improved efficiency through a unified, object-centric design.
ITSELF: Attention Guided Fine-Grained Alignment for Vision-Language Retrieval
Authors: Tien-Huy Nguyen; Huu-Loc Tran; Thanh Duc Ngo
Venue: WACV 2026 (main)

Abstract: Vision Language Models (VLMs) have rapidly advanced and show strong promise for text-based person search (TBPS), a task that requires capturing fine-grained relationships between images and text to distinguish individuals. Previous methods address these challenges through local alignment, yet they are often prone to shortcut learning and spurious correlations, yielding misalignment. Moreover, injecting prior knowledge can distort intra-modality structure. Motivated by our finding that encoder attention surfaces spatially precise evidence from the earliest training epochs, and to alleviate these issues, we introduce ITSELF, an attention-guided framework for implicit local alignment. At its core, Guided Representation with Attentive Bank (GRAB) converts the model's own attention into an Attentive Bank of high-saliency tokens and applies local objectives on this bank, learning fine-grained correspondences without extra supervision. To make the selection reliable and non-redundant, we introduce Multi-Layer Attention for Robust Selection (MARS), which aggregates attention across layers and performs diversity-aware top-k selection; and Adaptive Token Scheduler (ATS), which schedules the retention budget from coarse to fine over training, preserving context early while progressively focusing on discriminative details. Extensive experiments on three widely used TBPS benchmarks show state-of-the-art performance and strong cross-dataset generalization, confirming the effectiveness and robustness of our approach without additional prior supervision. Our project is publicly available at https://trhuuloc.github.io/itself
Structure-Aware Feature Rectification with Region Adjacency Graphs for Training-free Open-Vocabulary Semantic Segmentation
Authors: Qiming Huang; Hao Ai; Jianbo Jiao
Venue: WACV 2026 (main)

Abstract: Benefiting from the inductive biases learned from large-scale datasets, open-vocabulary semantic segmentation (OVSS) leverages the power of vision-language models, such as CLIP, to achieve remarkable progress without requiring task-specific training. However, due to CLIP's pretraining nature on image-text pairs, it tends to focus on global semantic alignment, resulting in suboptimal performance when associating fine-grained visual regions with text. This leads to noisy and inconsistent predictions, particularly in local areas. We attribute this to a dispersed bias stemming from its contrastive training paradigm, which is difficult to alleviate using CLIP features alone. To address this, we propose a structure-aware feature rectification approach that incorporates instance-specific priors derived directly from the image. Specifically, we construct a region adjacency graph (RAG) based on low-level features (e.g. colour and texture) to capture local structural relationships and use it to refine CLIP features by enhancing local discrimination. Extensive experiments show that our method effectively suppresses segmentation noise, improves region-level consistency, and achieves strong performance on multiple open-vocabulary segmentation benchmarks.
Uplifting Table Tennis: A Robust, Real-World Application for 3D Trajectory and Spin Estimation
Authors: Daniel Kienzle; Katja Ludwig; Julian Lorenz; Shin'Ichi Satoh; Rainer Lienhart
Venue: WACV 2026 (main)

Abstract: Obtaining the precise 3D motion of a table tennis ball from standard monocular videos is a challenging problem, as existing methods trained on synthetic data struggle to generalize to the noisy, imperfect ball and table detections of the real world. This is primarily due to the inherent lack of 3D ground truth trajectories and spin annotations for real-world video. To overcome this, we propose a novel two-stage pipeline that divides the problem into a front-end perception task and a back-end 2D-to-3D uplifting task. This separation allows us to train the front-end components with abundant 2D supervision from our newly created TTHQ dataset, while the back-end uplifting network is trained exclusively on physically-correct synthetic data. We specifically re-engineer the uplifting model to be robust to common real-world artifacts, such as missing detections and varying frame rates. By integrating a ball detector and a table keypoint detector, our approach transforms a proof-of-concept uplifting method into a practical, robust, and high-performing end-to-end application for 3D table tennis trajectory and spin analysis.
Empowering Source-Free Domain Adaptation via MLLM-Guided Reliability-Based Curriculum Learning
Authors: Dongjie Chen; Kartik Patwari; Zhengfeng Lai; Xiaoguang Zhu; Sen-ching Cheung; Chen-Nee Chuah
Venue: WACV 2026 (main)

Abstract: Existing SFDA methods struggle to fully use pre-trained knowledge and often rely on a single model's predictions or handcrafted prompts, limiting robustness under domain shift. Multimodal Large Language Models (MLLMs) offer a promising alternative: they encode rich visual-semantic knowledge and generalize well without task-specific tuning. However, their use in SFDA is hindered by instruction-following failures, inconsistent outputs, and high inference costs. We propose Reliability-based Curriculum Learning (RCL), a novel framework that distills robust supervision from multiple frozen MLLMs into a compact target model. RCL organizes adaptation as a three-stage curriculum that progressively incorporates pseudo-labels based on inter-model agreement and model confidence, enabling stable and noise-aware training. Our approach achieves state-of-the-art performance on standard SFDA datasets, Office-Home, DomainNet-126, and VisDA-C, outperforming zero-shot MLLMs, their ensembles, all without accessing source data or tuning foundation models. Code is available at https://github.com/Dong-Jie-Chen/RCL.
LighthouseGS: Indoor Structure-aware 3D Gaussian Splatting for Panorama-Style Mobile Captures
Authors: Seungoh Han; Jaehoon Jang; Hyunsu Kim; Jaeheung Surh; Junhyung Kwak; Hyowon Ha; Kyungdon Joo
Venue: WACV 2026 (main)

