GenAI4ELab/papercli-papers huggingface dataset:
DiT-VTON: Diffusion Transformer Framework for Unified Multi-Category Virtual Try-On and Virtual Try-All with Integrated Image Editing
Authors: Qi Li; Shuwen Qiu; Kee Kiat Koo; Julien Han; Karim Bouyarmane
Venue: WACV 2026 (main)

Abstract: The rapid growth of e-commerce has intensified the demand for Virtual Try-On (VTO) technologies, enabling customers to realistically visualize products overlaid on their own images. Despite recent advances, existing VTO models face challenges with fine-grained detail preservation, robustness to real-world imagery, efficient sampling, image editing capabilities, and generalization across diverse product categories. In this paper, we present DiT-VTON, a novel VTO framework that leverages an architecture based on a Diffusion Transformer (DiT), renowned for its performance on text-conditioned image generation (text-to-image), adapted here for the image-conditioned VTO task. We systematically explore multiple DiT configurations, including in-context token concatenation, channel concatenation, and ControlNet integration, to determine the best setup for VTO image conditioning. To enhance robustness, we train the model on an expanded dataset encompassing varied backgrounds, unstructured references, and non-garment categories, demonstrating the benefits of data scaling for VTO adaptability. DiT-VTON also redefines the VTO task beyond garment try-on, offering a versatile Virtual Try-All (VTA) solution capable of handling a wide range of product categories and supporting advanced image editing functionalities, such as pose preservation, precise localized region editing and refinement, texture transfer and object-level customization. Experimental results show that our model surpasses state-of-the-art methods on public benchmark tests like VITON-HD on the VTO task, achieving superior detail preservation and robustness without reliance on additional image condition encoders. It also surpasses state-of-the-art models that have VTA and image editing capabilities on a varied dataset composed of thousands of product categories. As a result, DiT-VTON significantly advances VTO applicability in diverse real-world scenarios, enhancing both the realism and personalization of online shopping experiences.
VectorSynth: Fine-Grained Satellite Image Synthesis with Structured Semantics
Authors: Daniel Cher; Brian Wei; Srikumar Sastry; Nathan Jacobs
Venue: WACV 2026 (main)

Abstract: We introduce VectorSynth, a diffusion-based framework for pixel-accurate satellite image synthesis conditioned on polygonal geographic annotations with semantic attributes. Unlike prior text- or layout-conditioned models, VectorSynth learns dense cross-modal correspondences that align imagery and semantic vector geometry, enabling fine-grained, spatially grounded edits. A vision language alignment module produces pixel-level embeddings from polygon semantics; these embeddings guide a conditional image generation framework to respect both spatial extents and semantic cues. VectorSynth supports interactive workflows that mix language prompts with geometry-aware conditioning, allowing rapid what-if simulations, spatial edits, and map-informed content generation. For training and evaluation, we assemble a collection of satellite scenes paired with pixel-registered polygon annotations spanning diverse urban scenes with both built and natural features. We observe strong improvements over prior methods in semantic fidelity and structural realism, and show that our trained vision language model demonstrates fine-grained spatial grounding. Code and data will be released.
FlowEO: Generative Unsupervised Domain Adaptation for Earth Observation
Authors: Georges Le Bellier; Nicolas Audebert
Venue: WACV 2026 (main)

Abstract: The increasing availability of Earth observation data offers unprecedented opportunities for large-scale environmental monitoring and analysis. However, these datasets are inherently heterogeneous, stemming from diverse sensors, geographical regions, acquisition times, and atmospheric conditions. Distribution shifts between training and deployment domains severely limit the generalization of pretrained remote sensing models, making unsupervised domain adaptation (UDA) crucial for real-world applications.We introduce FlowEO, a novel framework that leverages generative models for image-space UDA in Earth observation. We leverage flow matching to learn a semantically preserving mapping that transports from the source to the target image distribution. This allows us to tackle challenging domain adaptation configurations for classification and semantic segmentation of Earth observation images. We conduct extensive experiments across four datasets covering adaptation scenarios such as SAR to optical translation and temporal and semantic shifts caused by natural disasters.Experimental results demonstrate that FlowEO outperforms existing image translation approaches for domain adaptation while achieving on-par or better perceptual image quality, highlighting the potential of flow-matching-based UDA for remote sensing.
Pose-Diverse Multi-View Virtual Try-on from a Single Frontal Image via Diffusion Transformer
Authors: Seonghee Han; Minchang Chung; Gyeongsu Cho; Kyungdon Joo; Taehwan Kim
Venue: WACV 2026 (main)

