GenAI4ELab/papercli-papers huggingface dataset:
Grounding Degradations in Natural Language for All-In-One Video Restoration
Authors: Muhammad Kamran Janjua; Amirhosein Ghasemabadi; Kunlin Zhang; Mohammad Salameh; Chao Gao; Di Niu
Venue: WACV 2026 (main)

Abstract: In this work, we propose an all-in-one video restoration framework that grounds degradation-aware semantic context of video frames in natural language via foundation models, offering interpretable and flexible guidance. Unlike prior art, our method assumes no degradation knowledge in train or test time and learns an approximation to the grounded knowledge such that the foundation model can be safely disentangled during inference adding no extra cost. Further, we call for standardization of benchmarks in all-in-one video restoration, and propose two benchmarks in multi-degradation setting, three-task (3D) and four-task (4D), and two time-varying composite degradation benchmarks; one of the latter being our proposed dataset with varying snow intensity, simulating how weather degradations affect videos naturally. We compare our method with prior works and report state-of-the-art performance on all benchmarks.
Stabilizing Direct Training of Spiking Neural Networks: Membrane Potential Initialization and Threshold-robust Surrogate Gradient
Authors: Hyunho Kook; Byeongho Yu; Jeong Min Oh; Eunhyeok Park
Venue: WACV 2026 (main)

Abstract: Recent advancements in the direct training of Spiking Neural Networks (SNNs) have demonstrated high-quality outputs even at early timesteps, paving the way for novel energy-efficient AI paradigms. However, the inherent non-linearity and temporal dependencies in SNNs introduce persistent challenges, such as temporal covariate shift (TCS) and unstable gradient flow with learnable neuron thresholds. In this paper, we present two key innovations: MP-Init (Membrane Potential Initialization) and TrSG (Threshold-robust Surrogate Gradient). MP-Init addresses TCS by aligning the initial membrane potential with its stationary distribution, while TrSG stabilizes gradient flow with respect to threshold voltage during training. Extensive experiments validate our approach, achieving state-of-the-art accuracy on both static and dynamic image datasets. The code is available at: https://github.com/kookhh0827/SNN-MP-Init-TRSG
RegionAligner: Bridging Ego-Exo Views for Object Correspondence via Unified Text-Visual Learning
Authors: Yuhao Su; Ehsan Elhamifar
Venue: WACV 2026 (main)

Abstract: Establishing object correspondence between egocentric (ego) and exocentric (exo) views is a critical capability for robot learning and human-robot interaction. The core task involves segmenting an object in one view given a query mask from the opposing view. This is notoriously difficult due to cluttered scenes with many task-irrelevant objects and drastic appearance changes across perspectives. To address this, we introduce RegionAligner, a unified text-visual framework that strategically focuses learning on task-relevant regions. Our method first uses a large vision-language model to identify and name salient objects, effectively filtering out visual distractors. These object phrases are then fused with visual features from both views. We introduce a novel region-guided supervision strategy that promotes focus, enforces spatial alignment, and minimizes appearance disparity between the ego-exo views. Furthermore, our framework seamlessly adapts to unsupervised settings by automatically generating pseudo-labels from matched mask proposals, drastically reducing annotation costs. Extensive experiments on the challenging Ego-Exo4D dataset show RegionAligner significantly outperforms existing baselines, improving IoU by 10.16% (ego-to-exo) and 6.04% (exo-to-ego).
Matching Semantically Similar Non-Identical Objects
Authors: Yusuke Marumo; Kazuhiko Kawamoto; Satomi Tanaka; Shigenobu Hirano; Hiroshi Kera
Venue: WACV 2026 (main)

Abstract: Not identical but similar objects are ubiquitous in our world, ranging from four-legged animals such as dogs and cats to cars of different models and flowers of various colors. This study addresses a novel task of matching such non-identical objects at the pixel level. We propose a weighting scheme of descriptors, Semantic Enhancement Weighting (SEW), that incorporates semantic information from object detectors into existing sparse feature matching methods, extending their targets from identical objects captured from different perspectives to semantically similar objects. The experiments show successful matching between non-identical objects in various cases, including in-class design variations, class discrepancy, and domain shifts (e.g., photo vs. drawing and image corruptions). The code is available at https://github.com/Circ-Leaf/NIOM.
SphereEdit: Spherical Semantic Editing in Diffusion Models
Authors: Salamata Konate; Hassan Hamidi; Elham Dolatabadi; Frank Rudzicz; Laleh Seyyed-Kalantari
Venue: WACV 2026 (main)

