GenAI4ELab/papercli-papers huggingface dataset:
DocWaveDiff: A Predict-and-Refine approach for Document Image Enhancement with Wavelet U-Nets and Diffusion models
Authors: Matteo Marulli; Marco Bertini
Venue: WACV 2026 (main)

Abstract: OCR and document layout analysis algorithms are essential components of AI-based document systems, yet they are typically trained on clean, degradation-free images. When applied to degraded documents such as blurred scans or pages spoiled by undesired handwritten text their performance drops significantly. To address this issue, we propose DocWaveDiff, a novel document restoration method based on a predict-and-refine diffusion framework incorporating wavelet U-Nets. Given a degraded image patch and optionally its prior features, our Early Predictor generates an initial restoration, which is then refined by a Denoiser Refiner that estimates the residual image. The combination of these outputs yields the final restored result. We evaluate DocWaveDiff on multiple public benchmarks and demonstrate its strong performance across various document degradation scenarios, including deblurring and handwriting removal. Our results confirm that integrating wavelet transforms into the predict-and-refine framework enhances restoration quality and supports more robust document understanding systems.
A-V Representation Learning via Audio Shift Prediction for Multimodal Deepfake Detection and Temporal Localization
Authors: Ashutosh Anshul; Eng Siong Chng; Deepu Rajan
Venue: WACV 2026 (main)

Abstract: Recent multimodal deepfake detection methods typically rely on single-stage training, which can cause the model to focus on dataset-specific multimodal cues while missing important features that are helpful to detect unseen manipulations, thereby limiting generalization. While some approaches attempt to address this using self-supervised audio-visual pretraining, they may not fully exploit cross-modal temporal information. Also, they often assume that manipulations affect the entire video, ignoring more realistic cases where only short segments are altered. To overcome these limitations, we propose a two-stage training framework that first learns audio-visual temporal alignment in real videos and then uses this information to detect and localize potential deepfakes by identifying temporal inconsistencies. We propose a self-supervised shift-prediction pretraining objective to fully understand cross-modal temporal alignment across multiple temporal shifts applied to the audio input. The pretrained features enable the model to identify manipulations across entire videos as well as accurately localize deepfake segments in partially tampered content. Moreover, the pretrained components do not require task-specific fine-tuning, improving the model's flexibility for both classification and localization. Experiments on benchmark datasets demonstrate strong within-dataset performance, robust generalization to new manipulations and datasets, and accurate temporal localization.
3D Gaussian Point Encoders
Authors: Jim James; Benjamin Wilson; Simon Lucey; James Hays
Venue: WACV 2026 (main)

Abstract: In this work, we introduce the 3D Gaussian Point Encoder, an explicit per-point embedding built on mixtures of learned 3D Gaussians. This explicit geometric representation for 3D recognition tasks is a departure from widely used implicit representations such as PointNet. However, it is difficult to learn 3D Gaussian encoders in end-to-end fashion with standard optimizers. We develop optimization techniques based on natural gradients and distillation from PointNets to find a Gaussian Basis that can reconstruct PointNet activations. The resulting 3D Gaussian Point Encoders are faster and more parameter efficient than traditional PointNets. As in the 3D reconstruction literature where there has been considerable interest in the move from implicit (e.g., NeRF) to explicit (e.g., Gaussian Splatting) representations, we can take advantage of computational geometry heuristics to accelerate 3D Gaussian Point Encoders further. We extend filtering techniques from 3D Gaussian Splatting to construct encoders that run 2.7x faster as a comparable accuracy PointNet while using 46% less memory and 88% fewer FLOPs. Furthermore, we demonstrate the effectiveness of 3D Gaussian Point Encoders as a component in Mamba3D, running 1.27x faster and achieving a reduction in memory and FLOPs by 42% and 54% respectively. 3D Gaussian Point Encoders are lightweight enough to achieve high framerates on CPU-only devices.
ODEt(ODEl): Shortcutting the Time and the Length in Diffusion and Flow Models for Faster Sampling
Authors: Denis Gudovskiy; Wenzhao Zheng; Tomoyuki Okuno; Yohei Nakata; Kurt Keutzer
Venue: WACV 2026 (main)

