GenAI4ELab/papercli-papers huggingface dataset:
brat: Aligned Multi-View Embeddings for Brain MRI Analysis
Authors: Maxime Kayser; Maksim Gridnev; Wanting Wang; Max Bain; Aneesh Rangnekar; Avijit Chatterjee; Aleksandr Petrov; Harini Veeraraghavan; Nathaniel C. Swinburne
Venue: WACV 2026 (main)

Abstract: We present brat (brain report alignment transformer), a multi-view representation learning framework for brain magnetic resonance imaging (MRI) trained on MRIs paired with clinical reports. Brain MRIs present unique challenges due to the presence of numerous, highly varied, and often subtle abnormalities that are localized to a few slices within a 3D volume. To address these challenges, we introduce a brain MRI dataset 10x larger than existing ones, containing approximately 80,000 3D scans with corresponding radiology reports, and propose a multi-view pre-training approach inspired by advances in document retrieval. We develop an implicit query-feature matching mechanism and adopt concepts from quality-diversity to obtain multi-view embeddings of MRIs that are aligned with the clinical features given by report sentences. We evaluate our approach across multiple vision-language and vision tasks, demonstrating substantial performance improvements. The brat foundation models are publicly released.
TalkingPose: Efficient Face and Gesture Animation with Feedback-guided Diffusion Model
Authors: Alireza Javanmardi; Pragati Jaiswal; Tewodros Amberbir Habtegebrial; Christen Millerdurai; Shaoxiang Wang; Alain Pagani; Didier Stricker
Venue: WACV 2026 (main)

Abstract: Recent advancements in diffusion models have significantly improved the realism and generalizability of character-driven animation, enabling the synthesis of high-quality motion from just a single RGB image and a set of driving poses. Nevertheless, generating temporally coherent long-form content remains challenging. Existing approaches are constrained by computational and memory limitations, as they are typically trained on short video segments, thus performing effectively only over limited frame lengths and hindering their potential for extended coherent generation. To address these constraints, we propose TalkingPose, a novel diffusion-based framework specifically designed for producing long-form, temporally consistent human upper-body animations. TalkingPose leverages driving frames to precisely capture expressive facial and hand movements, transferring these seamlessly to a target actor through a stable diffusion backbone. To ensure continuous motion and enhance temporal coherence, we introduce a feedback-driven mechanism built upon image-based diffusion models. Notably, this mechanism does not incur additional computational costs or require secondary training stages, enabling the generation of animations with unlimited duration. Additionally, we introduce a comprehensive, large-scale dataset to serve as a new benchmark for human upper-body animation. Project page: https://dfki-av.github.io/TalkingPose
Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing
Authors: Saarthak Kapse; Robin Betz; Srinivasan Sivanandan
Venue: WACV 2026 (main)

Abstract: State Space Models (SSMs) with selective scan (Mamba) have been adapted into efficient vision models. Mamba, unlike Vision Transformers, achieves linear complexity for token interactions through a recurrent hidden state process. This sequential processing is enhanced by a parallel scan algorithm, which reduces the computational time of recurrent steps from L sequential steps to log(L) parallel steps with respect to the number of input tokens (L). In this work, we propose Fast Vision Mamba (FastVim), that further reduces the computational time of the SSM block by reducing the number of recurrent steps in Vision Mamba models while still retaining model performance. By alternately pooling tokens along image dimensions across Mamba blocks, we obtain a 2x reduction in the number of parallel steps in SSM block. Our model offers up to 72.5% speedup in inference speed compared to baseline Vision Mamba models on high resolution (2048x2048) images. Our experiments demonstrate state-of-the-art performance with dramatically improved throughput in a range of tasks such as image classification, cell perturbation prediction, segmentation, and object detection. Code is made available at https://github.com/insitro/FastVim
A Dataset and Framework for Learning State-invariant Object Representations
Authors: Rohan Sarkar; Avinash Kak
Venue: WACV 2026 (main)