Abstract: Despite significant advances in diffusion models, achieving precise and composable image editing without task-specific training remains a challenge. Existing approaches often rely on iterative optimization or linear latent operations, which are slow, brittle, and prone to attribute entanglement (e.g., editing "lipstick" inadvertently alters skin tone). We introduce SphereEdit, a training-free framework that leverages the spherical geometry of diffusion embeddings and token aware cross-attention to enable interpretable, fine-grained control. We represent semantic attributes as unit vector directions in the denoiser's prediction space and show that antipodal symmetry ("old" is approximately the negation of "young") naturally supports bidirectional edits, while approximate orthogonality enables clean composition through spherical coefficient. At inference, these directions modulate cross-attention activations, producing spatially localized edits without optimization or fine-tuning. SphereEdit achieves sharper, more disentangled edits than prior baselines, while remaining plug-and-play and applicable across diverse image editing tasks.
Pyramidal Spectrum: Frequency-based Hierarchically Vector Quantized VAE for Videos
Authors: Tushar Prakash; Onkar Susladkar; Sparsh Mittal; Inderjit S Dhillon
Venue: WACV 2026 (main)

Abstract: Variational Autoencoders (VAEs) form the foundation of modern video generation models. In particular, discrete latent VAEs with vector quantization have gained prominence for their superior perceptual sharpness, ability to model long-range dynamics, and efficient adaptation to downstream tasks. However, existing discrete VAEs face two key limitations: (i) a lack of frequency-domain modeling to enhance global spatiotemporal understanding, and (ii) fixed-resolution quantization schemes, preventing effective modeling of coarse-to-fine spatiotemporal hierarchies essential for video generation. To address these limitations, we propose a Pyramidal Vector Quantized Variational Autoencoder (PVQ-VAE) for videos. PVQ-VAE's encoder-decoder leverages Fast Fourier Transform and Discrete Wavelet Transform to capture global semantics and multi-scale local details jointly. We introduce Pyramidal Vector Quantization (PVQ), a hierarchical quantization scheme that discretizes features at multiple resolutions to better capture multi-scale information. To further boost fidelity, we introduce a cross-modal contrastive loss guided by a pretrained high-resolution image VAE. PVQ-VAE achieves state-of-the-art performance on WebVid-val, COCO-val, and MCL-JCV, reconstructing videos with high perceptual quality at up to 32x spatial and 16x temporal compression.
Large Sign Language Models: Toward 3D American Sign Language Translation
Authors: Sen Zhang; Xiaoxiao He; Di Liu; Zhaoyang Xia; Mingyu Zhao; Chaowei Tan; Vivian Li; Bo Liu; Dimitris N. Metaxas; Mubbasir Kapadia
Venue: WACV 2026 (main)

Abstract: We present Large Sign Language Models (LSLM), a novel framework for translating 3D American Sign Language (ASL) by leveraging Large Language Models (LLMs) as the backbone, which can benefit hearing-impaired individuals' virtual communication. Unlike existing sign language recognition methods that rely on 2D video, our approach directly utilizes 3D sign language data to capture rich spatial, gestural, and depth information in 3D scenes. This enables more accurate and resilient translation, enhancing digital communication accessibility for the hearing-impaired community. Beyond the task of ASL translation, our work explores the integration of complex, embodied multimodal languages into the processing capabilities of LLMs, moving beyond purely text-based inputs to broaden their understanding of human communication. We investigate both direct translation from 3D gesture features to text and an instruction-guided setting where translations can be modulated by external prompts, offering greater flexibility. This work provides a foundational step toward inclusive, multimodal intelligent systems capable of understanding diverse forms of language.
Learning Mask-Aware Offsets: Two-branch Deformable Attention Networks for Inpainting with Masked Region Avoidance
Authors: Hyeongseok Oh; Joonki Paik
Venue: WACV 2026 (main)

