GenAI4ELab/papercli-papers huggingface dataset:
AutoSew: A Geometric Approach to Stitching Prediction with Graph Neural Networks
Authors: Pablo Ríos-Navarro; Elena Garces; Jorge Lopez-Moreno
Venue: WACV 2026 (main)

Abstract: Automating garment assembly from sewing patterns remains a significant challenge due to the lack of standardized annotation protocols and the frequent absence of semantic cues. Existing methods often rely on panel labels or handcrafted heuristics, which limit their applicability to real-world, non-conforming patterns. We present AutoSew, a fully automatic, geometry-based approach for predicting stitch correspondences directly from 2D pattern contours. AutoSew formulates the problem as a graph matching task, leveraging a Graph Neural Network to capture local and global geometric context, and employing a differentiable optimal transport solver to infer stitching relationships--including multi-edge connections. To support this task, we update the GarmentCodeData dataset modifying over 18k patterns with realistic multi-edge annotations, reflecting industrial assembly scenarios. AutoSew achieves 96% F1-score and successfully assembles 73.3% of test garments without error, outperforming existing methods while relying solely on geometric input. Our results demonstrate that geometry alone can robustly guide stitching prediction, enabling scalable garment assembly without manual input. Our dataset and code are available online.
Color Preserving CMOS-SPAD Fusion for Multi-Frame HDR
Authors: Aleksi Suonsivu; Lauri Salmela; Lassi Helin; Leevi Uosukainen; Giacomo Boracchi
Venue: WACV 2026 (main)

Abstract: High dynamic range (HDR) imaging aims to simultaneously capture a large range of illuminance levels, often observed in real world scenes. An HDR image is conventionally captured on a CMOS sensor by combining multiple frames with different exposure values. Ultimately, the dynamic range is limited by the read noise of the CMOS at low-light and by the full-well capacity at high-light. Single-photon avalanche diode (SPAD) imagers display high HDR capabilities due to sensitivity from individual photons up to high photon fluxes. In this work, we demonstrate that fusing multi-frame raw-CMOS RGB and monochrome SPAD images can enhance the dynamic range and color accuracy in real-world settings. To fully leverage the SPAD data, we propose a Linearisation-Upsample processing block that linearises the intrinsically nonlinear response of SPAD-QIS and accounts for the low spatial resolution of current commercial SPAD technology. We demonstrate a clear advantage by including a SPAD image into a multiframe HDR pipeline with comprehensive evaluation on HDR datasets and real-world data. We also address the lack of public SPAD datasets by providing two raw CMOS-SPAD datasets for multi-frame HDR. Both datasets are available for download here: https://github.com/suonsivu/raw-cmos-spad-hdr
CLIP-UP: CLIP-Based Unanswerable Problem Detection for Visual Question Answering
Authors: Ben Vardi; Oron Nir; Ariel Shamir
Venue: WACV 2026 (main)

Abstract: Vision-Language Models (VLMs) demonstrate remarkable capabilities in visual understanding and reasoning, such as in Visual Question Answering (VQA), where the model is asked a question related to a visual input. Still, these models can make distinctly unnatural errors, for example, providing (wrong) answers to unanswerable VQA questions, such as questions asking about objects that do not appear in the image. To address this issue, we propose CLIP-UP: CLIP-based Unanswerable Problem detection, a novel lightweight method for equipping VLMs with the ability to withhold answers to unanswerable questions. CLIP-UP leverages CLIP-based similarity measures to extract question-image alignment information to detect unanswerability, requiring efficient training of only a few additional layers, while keeping the original VLMs' weights unchanged. Tested across several models, CLIP-UP achieves significant improvements on benchmarks assessing unanswerability in both multiple-choice and open-ended VQA, surpassing other methods, while preserving original performance on other tasks.
BoxSplitGen: A Generative Model for 3D Part Bounding Boxes in Varying Granularity
Authors: Juil Koo; Wei-Tung Lin; Chanho Park; Chanhyeok Park; Minhyuk Sung
Venue: WACV 2026 (main)