Abstract: We introduce state invariance - robustness to changes in an object's structural form (as for a folded umbrella or for crumpled clothing) - complementing other common invariances to learn object representations for recognition and retrieval tasks. For this, we present ObjectsWithStateChange (OWSC), a novel dataset that captures variations in object appearance arising from state changes, along with pose, viewpoint, and illumination variations, to advance research in fine-grained 3D object recognition and retrieval. A key challenge is that objects within and across categories may look visually similar under certain state changes, making discrimination difficult. To address this, we propose a curriculum learning based mining strategy that progressively samples harder object pairs based on inter-object distances in the learned embedding space after each epoch, gradually sampling harder-to-distinguish examples of visually similar objects from within and across categories during training. Our ablation shows that this curriculum learning strategy enhances the model's ability to learn discriminative invariant features for fine-grained tasks, improving object recognition accuracy by 7.9% and retrieval mAP by 9.2% over prior methods on our new OWSC dataset and three other multi-view datasets, such as ModelNet40, ObjectPI, FG3D. Our OWSC dataset is available at https://github.com/sarkar-rohan/ObjectsWithStateChange .
SurfDist: Interpretable Three-Dimensional Instance Segmentation Using Curved Surface Patches
Authors: Jackson Borchardt; Saul Kato
Venue: WACV 2026 (main)

Abstract: We present SurfDist, a convolutional neural network architecture for three-dimensional volumetric instance segmentation. SurfDist is a modification of the popular model architecture StarDist-3D which enables learning instance boundaries as closed piecewise compositions of smooth parametric surfaces. This parameterization breaks StarDist-3D's coupling of instance dimension and instance voxel resolution, and it produces predictions which may be upsampled to arbitrarily high resolutions without introduction of voxelization artifacts. For datasets with blob-shaped instances, common in biomedical imaging, SurfDist can achieve higher segmentation accuracy than StarDist-3D with more compact instance parameterizations.
Histogram Assisted Quality Aware Generative Model for Resolution Invariant NIR Image Colorization
Authors: Abhinav Attri; Rajeev Ranjan Dwivedi; Samiran Das; Vinod Kumar Kurmi
Venue: WACV 2026 (main)

Abstract: We present HAQAGen, a unified generative model for resolution-invariant NIR-to-RGB colorization that balances chromatic realism with structural fidelity. The proposed model introduces (i) a combined loss term aligning the global color statistics through differentiable histogram matching, perceptual image quality measure, and feature-based similarity to preserve texture information, (ii) local hue-saturation priors injected via Spatially Adaptive Denormalization (SPADE) to stabilize chromatic reconstruction, and (iii) texture-aware supervision within a Mamba backbone to preserve fine details. We introduce an adaptive-resolution inference engine that further enables high-resolution translation without sacrificing quality. Our proposed NIR-to-RGB translation model simultaneously enforces global color statistics and local chromatic consistency, while scaling to native resolutions without compromising texture fidelity or generalization. Extensive evaluations on FANVID, OMSIV, VCIP2020, and RGB2NIR using different evaluation metrics demonstrate consistent improvements over state-of-the-art baseline methods. HAQAGen produces images with sharper textures and natural colors, attaining significant gains as per perceptual metrics. These results position HAQAGen as a scalable and effective solution for NIR-to-RGB translation across diverse imaging scenarios.
DRWKV: Focusing on Object Edges for Low-Light Image Enhancement
Authors: Xuecheng Bai; Yuxiang Wang; Boyu Hu; Qinyuan Jie; Chuanzhi Xu; Kechen Li; Hongru Xiao; Vera Chung
Venue: WACV 2026 (main)

Abstract: Low-light image enhancement remains a challenging task, particularly in preserving object edge continuity and fine structural details under extreme illumination degradation. In this paper, we propose a novel model, DRWKV (Detailed Receptance Weighted Key Value), which integrates our proposed Global Edge Retinex (GER) theory, enabling effective decoupling of illumination and edge structures for enhanced edge fidelity. Secondly, we introduce Evolving WKV Attention, a spiral-scanning mechanism that captures spatial edge continuity and models irregular structures more effectively. Thirdly, we design the Bilateral Spectrum Aligner (Bi-SAB) and a tailored MS2-Loss to jointly align luminance and chrominance features, improving visual naturalness and mitigating artifacts. Extensive experiments on five LLIE benchmarks demonstrate that DRWKV achieves leading performance in PSNR, SSIM, and NIQE while maintaining low computational complexity. Furthermore, DRWKV enhances downstream performance in low-light multi-object tracking tasks, validating its generalization capabilities.
Fetal and Neonatal Cortical Surface Reconstruction with Anatomical Normal-guidance and Perceptual Enhancements
Authors: Jiyang Lee; Woori Bae; U-Geun Ji; Hanyeol Yang; Jong-Min Lee
Venue: WACV 2026 (main)