Abstract: Continuous normalizing flows (CNFs) and diffusion models (DMs) generate high-quality data from a noise distribution. However, their sampling process demands multiple iterations to solve an ordinary differential equation (ODE) with high computational complexity. State-of-the-art methods focus on reducing the number of discrete time steps during sampling to improve efficiency. In this work, we explore a complementary direction in which the quality-complexity tradeoff can also be controlled in terms of the neural network length. We achieve this by rewiring the blocks in the transformer-based architecture to solve an inner discretized ODE w.r.t. its depth. Then, we apply a length consistency term during flow matching training, and as a result, the sampling can be performed with an arbitrary number of time steps and transformer blocks. Unlike others, our ODE_t(ODE_l) approach is solver-agnostic in time dimension and reduces both latency and, importantly, memory usage. CelebA-HQ and ImageNet generation experiments show a latency reduction of up to 2x in the most efficient sampling mode, and FID improvement of up to 2.8 points for high-quality sampling when applied to prior methods. We open-source our code and checkpoints at github.com/gudovskiy/odelt.
Can Image Splicing and Copy-Move Forgery Be Detected by the Same Model? Forensim: An Attention-Based State-Space Approach
Authors: Soumyaroop Nandi; Prem Natarajan
Venue: WACV 2026 (main)

Abstract: We introduce Forensim, an attention-based state-space framework for image forgery detection that jointly localizes both manipulated (target) and source regions. Unlike traditional approaches that rely solely on artifact cues to detect spliced or forged areas, Forensim is designed to capture duplication patterns crucial for understanding context. In scenarios like protest imagery, detecting only the forged region--e.g., a duplicated act of violence inserted into a peaceful crowd--can mislead interpretation, highlighting the need for joint source-target localization. Forensim outputs three-class masks (pristine, source, target) and supports detection of both splicing and copy-move forgeries within a unified architecture. We propose a visual state-space model that leverages normalized attention maps to identify internal similarities, paired with a region-based block-attention module to distinguish manipulated regions. This design enables end-to-end training and precise localization. Forensim achieves state-of-the-art performance on standard benchmarks. We also release CMFD_Anything, a new dataset addressing limitations of existing copy-move forgery datasets. Source code and dataset will be released.
Controllable Long-term Motion Generation with Extended Joint Targets
Authors: Eunjong Lee; Eunhee Kim; Sanghoon Hong; Eunho Jung; Jihoon Kim
Venue: WACV 2026 (main)

Abstract: Generating stable and controllable character motion in real-time is a key challenge in computer animation. Existing methods often fail to provide fine-grained control or suffer from motion degradation over long sequences, limiting their use in interactive applications. We propose COMET, an autoregressive framework that runs in real time, enabling versatile character control and robust long-horizon synthesis. Our efficient Transformer-based conditional VAE allows for precise, interactive control over arbitrary user-specified joints for tasks like goal-reaching and in-betweening from a single model. To ensure long-term temporal stability, we introduce a novel reference-guided feedback mechanism that prevents error accumulation. This mechanism also serves as a plug-and-play stylization module, enabling real-time style transfer. Extensive evaluations demonstrate that COMET robustly generates high-quality motion at real-time speeds, significantly outperforming state-of-the-art approaches in complex motion control tasks and confirming its readiness for demanding interactive applications. Video results and code are available at https://comet-proj.github.io/.
CVP: Central-Peripheral Vision-Inspired Multimodal Model for Spatial Reasoning
Authors: Zeyuan Chen; Xiang Zhang; Haiyang Xu; Jianwen Xie; Zhuowen Tu
Venue: WACV 2026 (main)