Abstract: Human creativity follows a perceptual process, moving from abstract ideas to finer details during creation. While 3D generative models have advanced dramatically, models specifically designed to assist human imagination in 3D creation--particularly for detailing abstractions from coarse to fine--have not been explored. We propose a framework that enables intuitive and interactive 3D shape generation by iteratively splitting bounding boxes to refine the set of bounding boxes. The main technical components of our framework are two generative models: the box-splitting generative model and the box-to-shape generative model. The first model, named \Ours  , generates a collection of 3D part bounding boxes with varying granularity by iteratively splitting coarse bounding boxes. It utilizes part bounding boxes created through agglomerative merging and learns the reverse of the merging process--the splitting sequences. The model consists of two main components: the first learns the categorical distribution of the box to be split, and the second learns the distribution of the two new boxes, given the set of boxes and the indication of which box to split. The second model, the box-to-shape generative model, is trained by leveraging the 3D shape priors learned by an existing 3D diffusion model while adapting the model to incorporate bounding box conditioning. In our experiments, we demonstrate that the box-splitting generative model outperforms token prediction models and the inpainting approach with an unconditional diffusion model. Also, we show that our box-to-shape model, based on a state-of-the-art 3D diffusion model, provides superior results compared to a previous model.
Beyond Paired Data: Self-Supervised UAV Geo-Localization from Reference Imagery Alone
Authors: Tristan Amadei; Enric Meinhardt-Llopis; Benedicte Bascle; Corentin Abgrall; Gabriele Facciolo
Venue: WACV 2026 (main)

Abstract: Image-based localization in GNSS-denied environments is critical for UAV autonomy. State-of-the-art methods typically achieve this by matching onboard aerial images to a database of geo-referenced satellite images. However, these methods are fundamentally data-hungry, requiring large-scale, paired satellite and UAV imagery for training, which is often expensive and impractical. To address this, we propose a novel training paradigm that eliminates the need for any UAV data during training by learning to localize from satellite-view reference images alone. This is enabled by a data augmentation strategy that simulates the challenging visual domain shift from satellite to real-world UAV imagery. We introduce CAEVL, an efficient model designed to leverage this paradigm. To validate our approach, we release ViLD, a new, challenging dataset of real-world UAV images.
Improving Animal Pose Estimation through Species Similarity Measures and Rigorous Label Definition
Authors: Medhashree Parhy; Shaan Chanchani; Claire Kim; Josh Mansky; Zian Pan; Parth Thakre; Haoyu Chen; Amy R. Reibman
Venue: WACV 2026 (main)

Abstract: Effective image-based analysis of animals, their phenotypes, and their behavior requires accurate localization of key bodyparts. Keypoint detection algorithms can be either generalized, for a large set of animal species, or specialized and targeted to a specific small set of species. In this paper, we explore specialized models, and address two critical aspects of the associated data-label quality: selection of training data and definition of keypoints. Using antelope species as an example, we introduce a variety of species-similarity measures that we apply for selecting relevant training samples, and we demonstrate that training with the automatically selected species leads to improved pose estimation performance while reducing the required number of images. Then, we demonstrate that labeling keypoints with more precise locations leads to improved localization performance that would be valuable for downstream tasks.
Training-Free Few-Shot Segmentation via Vision-Language Guided Prompting
Authors: Euihyun Yoon; Taejin Park; Jaekoo Lee
Venue: WACV 2026 (main)

Abstract: Object segmentation relies heavily on costly pixel-level annotations and struggles to generalize to unseen domains. The recent introduction of the Segment Anything Model (SAM), a foundation model for segmentation, offers a prompt-driven, zero-shot capability that has been applied in various domains (e.g., autonomous driving, satellite imagery, medical imaging) and extended to Few-Shot Segmentation (FSS) tasks. However, existing SAM-based FSS methods typically generate prompts by using a vision encoder to measure support-query image similarity, which often biases towards the support images and fails when there are significant support-query context shifts. To address this limitation, we propose a training-free FSS approach that combines visual and textual cues to generate effective prompts for the target class. By leveraging both vision and language information, our approach bridges the support-query gap and guides SAM to segment novel objects more reliably. Without any additional training, our method outperforms previous state-of-the-art FSS methods on established benchmarks (COCO\text - 20^i, Pascal\text - 5^i), demonstrating its effectiveness and robust generalization. Our code is publicly available on GitHub.
SynchroRaMa : Lip-Synchronized and Emotion-Aware Talking Face Generation via Multi-Modal Emotion Embedding
Authors: Phyo Thet Yee; Dimitrios Kollias; Sudeepta Mishra; Abhinav Dhall
Venue: WACV 2026 (main)