Abstract: Accurate reconstruction of cortical surfaces from fetal and neonatal brain MRI plays a fundamental role in neuroscience research and clinical applications. Despite recent advances in deep learning-based approaches, accurate fetal and neonatal surface reconstruction remains challenging due to low tissue contrast, narrow sulci, and complex folding patterns. We present ANPE (Anatomical Normal-guidance with Perceptual Enhancements), a simple yet effective framework that enhances cortical surface reconstruction networks through three key aspects: Our method enforces anatomically plausible deformations by adaptively integrating normal vectors with velocity vectors to simultaneously capture global structure and fine-grained details, significantly improving geometric coherence. Furthermore, we amplify structural transitions and tissue boundaries without requiring explicit segmentation or signed distance functions to eliminate the dependency on additional processing steps, making our method more efficient and widely applicable. Finally, we utilize a context-aware loss function that transcends traditional point-wise losses by integrating a pre-trained feature extractor to capture hierarchical contextual and structural similarities, blending surface-based and image-based features to ensure anatomically meaningful reconstructions. Our simple yet efficient framework is shown to be an efficient, more accurate, and biologically informed approach, presenting a new baseline for fetal and neonatal cortical surface reconstruction.
Intra-Class Probabilistic Embeddings for Uncertainty Estimation in Vision-Language Models
Authors: Zhenxiang Lin; Maryam Haghighat; Will Browne; Dimity Miller
Venue: WACV 2026 (main)

Abstract: Vision-language models (VLMs), such as CLIP, have gained popularity for their strong open vocabulary classification performance, but they are prone to assigning high confidence scores to misclassifications, limiting their reliability in safety-critical applications. We introduce a training-free, post-hoc uncertainty estimation method for contrastive VLMs that can be used to detect erroneous predictions. Our approach assesses visual feature consistency within a class, using feature projection combined with multivariate Gaussians to create class-specific probabilistic embeddings. At inference, test embeddings are scored using the log-probability under each class distribution, with a softmax-normalized density used as the new confidence score. Our method is VLM-agnostic, requires no fine-tuning, robust to label shift, and works effectively with as few as 300 training images per class. Extensive experiments on ImageNet, Flowers102, and Food101 show state-of-the-art error detection performance, significantly outperforming both deterministic and probabilistic VLM baselines. Code is available at https://github.com/zhenxianglin/ICPE.
ExDDV: A New Dataset for Explainable Deepfake Detection in Video
Authors: Vlad Hondru; Eduard Hogea; Darian Onchis; Radu Tudor Ionescu
Venue: WACV 2026 (main)

Abstract: The ever growing realism and quality of generated videos makes it increasingly harder for humans to spot deepfake content, who need to rely more and more on automatic deepfake detectors. However, deepfake detectors are also prone to errors, and their decisions are not explainable, leaving humans vulnerable to deepfake-based fraud and misinformation. To this end, we introduce ExDDV, the first dataset and benchmark for Explainable Deepfake Detection in Video. ExDDV comprises around 5.4K real and deepfake videos that are manually annotated with text descriptions (to explain the artifacts) and clicks (to point out the artifacts). We evaluate a number of vision-language models on ExDDV, performing experiments with various fine-tuning and in-context learning strategies. Our results show that text and click supervision are both required to develop robust explainable models for deepfake videos, which are able to localize and describe the observed artifacts. Our novel dataset and code to reproduce the results are available at https://github.com/vladhondru25/ExDDV.
TriaGS: Differentiable Triangulation-Guided Geometric Consistency for 3D Gaussian Splatting
Authors: Quan Tran; Tuan Dang
Venue: WACV 2026 (main)