Abstract: Image inpainting restores missing regions in a visually plausible way, yet existing methods struggle with irregular holes and complex structures due to fixed kernels and static attention. In this paper, we propose Mask-Aware Deformable Inpainting Network (MADIN), which incorporates mask information for position-aware control. The proposed model employs a Two-branch Offset Estimator that jointly utilizes query features and mask signals to reliably predict reference positions even within masked regions. In addition, Adaptive Offset Range Scaling adjusts offset magnitude based on masking ratio to capture broader context when needed. Experiments on CelebA-HQ and Places2 show that MADIN achieves state-of-the-art results in PSNR, SSIM, LPIPS, and FID, while remaining lightweight and efficient. The code will be released at https://github.com/ohhhyeongsn/MADIN
QuadraNet V2: Efficient and Sustainable Training of High-Order Neural Networks with Quadratic Adaptation
Authors: Chenhui Xu; Fuxun Yu; Jinjun Xiong; Xiang Chen
Venue: WACV 2026 (main)

Abstract: Machine learning is evolving towards high-order models that necessitate pre-training on extensive datasets, a process associated with significant overheads. Traditional models, despite having pre-trained weights, are becoming obsolete due to architectural differences that obstruct the effective transfer and initialization of these weights. To address these challenges, we introduce a novel framework, QuadraNet V2, which leverages quadratic neural networks to create efficient and sustainable high-order learning models. Our method initializes the primary term of the quadratic neuron using a standard neural network, while the quadratic term is employed to adaptively enhance the learning of data non-linearity or shifts. This integration of pre-trained primary terms with quadratic terms, which possess advanced modeling capabilities, significantly augments the information characterization capacity of the high-order network. By utilizing existing pre-trained weights, QuadraNet V2 reduces the required GPU hours for training by 90% to 98.4% compared to training from scratch, demonstrating both efficiency and effectiveness.
Occlusion Boundary and Depth: Mutual Enhancement via Multi-Task Learning
Authors: Lintao Xu; Yinghao Wang; Chaohui Wang
Venue: WACV 2026 (main)

Abstract: Occlusion Boundary Estimation (OBE) identifies boundaries arising from both inter-object occlusions and self-occlusion within individual objects. This task is closely related to Monocular Depth Estimation (MDE), which infers depth from a single image, as Occlusion Boundaries (OBs) provide critical geometric cues for resolving depth ambiguities, while depth can conversely refine occlusion reasoning. In this paper, we aim to systematically model and exploit this mutually beneficial relationship. To this end, we propose MoDOT, a novel framework for joint estimation of depth and OBs, which incorporates a new Cross-Attention Strip Module (CASM) to leverage mid-level OB features for depth prediction, and a novel OB-Depth Constraint Loss (OBDCL) to enforce geometric consistency. To facilitate this study, we contribute OB-Hypersim, a large-scale photorealistic dataset with precise depth and self-occlusion-handled OB annotations. Extensive experiments on two synthetic datasets and NYUD-v2 demonstrate that MoDOT achieves significantly better performance than single-task baselines and multi-task competitors. Furthermore, models trained solely on our synthetic data demonstrate strong generalization to real-world scenes without fine-tuning, producing depth maps with sharper boundaries and improved geometric fidelity. Collectively, these results underscore the significant benefits of jointly modeling OBs and depth. Code and resources are available at https://github.com/xul-ops/MoDOT.
3D Superquadric Splatting
Authors: Daniel MacSwayne; Ales Leonardis; Jianbo Jiao
Venue: WACV 2026 (main)