Abstract: Audio-driven talking face generation has received growing interest, particularly for applications requiring expressive and natural human-avatar interaction. However, most existing emotion-aware methods rely on a single modality (either audio or image) for emotion embedding, limiting their ability to capture nuanced affective cues. Additionally, most methods condition on a single reference image, restricting the model's ability to represent dynamic changes in actions or attributes across time. To address these issues, we introduce SynchroRaMa, a novel framework that integrates a multi-modal emotion embedding by combining emotional signals from text (via sentiment analysis) and audio (via speech-based emotion recognition and audio-derived valence-arousal features), enabling the generation of talking face videos with richer and more authentic emotional expressiveness and fidelity. To ensure natural head motion and accurate lip synchronization, SynchroRaMa includes an audio-to-motion (A2M) module that generates motion frames aligned with the input audio. Finally, SynchroRaMa incorporates scene descriptions generated by Large Language Model (LLM) as additional textual input, enabling it to capture dynamic actions and high-level semantic attributes. Conditioning the model on both visual and textual cues enhances temporal consistency and visual realism. Quantitative and qualitative experiments on benchmark datasets demonstrate that SynchroRaMa outperforms the state-of-the-art, achieving improvements in image quality, expression preservation, and motion realism. A user study further confirms that SynchroRaMa achieves higher subjective ratings than competing methods in overall naturalness, motion diversity, and video smoothness.
HDR Reconstruction Boosting with Training-Free and Exposure-Consistent Diffusion
Authors: Yo-Tin Lin; Su-Kai Chen; Hou-Ning Hu; Yen-Yu Lin; Yu-Lun Liu
Venue: WACV 2026 (main)

Abstract: Single LDR to HDR reconstruction remains challenging for over-exposed regions where traditional methods often fail due to complete information loss. We present a training-free approach that enhances existing indirect HDR reconstruction methods through diffusion-based inpainting. Our method combines text-guided diffusion models with SDEdit refinement to generate plausible content in over-exposed areas while maintaining consistency across multi-exposure LDR images. Unlike previous approaches requiring extensive training, our method seamlessly integrates with existing indirect HDR reconstruction techniques through an iterative compensation mechanism that ensures luminance coherence across multiple exposures. We demonstrate significant improvements in both perceptual quality and quantitative metrics on standard HDR datasets and in-the-wild captures. Results show that our method effectively recovers natural details in challenging scenarios while preserving the advantages of existing HDR reconstruction pipelines.
Exploring Automated Recognition of Instructional Activity and Discourse from Multimodal Classroom Data
Authors: Ivo Bueno; Ruikun Hou; Babette Bühler; Tim Fütterer; James Drimalla; Jonathan K. Foster; Peter Youngs; Peter Gerjets; Ulrich Trautwein; Enkelejda Kasneci
Venue: WACV 2026 (main)

Abstract: Observation of classroom interactions can provide concrete feedback to teachers, but current methods rely on manual annotation, which is resource-intensive and hard to scale. This work explores AI-driven analysis of classroom recordings, focusing on multimodal instructional activity and discourse recognition as a foundation for actionable feedback. Using a densely annotated dataset of 164 hours of video and 68 lesson transcripts, we design parallel, modality-specific pipelines. For video, we evaluate zero-shot multimodal LLMs, fine-tuned vision-language models, and self-supervised video transformers on 24 activity labels. For transcripts, we fine-tune a transformer-based classifier with contextualized inputs and compare it against prompting-based LLMs on 19 discourse labels. To handle class imbalance and multi-label complexity, we apply per-label thresholding, context windows, and imbalance-aware loss functions. The results show that fine-tuned models consistently outperform prompting-based approaches, achieving macro-F1 scores of 0.577 for video and 0.460 for transcripts. These results demonstrate the feasibility of automated classroom analysis and establish a foundation for scalable teacher feedback systems.
GeoHSAF: Geometric Hippocampus Shape Analysis Framework for Longitudinal Alzheimer's Disease Classification
Authors: Mubarak Olaoluwa; Heni Loukil; Arafet Sbei; Hassen Drira
Venue: WACV 2026 (main)