Abstract: 3D Gaussian Splatting is crucial for real-time novel view synthesis due to its efficiency and ability to render photorealistic images. However, building a 3D Gaussian is guided solely by photometric loss, which can result in inconsistencies in reconstruction. This under-constrained process often results in "floater" artifacts and an unstructured geometry, preventing the extraction of high-fidelity surfaces. To address this issue, our paper introduces a novel method that improves reconstruction by enforcing global geometry consistency through constrained multi-view triangulation. Our approach aims to achieve a consensus on 3D representation in the physical world by utilizing various estimated views. We optimize this process by evaluating a 3D point against a robust consensus point, which is re-triangulated from a bundle of neighboring views in a self-supervised fashion. We demonstrate the effectiveness of our method across multiple datasets, achieving state-of-the-art results. On the DTU dataset, our method attains a mean Chamfer Distance of 0.50 mm, outperforming comparable explicit methods. We will make our code open-source to facilitate community validation and ensure reproducibility.
ART: Actor-Related Tubelet for Detecting Complex-shaped Action Tubes
Authors: Jiaojiao Zhao
Venue: WACV 2026 (main)

Abstract: This paper addresses the challenge of detecting complex-shaped action tubes in videos. Existing methods assume that actor's position changes slightly in short video clips. Therefore, they either oversimplify the shape of action tubes by representing them as cuboids or learnable positional patterns. However, these solutions may produce an action tube losing the corresponding actor when the actor trajectory becomes complex. This is because they rely solely on position information to determine action tubes, lacking the ability to trace the same actor when their movement patterns are intricate. To address this issue, we propose Actor-related Tubelet (ART), which incorporates actor-specific information when generating action tubes. Regardless of the complexity of an actor's trajectory, ART ensures that an action tube consistently tracks the same actor, relying on actor-specific cues rather than solely on positional information. To assess ART's effectiveness, we introduce a metric for quantifying tube shape complexity and evaluate ART on three mainstream datasets, MultiSports, UCF101-24 and JHMDB51-21, achieving substantial improvements.
Line Art Colorization with Offset Prior-based Diffusion Model
Authors: Xuan Zhu; Miao Cao; Fang-Lue Zhang; Yu-Kun Lai; Paul L Rosin
Venue: WACV 2026 (main)

Abstract: Reference-based line art video colorization colorizes the target line art according to reference images, which is an essential stage for the cartoon production workflow. However, the manual colorization process is time-consuming and repetitive, making automatic video colorization highly desirable. Existing cartoon colorization methods struggle with domain misalignment between the reference and line art images and the loss of details caused by compression into a low-dimensional space in the existing video diffusion models, reducing colorization quality. In this paper, we propose an Offset Prior-based Diffusion Model (OPDM) for cartoon video colorization, which utilizes the powerful generation capability of the diffusion model and cross-domain matching priors to generate high-quality colorization results. Specifically, we design a simple and effective Offset-Adapter that leverages the idea of sampling offsets in deformable convolution to estimate the cross-domain spatial offset features between the target line arts and reference images. We further introduce a new training strategy that combines forward diffusion and reverse denoising in the training stage to ensure content consistency. Experiments on a public cartoon dataset and our newly constructed long cartoon video dataset demonstrate that our proposed method outperforms the existing state-of-the-art line art coloring methods. Code is available at https://github.com/xzh976/OPDM.
F-ViTA: Foundation Model Guided Visible to Infrared Translation
Authors: Jay Nitin Paranjape; Celso M De Melo; Vishal M. Patel
Venue: WACV 2026 (main)

Abstract: Thermal imaging is crucial for scene understanding, particularly in low-light and nighttime conditions. However, collecting large thermal datasets is costly and labor-intensive due to the specialized equipment required for infrared image capture. To address this challenge, researchers have explored visible-to-thermal image translation. Most existing methods rely on Generative Adversarial Networks (GANs) or Diffusion Models (DMs), treating the task as a style transfer problem. As a result, these approaches attempt to learn both the modality distribution shift and underlying physical principles from limited training data. In this paper, we propose F-ViTA, a novel approach that leverages the general world knowledge embedded in foundation models to guide the diffusion process for improved translation. Specifically, we condition an InstructPix2Pix Diffusion Model with zero-shot masks and labels from foundation models such as SAM and Grounded DINO. This allows the model to learn meaningful correlations between scene objects and their thermal signatures in infrared imagery. Extensive experiments on five public datasets demonstrate that F-ViTA outperforms state-of-the-art (SOTA) methods. Furthermore, our model generalizes well to out-of-distribution (OOD) scenarios and can generate Long-Wave Infrared (LWIR), Mid-Wave Infrared (MWIR), and Near-Infrared (NIR) translations from the same visible image. Code: Released post-review.
DATTA: Domain-Adversarial Test-Time Adaptation for Cross-Domain WiFi-Based Human Activity Recognition
Authors: Julian Strohmayer; Rafael Sterzinger; Matthias Wödlinger; Martin Kampel
Venue: WACV 2026 (main)