Abstract: We introduce LighthouseGS, a practical novel view synthesis framework based on 3D Gaussian Splatting that utilizes simple panorama-style captures from a single mobile device. While convenient, this rotation-dominant motion and narrow baseline make accurate camera pose and 3D point estimation challenging, especially in textureless indoor scenes. To address these challenges, LighthouseGS leverages rough geometric priors, such as mobile device camera poses and monocular depth estimation, and utilizes indoor planar structures. Specifically, we propose a new initialization method called plane scaffold assembly to generate consistent 3D points on these structures, followed by a stable pruning strategy to enhance geometry and optimization stability. Additionally, we present geometric and photometric corrections to resolve inconsistencies from motion drift and auto-exposure in mobile devices. Tested on real and synthetic indoor scenes, LighthouseGS delivers photorealistic rendering, outperforming state-of-the-art methods and enabling applications like panoramic view synthesis and object placement. Project page: https://vision3d-lab.github.io/lighthousegs/
Mitigating Object and Action Hallucinations in Multimodal LLMs via Self-Augmented Contrastive Alignment
Authors: Kai-Po Chang; Wei-Yuan Cheng; Chi-Pin Huang; Fu-En Yang; Yu-Chiang Frank Wang
Venue: WACV 2026 (main)

Abstract: Recent advancement in multimodal LLMs (MLLMs) has demonstrated their remarkable capability to generate descriptive captions for input videos. However, these models suffer from factual inaccuracies in the generated descriptions, causing severe hallucination issues. While prior works have explored alleviating hallucinations for static images, jointly mitigating visual object and temporal action hallucinations for dynamic videos remains a challenging and unsolved task. To tackle this challenge, we propose a Self-Augmented Contrastive Alignment (SANTA) framework for enabling object and action faithfulness by exempting the spurious correlations and enforcing the emphasis on visual facts. SANTA employs a hallucinative self-augmentation scheme to identify the potential hallucinations that lie in the MLLM and transform the original captions to the contrasted negatives. Furthermore, we develop a tracklet-phrase contrastive alignment to match the regional objects and relation-guided actions with their corresponding visual and temporal phrases. Extensive experiments demonstrate that SANTA outperforms existing methods in alleviating object and action hallucinations, yielding superior performance on the hallucination examination benchmarks.
Segmentation-Aware Latent Diffusion for Satellite Image Super-Resolution: Enabling Smallholder Farm Boundary Delineation
Authors: Aditi Agarwal; Anjali Jain; Nikita Saxena; Ishan Deshpande; Michal Kazmierski; Abigail Annkah; Nadav Sherman; Karthikeyan Shanmugam; Alok Talekar; Vaibhav Rajan
Venue: WACV 2026 (main)

Abstract: Delineating farm boundaries through segmentation of satellite images is a fundamental step in many agricultural applications. The task is particularly challenging for smallholder farms, where accurate delineation requires the use of high resolution (HR) imagery which are available only at low revisit frequencies (e.g., annually). To support more frequent (sub-) seasonal monitoring, HR images could be combined as references (ref) with low resolution (LR) images - having higher revisit frequency (e.g., weekly) - using reference-based super resolution (Ref-SR) methods. However, current Ref-SR methods optimize perceptual quality and smooth over crucial features needed for downstream tasks, and are unable to meet the large scale-factor requirements for this task. Further, previous two-step approaches of SR followed by segmentation do not effectively utilize diverse satellite sources as inputs. We address these problems through a new approach, SEED-SR, which uses a combination of conditional latent diffusion models and large-scale multi-spectral, multi-source geo-spatial foundation models. Our key innovation is to bypass the explicit SR task in the pixel space and instead perform SR in a segmentation-aware latent space. This unique approach enables us to generate segmentation maps at an unprecedented 20x scale factor, and rigorous experiments on two large, real datasets demonstrate up to 25.5% and 12.9% relative improvement in instance and semantic segmentation metrics respectively over approaches based on state-of-the-art Ref-SR methods
Eff-GRot: Efficient and Generalizable Rotation Estimation with Transformers
Authors: Fanis Mathioulakis; Gorjan Radevski; Tinne Tuytelaars
Venue: WACV 2026 (main)

Abstract: We introduce Eff-GRot, an approach for efficient and generalizable rotation estimation from RGB images. Given a query image and a set of reference images with known orientations, our method directly predicts the object's rotation in a single forward pass, without requiring object- or category-specific training. At the core of our framework is a transformer that performs a comparison in the latent space, jointly processing rotation-aware representations from multiple references alongside a query. This design enables a favorable balance between accuracy and computational efficiency while remaining simple, scalable, and fully end-to-end. Experimental results show that Eff-GRot offers a promising direction toward more efficient rotation estimation, particularly in latency-sensitive applications.