Abstract: Alzheimer's disease (AD) is the most common form of dementia and a progressive, irreversible brain disorder that affects millions worldwide. The majority of existing research on AD classification relies on cross-sectional brain magnetic resonance imaging studies, which consider information from a single time point and fail to account for the progressive nature of AD. Longitudinal analysis, however, is crucial for capturing AD evolution and enabling more accurate diagnosis. To address this gap, we propose GeoHSAF, a novel hippocampus-based geometric learning framework for longitudinal AD classification. To overcome the challenge of missing or inconsistent hippocampal shapes across subjects and time points, our framework includes an interpolation module that predicts intermediate shapes, ensuring temporal continuity. We evaluate the effectiveness of GeoHSAF on three public longitudinal AD datasets: ADNI, OASIS, and AIBL, and benchmark its performance against existing approaches. GeoHSAF achieves new state-of-the-art results on binary classification tasks (AD vs. NC), while also demonstrating strong performance on more challenging triple-class classification tasks (AD vs. NC vs. MCI). Our work is fully reproducible, and all code is available at: https://github.com/ayodejimb/GeoHSAF
Power of Boundary and Reflection: Semantic Transparent Object Segmentation using Pyramid Vision Transformer with Transparent Cues
Authors: Tuan-Anh Vu; Hai Nguyen-Truong; Ziqiang Zheng; Binh-Son Hua; Qing Guo; Ivor W. Tsang; Sai-Kit Yeung
Venue: WACV 2026 (main)

Abstract: Glass is a prevalent material among solid objects in everyday life, yet segmentation methods struggle to distinguish it from opaque materials due to its transparency and reflection. While it is known that human perception relies on boundary and reflective-object features to distinguish glass objects, the existing literature has not yet sufficiently captured both properties when handling transparent objects. Hence, we propose incorporating both of these powerful visual cues via the Boundary Feature Enhancement and Reflection Feature Enhancement modules in a mutually beneficial way. Our proposed framework, TransCues, is a pyramidal transformer encoder-decoder architecture to segment transparent objects. We empirically show that these two modules can be used together effectively, improving overall performance across various benchmark datasets, including glass object semantic segmentation, mirror object semantic segmentation, and generic segmentation datasets. Our method outperforms the state-of-the-art by a large margin, achieving +4.2% mIoU on Trans10K-v2, +5.6% mIoU on MSD, +10.1% mIoU on RGBD-Mirror, +13.1% mIoU on TROSD, and +8.3% mIoU on Stanford2D3D, showing the effectiveness of our method against glass objects.
SCAdapter: Content-Style Disentanglement for Diffusion Style Transfer
Authors: Luan Thanh Trinh
Venue: WACV 2026 (main)

Abstract: Diffusion models have emerged as the leading approach for style transfer, yet they struggle with photo-realistic transfers, often producing painting-like results or missing detailed stylistic elements. Current methods inadequately address unwanted influence from original content styles and style reference content features. We introduce SCAdapter, a novel technique leveraging CLIP image space to effectively separate and integrate content and style features. Our key innovation systematically extracts pure content from content images and style elements from style references, ensuring authentic transfers. This approach is enhanced through three components: Controllable Style Adaptive Instance Normalization (CSAdaIN) for precise multi-style blending, KVS Injection for targeted style integration, and a style transfer consistency objective maintaining process coherence. Comprehensive experiments demonstrate SCAdapter significantly outperforms state-of-the-art methods in both conventional and diffusion-based baselines. By eliminating DDIM inversion and inference-stage optimization, our method achieves at least 2x faster inference than other diffusion-based approaches, making it both more effective and efficient for practical applications.
Equivariant Sampling for Improving Diffusion Model-based Image Restoration
Authors: Chenxu Wu; Qingpeng Kong; Peiang Zhao; Wendi Yang; Wenxin Ma; Fenghe Tang; Zihang Jiang; S.Kevin Zhou
Venue: WACV 2026 (main)