Abstract: We present a central-peripheral vision-inspired framework (CVP), a simple yet effective multimodal model for spatial reasoning that draws inspiration from the two types of human visual fields -- central vision and peripheral vision. Existing approaches primarily rely on unstructured representations, such as point clouds, voxels, or patch features, and inject scene context implicitly via coordinate embeddings. However, this often results in limited spatial reasoning capabilities due to the lack of explicit, high-level structural understanding. To address this limitation, we introduce two complementary components into a Large Multimodal Model-based architecture: target-affinity token, analogous to central vision, that guides the model's attention toward query-relevant objects; and allocentric grid, akin to peripheral vision, that captures global scene context and spatial arrangements. These components work in tandem to enable structured, context-aware understanding of complex 3D environments. Experiments show that CVP achieves state-of-the-art performance across a range of 3D scene understanding benchmarks.
TopoRec: Point Cloud Recognition Using Topological Data Analysis
Authors: Anirban Ghosh; Iliya Kulbaka; Ian Dahlin; Ayan Dutta
Venue: WACV 2026 (main)

Abstract: Point cloud-based object/place recognition remains a problem of interest in applications such as autonomous driving, scene reconstruction, and localization. Extracting a meaningful global descriptor from a query point cloud that can be matched with the descriptors of the database point clouds is a challenging problem. Furthermore, when the query point cloud is noisy or has been transformed (e.g., rotated), it adds to the complexity. To this end, we propose a novel methodology, named TopoRec, which utilizes Topological Data Analysis (TDA) for extracting local descriptors from a point cloud, thereby eliminating the need for resource-intensive GPU-based machine learning training. More specifically, we used the ATOL vectorization method to generate vectors for point clouds. To test the quality of the proposed TopoRec technique, we have implemented it on multiple real-world (e.g., Oxford RobotCar, NCLT) and realistic (e.g., ShapeNet) point cloud datasets for large-scale place and object recognition, respectively. Unlike existing learning-based approaches such as PointNetVLAD and PCAN, our method does not require extensive training, making it easily adaptable to new environments. Despite this, it consistently outperforms both state-of-the-art learning-based and handcrafted baselines (e.g., M2DP, ScanContext) on standard benchmark datasets, demonstrating superior accuracy and strong generalization.
SFMNet: Sparse Focal Modulation for 3D Object Detection
Authors: Oren Shrout; Ayellet Tal
Venue: WACV 2026 (main)

Abstract: We propose SFMNet, a novel 3D sparse detector that combines the efficiency of sparse convolutions with the ability to model long-range dependencies. While traditional sparse convolution techniques efficiently capture local structures, they struggle with modeling long-range relationships. However, these relationships are essential for 3D object detection. In contrast, transformers are designed to capture these long-range dependencies through attention mechanisms. But, they come with high computational costs, due to their quadratic query-key-value interactions. Furthermore, directly applying attention to non-empty voxels is inefficient due to the sparse nature of 3D scenes. Our SFMNet is built on a novel Sparse Focal Modulation (SFM) module, which integrates short- and long-range contexts with linear complexity by leveraging a new hierarchical sparse convolution design. This approach enables SFMNet to achieve high detection performance with improved efficiency, making it well-suited for large-scale LiDAR scenes. We show that our detector achieves state-of-the-art performance on autonomous driving datasets.
Not Like Transformers: Drop the Beat Representation for Dance Generation with Mamba-Based Diffusion Model
Authors: Sangjune Park; Inhyeok Choi; Donghyeon Soon; Youngwoo Jeon; Kyungdon Joo
Venue: WACV 2026 (main)

Abstract: Dance is a form of human motion characterized by emotional expression and communication, playing a role in various fields such as music, virtual reality, and content creation. Existing methods for dance generation often fail to adequately capture the inherently sequential, rhythmical, and music-synchronized characteristics of dance. In this paper, we propose MambaDance, a new dance generation approach that leverages a Mamba-based diffusion model. Mamba, well-suited to handling long and autoregressive sequences, is integrated into our two-stage diffusion architecture, substituting off-the-shelf Transformer. Additionally, considering the critical role of musical beats in dance choreography, we propose a Gaussian-based beat representation to explicitly guide the decoding of dance sequences. Experiments on AIST++ and FineDance datasets for each sequence length show that our proposed method effectively generates plausible dance movements while reflecting essential characteristics, consistently from short to long dances, compared to the previous methods. Additional qualitative results and demo videos are available at \small https://vision3d-lab.github.io/mambadance .
Multi-Grained Text-Guided Image Fusion for Multi-Exposure and Multi-Focus Scenarios
Authors: Mingwei Tang; Jiahao Nie; Guang Yang; Ziqing Cui; Jie Li
Venue: WACV 2026 (main)