Abstract: Gaussian Splatting has proven to be an effective algorithm for novel view synthesis and 3D reconstruction from multi-view images. However, the underlying volumetric primitive -- the ellipsoidal Gaussian-- has limited expressive capabilities, leading to difficulties in 3D modelling (especially geometry such as edges, corners, and high curvature). To address this limitation, in this paper, we introduce Superquadric Splats (SQS), an extended class of volumetric primitives, as a super-set of Gaussian splats, to model more detailed geometry. We treat superquadrics as volumetric distance functions rather than level-set surfaces. A non-trivial differentiable rendering pipeline is developed to support this. Extensive experimental analysis on multiple datasets validates the effectiveness of the proposed SQS approach, showing both enhanced visual and geometric performance compared to Gaussian-based splatting (with more than 1dB in PSNR and prominent geometric improvement).
S2O: Static to Openable Enhancement for Articulated 3D Objects
Authors: Denys Iliash; Hanxiao Jiang; Yiming Zhang; Manolis Savva; Angel X. Chang
Venue: WACV 2026 (main)

Abstract: Despite much progress in large 3D datasets there are currently few interactive 3D object datasets, and their scale is limited due to the manual effort required in their construction. We introduce the static to openable (S2O) task which creates interactive articulated 3D objects from static counterparts through openable part detection, motion prediction, and interior geometry completion. We formulate a unified data generation framework to tackle this task, and curate a challenging dataset of openable 3D objects that serves as a test bed for systematic evaluation. Our experiments benchmark methods from prior work, extended and improved methods, and simple yet effective heuristics for the S2O task. We find that turning static 3D objects into interactively openable counterparts is possible but that all methods struggle to generalize to realistic settings of the task on our dataset, and we highlight promising future work directions. Our work enables efficient creation of interactive 3D objects for robotic manipulation and embodied AI tasks.
Personalized Image Privacy Advisors via Federated Daisy-Chaining
Authors: Sourasekhar Banerjee; Vengateswaran Subramaniam; Debaditya Roy; Vigneshwaran Subbaraju; Monowar Bhuyan
Venue: WACV 2026 (main)

Abstract: Image sharing on social media has become routine but poses serious privacy risks, as users may unknowingly expose sensitive information. This necessitates an image privacy advisor that assigns personalized privacy risk scores, helping users decide whether to share images publicly or not. However, centralized training of such models risks user data exposure and loss of ownership, as data must be uploaded to a central server. To safeguard user privacy, we adopt Federated Learning (FL), which enables collaborative model training without sharing raw data. Despite its advantages, FL faces challenges such as data heterogeneity from diverse user privacy preferences, limited annotations per user, and communication overhead. To address these issues, we propose CFedDC, a personalized FL algorithm combined with PIONet, a mobile friendly model with 14.5x fewer trainable parameters and 93.09% lower memory footprint than centralized baselines. CFedDC mitigates data heterogeneity through clustering and cluster aware regularization with stability, and tackles data scarcity using a daisy-chaining knowledge transfer mechanism. Comprehensive experimental evaluations demonstrate that our proposed method achieves well-aligned personalized user privacy scores, outperforming existing centralized and FL-based image privacy models.
PoseGaussian: Pose-Driven Novel View Synthesis for Robust 3D Human Reconstruction
Authors: Ju Shen; Chen Chen; Tam V. Nguyen; Vijayan K. Asari
Venue: WACV 2026 (main)

Abstract: We propose PoseGaussian, a pose-guided Gaussian Splatting framework for high-fidelity human novel view synthesis. Human body pose serves a dual purpose in our design: as a structural prior, it is fused with a color encoder to refine depth estimation; as a temporal cue, it is processed by a dedicated pose encoder to enhance temporal consistency across frames. These components are integrated into a fully differentiable, end-to-end trainable pipeline. Unlike prior works that use pose only as a condition or for warping, PoseGaussian embeds pose signals into both geometric and temporal stages to improve robustness and generalization. It is specifically designed to address challenges inherent in dynamic human scenes, such as articulated motion and severe self-occlusion. Notably, our framework achieves real-time rendering at 100 FPS, maintaining the efficiency of standard Gaussian Splatting pipelines. We validate our approach on ZJU-MoCap, THuman2.0, and in-house datasets, demonstrating state-of-the-art performance in perceptual quality and structural accuracy (PSNR 30.86, SSIM 0.979, LPIPS 0.028).
UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training
Authors: Jiawei Qin; Xucong Zhang; Yusuke Sugano
Venue: WACV 2026 (main)