Abstract: We study multi-view virtual try-on framework from a single reference image of themselves and a single frontal image of a garment. While most existing approaches focus on single-view synthesis, their reliance on a single, fixed viewpoint limits their application in immersive environments that require diverse poses and viewpoints. The ability to generate a multi-view virtual try-on image is crucial for a comprehensive user experience, as if the user to inspect the garment from multiple views, including the back and sides, providing a similar experience to a real fitting room. In this paper, we propose a novel framework for pose-controllable, multi-view virtual try-on from a single image. Our method incorporates the proposed cross attention injection for high-quality synthesis and allows for fine-grained pose control. Our framework consists of two stages: the first stage generates a clean frontal try-on result using an off-the-shelf model, and the second stage creates diverse viewpoints using a diffusion transformer. Additionally, an attention injection mechanism ensures consistent identity and garment preservation across synthesized views. Unlike conventional methods that require multiple images of the user or the garment from various angles, our model eliminates these constraints by synthesizing multi-view results from a single input image pair. Our method not only generates realistic images but also enables users to virtually inspect the fit and arrangement of the garment from multiple angles without the need for additional data. Our extensive experiments demonstrate that our framework outperforms existing 3D-based multi-view virtual try-on methods in terms of image quality and pose diversity.
ObjectMeshDeform : Towards Recovering Precise 3D Geometry of Real Objects via Image-guided Mesh Deformation of 3D Generative Priors
Authors: Siddharth Katageri; Sanjana Sinha; Sourav Ghosh; Soumyadip Maity; Brojeshwar Bhowmick
Venue: WACV 2026 (main)

Abstract: 3D Generative Models that synthesize high fidelity 3D assets from single view or multi-view images cannot recover precise geometry and real-world object measurements needed for practical applications. On the other hand, multi-view 3D reconstruction methods based on structure from motion, implicit surfaces, gaussian splatting, fail to recover high fidelity object meshes with dense geometry, shape regularity, smooth surfaces present in real world objects. In this paper we propose a novel approach that leverages a 3D mesh prior synthesized by generative models pre-trained on large scale 3D synthetic datasets. Our method refines the initial mesh geometry without use of any additional training data while improving accuracy of 3D geometry via multi-view consistency, without degrading the mesh surface quality. Our method can automatically reconstruct meshes from images of objects in real world scenes, without requiring any additional large-scale training data or manual inputs.
Cluster-based Pseudo-labeling for Semi-Supervised LiDAR Semantic Segmentation
Authors: Qingju Guo; Shuang Li; Jing Geng; Binhui Xie; Jiawei Shan; Wei Li
Venue: WACV 2026 (main)

Abstract: The costly annotation process has driven the development of semi-supervised learning (SSL) approaches. Existing semi-supervised LiDAR segmentation methods typically process entire point clouds directly, aiming to assign labels to all points at the scene scale. However, the large number of points, combined with their sparse and irregular nature, makes it challenging to learn scene-level optimization objectives, especially in SSL settings where labeled data are insufficient. This paper presents a Cluster-based pseudo-LAbeling Semi-Supervised technique, called CLASS. CLASS is designed to divide point clouds into several small, pure clusters, thereby decomposing challenging scene-scale segmentation task into more manageable cluster-scale classification and segmentation tasks, enabling the generation of high-quality pseudo labels for unlabeled data. CLASS possesses three key properties. i) Task simplicity: our pseudo-labeling process is based on simpler cluster-scale classification and segmentation tasks, resulting in ease of learning. ii) Labeling effectiveness: CLASS can generate pseudo-labels comparable to ground truth using only approximately 10% labeled data. iii) Universal versatility: CLASS exhibits flexibility regarding LiDAR representations (e.g., BEV, voxel, and range view). Comprehensive experiments on popular LiDAR segmentation benchmarks demonstrate its superiority.
Memory-Augmented Representation for Efficient Event-based Visuomotor Policy Learning with Adaptive Perception and Control
Authors: Uday Kamal; Saibal Mukhopadhyay
Venue: WACV 2026 (main)