Abstract: Image fusion aims to synthesize a single high-quality image from a pair of inputs captured under challenging conditions, such as differing exposure levels or focal depths. A core challenge lies in effectively handling disparities in dynamic range and focus depth between the inputs. With the advent of vision-language models, recent methods incorporate textual descriptions as auxiliary guidance to enhance fusion quality. However, simply incorporating coarse-grained descriptions hampers the understanding of fine-grained details and poses challenges for precise cross-modal alignment. To address these limitations, we propose Multi-grained Text-guided Image Fusion (MTIF), a novel fusion paradigm with three key designs. First, it introduces multi-grained textual descriptions that separately capture fine details, structural cues, and semantic content, guiding image fusion through a hierarchical cross-modal modulation module. Second, it involves supervision signals at each granularity to facilitate alignment between visual and textual features and enhance the utility of auxiliary text. Third, it adopts a saliency-driven enrichment module to augment training data with dense semantic content, further strengthening the cross-modal modulation and alignment. Extensive experiments show that MTIF consistently outperforms previous methods on both multi-exposure and multi-focus image fusion tasks.
Odo: Depth-Guided Diffusion for Identity-Preserving Body Reshaping
Authors: Siddharth Khandelwal; Sridhar Kamath; Arjun Jain
Venue: WACV 2026 (main)

Abstract: Human shape editing enables controllable transformation of a person's body shape, such as thin, muscular, or overweight, while preserving pose, identity, clothing, and background. Unlike human pose editing, which has advanced rapidly, shape editing remains relatively under-explored. Current approaches typically rely on 3D morphable models or image warping, often introducing unrealistic body proportions, texture distortions, and background inconsistencies due to alignment errors and deformations. A key limitation is the lack of large-scale, publicly available datasets for training and evaluating body shape manipulation methods. In this work, we introduce the first large-scale dataset of 18,573 images across 1523 subjects, specifically designed for controlled human shape editing. It features diverse variations in body shape, including fat, muscular and thin, captured under consistent identity, clothing, and background conditions. Using this dataset, we propose Odo, an end-to-end diffusion-based method that enables realistic and intuitive body reshaping guided by simple semantic attributes. Our approach combines a frozen UNet that preserves fine-grained appearance and background details from the input image with a ControlNet that guides shape transformation using target SMPL depth maps. Extensive experiments demonstrate that our method outperforms prior approaches, achieving per-vertex reconstruction errors as low as 7.5mm, significantly lower than the 13.6mm observed in baseline methods, while producing realistic results that accurately match the desired target shapes.
Enhancing Visual Planning with Auxiliary Tasks and Multi-token Prediction
Authors: Ce Zhang; Yale Song; Ruta Desai; Michael Louis Iuzzolino; Joseph Tighe; Gedas Bertasius; Satwik Kottur
Venue: WACV 2026 (main)

Abstract: Visual Planning for Assistance (VPA) aims to predict a sequence of user actions required to achieve a specified goal based on a video showing the user's progress. Although recent advances in multimodal large language models (MLLMs) have shown promising results in video understanding, long-horizon visual planning remains a challenging problem. We identify two challenges in training large MLLMs for video-based planning tasks: (1) scarcity of procedural annotations, limiting the model's ability to learn procedural task dynamics effectively, and (2) inefficiency of next-token prediction objective to explicitly capture the structured action space for visual planning when compared to free-form, natural language. To tackle data scarcity, we introduce Auxiliary Task Augmentation. We design and train our model on auxiliary tasks relevant to long-horizon video-based planning (e.g., goal prediction) to augment the model's planning ability. To more explicitly model the structured action space unique to visual planning tasks, we leverage Multi-token Prediction, extending traditional next-token prediction by using multiple heads to predict multiple future tokens during training. Our approach, VideoPlan, achieves state-of-the-art VPA performance on the COIN and CrossTask datasets, surpassing prior methods by 7.3% and 3.4%, respectively, when predicting 3 future actions. We further extend our method to the challenging Ego4D Long-term Action Anticipation task, and show that it is on par with the state-of-the-art approaches despite not using specialized egocentric features. Code is available at https://github.com/CeeZh/VideoPlan.
F-INR: Functional Tensor Decomposition for Implicit Neural Representations
Authors: Sai Karthikeya Vemuri; Tim Büchner; Joachim Denzler
Venue: WACV 2026 (main)