Abstract: Recent advances in generative models, especially diffusion models, have significantly improved image restoration (IR) performance. However, existing problem-agnostic diffusion model-based image restoration (DMIR) methods face challenges in fully leveraging diffusion priors, resulting in suboptimal performance. In this paper, we address the limitations of current problem-agnostic DMIR methods by analyzing their sampling process and providing effective solutions. We introduce EquS, a DMIR method that imposes equivariant information through dual sampling trajectories. To further boost EquS, we propose the Timestep-Aware Schedule (TAS) and introduce EquS^+. TAS prioritizes deterministic steps to enhance certainty and sampling efficiency. Extensive experiments on benchmarks demonstrate that our method is compatible with previous problem-agnostic DMIR methods and significantly boosts their performance without increasing computational costs. Our code is available in https://github.com/FouierL/EquS.
Generalization of Real World Video Deblurring By Image-to-Image Translation
Authors: Kassymzhomart Aitbek; Seungjoon Yang
Venue: WACV 2026 (main)

Abstract: We address the challenge of generalizing video deblurring models to real-world scenarios, where traditional methods often fail due to a significant domain gap between synthetic and real blur. This work extends the image-to-image translation framework to the more complex domain of video deblurring, introducing a training procedure that effectively bridges this gap. Our method integrates a robust video deblurring backbone with realistic motion priors captured from gimbal-mounted cameras, enabling the model to generalize well across both synthetic and real-world datasets--without requiring paired real-world training data or dataset-specific tuning. Extensive experiments demonstrate that our approach outperforms existing state-of-the-art methods on multiple real-world benchmarks, including datasets never seen during training. Importantly, the generalization capability stems not from the specific architecture, but from the modular training procedure itself, which can be readily applied to other deblurring backbones. This positions our method as a scalable and transferable framework for real-world video deblurring.
CRISP: Cylindrical Rendering for In-Stream Point Clouds
Authors: Hyungwoo Kang; Seonyoung Jang; Yeojun Yoon; Byung Tae Oh
Venue: WACV 2026 (main)

Abstract: Conventional point cloud rendering methods are based on the assumption that the complete point cloud is used as input data. However, these methods exhibit limitations in real-world applications, particularly in-stream environments. In real-world scenarios, point cloud acquisition and transmission typically require compression, resulting in degraded point clouds that should be rendered effectively. To address these challenges, we proposed a novel point cloud rendering method through cylindrical projection, optimized for in-stream environments. This approach minimizes distortion by projecting the entire point cloud radially onto a 2D cylindrical plane based on its central axis, enabling grid-based 2D processing. This method provides stable and consistent performance even with sparse or incomplete point clouds. Extensive benchmark experiments have revealed that the proposed framework effectively renders various scenes and objects without requiring additional fine-tuning and demonstrated superior performance and generalization ability in in-stream environments compared with conventional methods.
ProSkill: Segment-Level Skill Assessment in Procedural Videos
Authors: Michele Mazzamuto; Daniele Di Mauro; Gianpiero Francesca; Giovanni Maria Farinella; Antonino Furnari
Venue: WACV 2026 (main)

Abstract: Skill assessment in procedural videos is crucial for the objective evaluation of human performance in settings such as manufacturing and procedural daily tasks. Current research on skill assessment has predominantly focused on sports and lacks large-scale datasets for complex procedural activities. Existing studies typically involve only a limited number of actions, focus on either pairwise assessments (e.g., A is better than B) or on binary labels (e.g., good execution vs needs improvement). In response to these shortcomings, we introduce ProSKILL, the first benchmark dataset for action-level skill assessment in procedural tasks. ProSKILL provides absolute skill assessment annotations, along with pairwise ones. This is enabled by a novel and scalable annotation protocol that allows for the creation of an absolute skill assessment ranking starting from pairwise assessments. This protocol leverages a Swiss Tournament scheme for efficient pairwise comparisons, which are then aggregated into consistent, continuous global scores using an ELO-based rating system. We use our dataset to benchmark the main state-of-the-art skill assessment algorithms, including both ranking-based and pairwise paradigms. The suboptimal results achieved by the current state-of-the-art highlight the challenges and thus the value of ProSKILL in the context of skill assessment for procedural videos.