Abstract: Event-based cameras are well-suited for fast and agile autonomous navigation due to their ultra-fast, microsecond level temporal resolution. However, fully leveraging this potential requires highly efficient processing algorithms capable of asynchronous, event-by-event representations and policy updates. Current methods employ synchronous dense representation or process events in a fixed-rate time windows, leading to inefficiencies via redundant computation. We address this by proposing an end-to-end framework for event-to-control policy learning designed for reactive navigation tasks. Our method consists of a memory-augmented perception module that updates the representation asynchronously and adaptively selects the number of events to process. Using the memory representation, a lightweight policy module is jointly optimized with the perception module, and learns to predict control commands at rates that dynamically adjust to scene complexity in an event-based reinforcement learning setting. Evaluations on simulated drone navigation tasks demonstrate higher sample efficiency and robustness compared to dense frame-based methods. Moreover, our approach significantly reduces computational complexity by minimizing processing steps and event counts while maintaining competitive performance against state-of-the-art event-based methods.
DiffRegCD: Integrated Registration and Change Detection with Diffusion Features
Authors: Seyedehanita Madani; Rama Chellappa; Vishal M. Patel
Venue: WACV 2026 (main)

Abstract: Change detection (CD) is critical in computer vision and remote sensing, with applications in monitoring, disaster response, and urban analysis. Most CD models assume co-registered inputs, but real imagery often suffers from parallax, viewpoint shifts, or long temporal gaps, leading to severe misalignment. Conventional register-then-detect pipelines and recent joint frameworks (e.g., BiFA, ChangeRD) remain limited: they rely on regression-only flow, global homographies, or synthetic perturbations that fail under large displacements. We propose DiffRegCD, an integrated framework that couples dense registration and change detection. DiffRegCD reformulates correspondence as a Gaussian-smoothed classification task, delivering sub-pixel accuracy and stable training. It builds on frozen multi-scale features from a pretrained denoising diffusion model, which provide invariance to viewpoint and illumination variation. Supervision is enabled by controlled affine perturbations applied to standard CD datasets, yielding paired ground truth for both flow and change detection without pseudo-labels. Experiments on aerial (LEVIR-CD, DSIFN-CD, WHU-CD, SYSU-CD) and ground-level (VL-CMU-CD) datasets show that DiffRegCD outperforms recent baselines and remains robust under wide temporal and viewpoint variation, establishing diffusion features and classification-based correspondence as a strong foundation for integrated CD.
LangPose: Language-Aligned Motion for Robust 3D Human Pose Estimation
Authors: Longyun Liao; Rong Zheng
Venue: WACV 2026 (main)

Abstract: 2D-to-3D human pose lifting is an ill-posed problem due to depth ambiguity and occlusion. Existing methods relying on spatial and temporal consistency alone are insufficient to resolve these problems especially in the presence of significant occlusions or high dynamic actions. Semantic information, however, offers a complementary signal that can help disambiguate such cases. To this end, we propose LangPose, a framework that leverages action knowledge by aligning motion embeddings with text embeddings of fine-grained action labels. LangPose operates in two stages: pretraining and fine-tuning. In the pretraining stage, the model simultaneously learns to recognize actions and reconstruct 3D poses from masked and noisy 2D poses. During the fine-tuning stage, the model is further refined using real-world 3D human pose estimation datasets without action labels. Additionally, our framework incorporates masked body parts and masked time windows in motion modeling, encouraging the model to leverage semantic information when spatial and temporal consistency is unreliable. Experiments demonstrate the effectiveness of LangPose, achieving SOTA level performance in 3D pose estimation on public datasets, including Human3.6M and MPI-INF-3DHP. Specifically, LangPose achieves an MPJPE of 36.7mm on Human3.6M with detected 2D poses as input and 15.5mm on MPI-INF-3DHP with ground-truth 2D poses as input.
Accelerated Dose Generation in Gamma Knife Radiosurgery Using a Wavelet Diffusion Model for Sparse Representation
Authors: Sangyoon Lee; Shubuendu Mishra; Yoichi Watanabe
Venue: WACV 2026 (main)