Abstract: Implicit Neural Representations (INRs) model signals as continuous, differentiable functions.However, monolithic INRs scale poorly with data dimensionality, leading to excessive training costs.We propose F-INR, a framework that addresses this limitation by factorizing a high-dimensional INR into a set of compact, axis-specific sub-networks based on functional tensor decomposition.These sub-networks learn low-dimensional functional components that are then combined via tensor operations.This factorization reduces computational complexity while additionally improving representational capacity.F-INR is both architecture- and decomposition-agnostic.It integrates with various existing INR backbones (e.g., SIREN, WIRE, FINER, Factor Fields) and tensor formats (e.g., CP, TT, Tucker), offering fine-grained control over the speed-accuracy trade-off via the tensor rank and mode.Our experiments show F-INR accelerates training by up to 20xand improves fidelity by over 6.0 dB PSNR compared to state-of-the-art INRs.We validate these gains on diverse tasks, including image representation, 3D geometry reconstruction, and neural radiance fields.We further show F-INR's applicability to scientific computing by modeling complex physics simulations. Thus, F-INR provides a scalable, flexible, and efficient framework for high-dimensional signal modeling.
Dronaquatics: Real-time Swimming Analytics Using Drone Captured Imagery
Authors: Thu Tran; Harold Abraham Joseph; Kichang Lee; Kenny Tsu Wei Choo; Dong Ma; Shaohui Foong; Thivya Kandappu; Jeonggil Ko; Rajesh Balan
Venue: WACV 2026 (main)

Abstract: Accurate swimming performance monitoring has traditionally relied on wearable sensors, which can disrupt natural technique and are often impractical in competitive settings. In this paper, we present a fully vision-based system for automatic swimmer analysis using overhead drone footage, removing the need for any body-mounted device or underwater equipment. By fine-tuning pose estimation models for aerial aquatic conditions, our approach robustly extracts full-body swimmer skeletons even under challenging scenarios such as splashes and partial occlusions. From these poses, we classify swimming strokes, compute instantaneous speed, estimate lap times, and count individual strokes. Unlike existing methods, our system provides scalable, unobtrusive, and infrastructure-free tracking. Evaluated on real-world drone-captured swimming competition data, our method achieves a median speed estimation error below 4% (under 0.05 m/s), a median lap time error of just 0.03s, and stroke count errors typically under one stroke per lap.
GASP: Unifying Geometric and Semantic Self-Supervised Pre-training for Autonomous Driving
Authors: William Ljungbergh; Adam Lilja; Adam Tonderski; Arvid Laveno Ling; Carl Lindström; Willem Verbeke; Junsheng Fu; Christoffer Petersson; Lars Hammarstrand; Michael Felsberg
Venue: WACV 2026 (main)

Abstract: Self-supervised pre-training based on next-token prediction has enabled large language models to capture the underlying structure of text, and has led to unprecedented performance on a large array of tasks when applied at scale. Similarly, autonomous driving generates vast amounts of spatiotemporal data, alluding to the possibility of harnessing scale to learn the underlying geometric and semantic structure of the environment and its evolution over time. In this direction, we propose a geometric and semantic self-supervised pre-training method, GASP, that learns a unified representation by predicting, at any queried future point in spacetime, (1) general occupancy, capturing the evolving structure of the 3D scene; (2) ego occupancy, modeling the ego vehicle path through the environment; and (3) distilled high-level features from a vision foundation model. By modeling geometric and semantic 4D occupancy fields instead of raw sensor measurements, the model learns a structured, generalizable representation of the environment and its evolution through time. We validate GASP on multiple autonomous driving benchmarks, demonstrating significant improvements in semantic occupancy forecasting, online mapping, and ego trajectory prediction. Our results demonstrate that continuous 4D geometric and semantic occupancy prediction provides a scalable and effective pre-training paradigm for autonomous driving. Code will be released.