Abstract: WiFi-based human activity recognition (HAR) faces significant challenges in cross-domain generalization due to dynamic environmental variations, device heterogeneity, and subtle changes in human behavior. In this paper, we introduce DATTA - Domain-Adversarial Test-Time Adaptation - a novel framework that combines domain-adversarial training (DAT) with test-time adaptation (TTA) and a random weight-resetting mechanism. Unlike previous approaches that apply these techniques in isolation, DATTA is specifically tailored for WiFi-based HAR: it leverages DAT to learn robust, domain-invariant features while TTA continuously refines the model on streaming data. To mitigate catastrophic forgetting during adaptation, we incorporate a weight-resetting mechanism, ensuring sustained performance over prolonged domain shifts. Our extensive experiments on the Widar3.0-G6D dataset demonstrate that DATTA not only outperforms state-of-the-art methods by up to 8.1% in F1-Score but also achieves real-time inference with a lightweight architecture, making it a compelling solution for practical WiFi sensing applications. The PyTorch implementation of DATTA is publicly available at: https://github.com/StrohmayerJ/DATTA.
PALMS+: Modular Image-Based Floor Plan Localization Leveraging Depth Foundation Model
Authors: Yunqian Cheng; Benjamin Princen; Roberto Manduchi
Venue: WACV 2026 (main)

Abstract: Indoor localization in GPS-denied environments is crucial for applications like emergency response and assistive navigation. Vision-based methods such as PALMS enable infrastructure-free localization using only a floor plan and a stationary scan, but are limited by the short range of smartphone LiDAR and ambiguity in indoor layouts. We propose PALMS+, a modular, image-based system that addresses these challenges by reconstructing scale-aligned 3D point clouds from posed RGB images using a foundation monocular depth estimation model (Depth Pro), followed by geometric layout matching via convolution with the floor plan. PALMS+ outputs a posterior over the location and orientation, usable for direct or sequential localization. Evaluated on the Structured3D and a custom campus dataset consisting of 80 observations across four large campus buildings, PALMS+ outperforms PALMS and F3Loc in stationary localization accuracy--without requiring any training. Furthermore, when integrated with a particle filter for sequential localization on 33 real-world trajectories, PALMS+ achieved lower localization errors compared to other methods, demonstrating robustness for camera-free tracking and its potential for infrastructure-free applications. Code and data are available at: https://github.com/Head-inthe-Cloud/PALMS-Plane-based-Accessible-Indoor-Localization-Using-Mobile-Smartphones
Ego-EXTRA: video-language Egocentric Dataset for EXpert-TRAinee assistance
Authors: Francesco Ragusa; Michele Mazzamuto; Rosario Forte; Irene D'Ambra; James Fort; Jakob Engel; Antonino Furnari; Giovanni Maria Farinella
Venue: WACV 2026 (main)

Abstract: We present Ego-EXTRA, a video-language Egocentric Dataset for EXpert-TRAinee assistance. Ego-EXTRA features 50 hours of unscripted egocentric videos of subjects performing procedural activities (the trainees) while guided by real-world experts who provide guidance and answer specific questions using natural language. Following a "Wizard of OZ" data collection paradigm, the expert enacts a wearable intelligent assistant, looking at the activities performed by the trainee exclusively from their egocentric point of view, answering questions when asked by the trainee, or proactively interacting with suggestions during the procedures. This unique data collection protocol enables Ego-EXTRA to capture a high-quality dialogue in which expert-level feedback is provided to the trainee. Two-way dialogues between experts and trainees are recorded, transcribed, and used to create a novel benchmark comprising more than 45k high-quality Visual Question Answer sets, which we use to evaluate Multimodal Large Language Models. The results show that Ego-EXTRA is challenging and highlight the limitations of current models when used to provide expert-level assistance to the user. The Ego-EXTRA dataset is publicly available to support the benchmark of egocentric video-language assistants: https://fpv-iplab.github.io/Ego-EXTRA/.