Abstract: Accurate dose calculation is critical in Gamma Knife Radiosurgery (GKRS), especially in regions near the skull where tissue heterogeneity can significantly alter dose distributions. Although the convolution algorithm-based dose calculation (Conv dose) using CT enables heterogeneity correction for an accurate treatment plan, it introduces additional clinical burdens, including longer planning times and increased radiation exposure, and routine acquisition of CT is not standard practice in GKRS. Moreover, Conv dose is computationally intensive, requiring up to several minutes for large targets, and in a clinical workflow where beam parameters and shot placements are iteratively adjusted, such delays can accumulate. Consequently, there is a growing demand for methods that can generate heterogeneity-aware convolution-equivalent doses in real time, on the order of a few seconds, to support smooth interactive plan adaptation. This study is the first to explore using the conditional wavelet Denoising Diffusion Probabilistic Model (cwDDPM) to generate radiation dose distributions. cwDDPM exploits the inherent sparsity of radiation dose maps in the wavelet domain to achieve more efficient learning and sampling. As a result, it produces synthetic Conv doses quickly and accurately, without relying on CT imaging or computationally intensive convolution calculations. Quantitative results across isodose overlap and dose-volume metrics demonstrate that cwDDPM achieves high fidelity to the ground truth Conv dose and performs comparably to state-of-the-art diffusion models, while reducing inference time by up to 45-fold. cwDDPM also demonstrates robustness across different tumor locations, especially near the skull, where heterogeneity correction is most critical. These results suggest that cwDDPM is a promising solution for rapid, CT-free Conv dose generation in GKRS planning.
Lorentz Entailment Cone for Semantic Segmentation
Authors: Zahid Hasan; Masud Ahmed; Nirmalya Roy
Venue: WACV 2026 (main)

Abstract: Semantic segmentation in hyperbolic space can capture hierarchical structure in low dimensions with uncertainty quantification. Existing approaches choose the Poincare ball model for hyperbolic geometry, which suffers from numerical instabilities, optimization, and computational challenges. We propose a novel, tractable, architecture-agnostic semantic segmentation framework in the hyperbolic Lorentz model. We employ text embeddings with semantic and visual cues to guide hierarchical pixel-level representations in Lorentz space. This enables stable and efficient optimization without requiring a Riemannian optimizer, and easily integrates with existing Euclidean architectures. Beyond segmentation, our approach yields free uncertainty estimation, confidence map, boundary delineation, hierarchical and text-based retrieval, and zero-shot performance, reaching generalized flatter minima. We further introduce a novel uncertainty and confidence indicator in Lorentz cone embeddings. Extensive experiments on ADE20K, COCO-Stuff-164k, Pascal-VOC, and Cityscapes with state-of-the-art models (DeepLabV3 and SegFormer) validate the effectiveness and generality of our approach. Our results demonstrate the potential of hyperbolic Lorentz embeddings for robust and uncertainty-aware semantic segmentation. Code is available at https://github.com/mxahan/Lorentz_semantic_segmentation.
Can We Challenge Open-Vocabulary Object Detectors with Generated Content in Street Scenes?
Authors: Annika Mütze; Sadia Ilyas; Christian Dörpelkus; Matthias Rottmann
Venue: WACV 2026 (main)