Abstract: Despite decades of research on data collection and model architectures, current gaze estimation models encounter significant challenges in generalizing across diverse data domains. Recent advances in self-supervised pre-training have demonstrated remarkable generalization across various vision tasks. However, their effectiveness in gaze estimation remains unexplored. We propose UniGaze, which for the first time leverages large-scale in-the-wild facial datasets for gaze estimation through self-supervised pre-training. Through systematic investigation, we clarify critical factors essential for effective pre-training in gaze estimation. Our experiments reveal that self-supervised approaches designed for semantic tasks fail when applied to gaze estimation, while our carefully designed pre-training pipeline consistently improves cross-domain performance. Through comprehensive experiments of the challenging cross-dataset evaluations and novel protocols, including leave-one-dataset-out and joint-dataset settings, we demonstrate that UniGaze significantly improves generalization across multiple data domains while minimizing reliance on costly labeled data. Source code and model are available at https://github.com/ut-vision/UniGaze.
One Model, Many Behaviors: Training-Induced Effects on Out-of-Distribution Detection
Authors: Gerhard Krumpl; Henning Avenhaus; Horst Possegger
Venue: WACV 2026 (main)

Abstract: Out-of-distribution (OOD) detection is crucial for deploying robust and reliable machine-learning systems in open-world settings. Despite steady advances in OOD detectors, their interplay with modern training pipelines that maximize in-distribution (ID) accuracy and generalization remains under-explored.We investigate this link through a comprehensive empirical study.Fixing the architecture to the widely adopted ResNet-50, we benchmark 21 post-hoc, state-of-the-art OOD detection methods across 54 ImageNet-trained models obtained via diverse training strategies and evaluate them on eight OOD test sets.Contrary to the common assumption that higher ID accuracy implies better OOD detection performance, we uncover a non-monotonic relationship: OOD performance initially improves with accuracy but declines once advanced training recipes push accuracy beyond the baseline.Moreover, we observe a strong interdependence between training strategy, detector choice, and resulting OOD performance, indicating that no single method is universally optimal.
DNA: Dual-branch Network with Adaptation for Open-Set Online Handwriting Generation
Authors: Tsai-Ling Huang; Nhat-Tuong Do-Tran; Ngoc-Hoang-Lam Le; Hong-Han Shuai; Ching-Chun Huang
Venue: WACV 2026 (main)

Abstract: Online handwriting generation (OHG) enhances handwriting recognition models by synthesizing diverse, human-like samples. However, existing OHG methods struggle to generate unseen characters, particularly in glyph-based languages like Chinese, limiting their real-world applicability. In this paper, we introduce our method for OHG, where the writer's style and the characters generated during testing are unseen during training. To tackle this challenge, we propose a Dual-branch Network with Adaptation (DNA), which comprises an adaptive style branch and an adaptive content branch. The style branch learns stroke attributes such as writing direction, spacing, placement, and flow to generate realistic handwriting. Meanwhile, the content branch is designed to generalize effectively to unseen characters by decomposing character content into structural information and texture details, extracted via local and global encoders, respectively. Extensive experiments demonstrate that our DNA model is well-suited for the unseen OHG setting, achieving state-of-the-art performance.
Universal Neural Architecture Space: Covering ConvNets, Transformers and Everything in Between
Authors: Ondrej Tybl; Lukas Neumann
Venue: WACV 2026 (main)

Abstract: We introduce Universal Neural Architecture Space (UniNAS), a generic search space for neural architecture search (NAS) which unifies convolutional networks, transformers, and their hybrid architectures under a single, flexible framework. Our approach enables discovery of novel architectures as well as analyzing existing architectures in a common framework. We also propose a new search algorithm that allows traversing the proposed search space, and demonstrate that the space contains interesting architectures, which, when using identical training setup, outperform state-of-the-art hand-crafted architectures. Finally, a unified toolkit including a standardized training and evaluation protocol is introduced to foster reproducibility and enable fair comparison in NAS research. Overall, this work opens a pathway towards systematically exploring the full spectrum of neural architectures with a unified graph-based NAS perspective. The source code is publicly available at  \smallhttps://www.github.com/ondratybl/UniNAS .