Abstract: Open-vocabulary object detectors such as Grounding DINO are trained on vast and diverse data, achieving remarkable performance on challenging datasets. Due to that, it is unclear where to find their limitations, which is of major concern when using in safety-critical applications. Real-world data does not provide sufficient control, required for a rigorous evaluation of model generalization. In contrast, synthetically generated data allows to systematically explore the boundaries of model competence/generalization. In this work, we address two research questions: 1) Can we challenge open-vocabulary object detectors with generated image content? 2) Can we find systematic failure modes of those models? To address these questions, we design two automated pipelines using stable diffusion to inpaint unusual objects with high diversity in semantics, by sampling multiple substantives from WordNet and ChatGPT. On the synthetically generated data, we evaluate and compare multiple open-vocabulary object detectors as well as a classical object detector. The synthetic data is derived from two real-world datasets, namely LostAndFound, a challenging out-of-distribution (OOD) detection benchmark, and the NuImages dataset. Our results indicate that inpainting can challenge open-vocabulary object detectors in terms of overlooking objects. Additionally, we find a strong dependence of open-vocabulary models on object location, rather than on object semantics. This provides a systematic approach to challenge open-vocabulary models and gives valuable insights on how data could be acquired to effectively improve these models.
Lose Your Self (LoYS): An Adversarial Entropy-based Unsupervised Approach for Model Debiasing
Authors: Vito Paolo Pastore; Massimiliano Ciranni; Vittorio Murino
Venue: WACV 2026 (main)

Abstract: When spurious correlations between targets and data are present in training samples, deep neural networks may struggle to generalize, typically learning shortcuts corresponding to such undesired correlations (i.e., the bias), rather than fundamental target attributes. If bias attributes are assumed to be known, several strategies can be applied to mitigate a model's dependency on bias, including upsampling or upweighting samples with no bias. However, this is hardly the case in real-world scenarios, and alternative unsupervised debiasing approaches have been proposed in recent years, assuming no bias information is available. In this work, we propose Lose Your Self (LoYS), a novel bias-unsupervised approach for model debiasing. The main design strategy in LoYS aims to force a model learning semantic features, discouraging it from learning bias-related descriptors, specifically focusing on the target classifier confidence. Exploiting an adversarial scheme based on entropy loss, against a shallow auxiliary classifier trained to match the predictions of a pre-trained biased model, and entropy regularizations, our experiments show how LoYS is competitive or outperforms state-of-the-art methods, on several common benchmarks.
Autocorrelation-based Fiducial Markers for Traceability
Authors: Ismail Bencheikh; Max Dunitz; Marie d'Autume; Enric Meinhardt-Llopis; Marc Pic; Gabriele Facciolo; Pablo Musé
Venue: WACV 2026 (main)

Abstract: Classical approaches to the rectification of a single image of a product, without stereo correspondences, require spatial landmarks. These landmarks, constructed from high-contrast elementary shapes that can be detected with simple algorithms, are highly conspicuous. To rectify complex deformations, one can use chessboard patterns of markers with elements that break quadrilateral symmetry, such as the three eyes of a QR code. However, these marker boards are even more conspicuous than a single marker. In traceability applications, only one site of marking is used, limiting the complexity of the surface on which it can be read, and exposing the mark to deidentification attacks for diversion of the product to a grey market. We introduce a method for constructing stealth and robust fiducial markers that can be displayed across a surface, limiting exposure to marker tampering for product deidentification. These markers, which we refer to as self-rectifying textures, can be used to rectify complex deformations by solving an inverse problem rather than relying on pixel correspondences of conspicuous landmarks. These stealth textures place fiducial markers in the autocorrelation of the image. In this way, crops of the deformed texture can be rectified using only these spatially invariant statistical properties. Affine transformations of an image correspond to linear transformations of the autocorrelation, without phase component. Exploiting this fact, self-rectifying textures enable the local estimation of the differential of a planar deformation by identifying landmarks in the autocorrelation image, such as peaks, whose locations in the fronto-parallel view of the texture are known. The translation component can be recovered independently via phase correlation. A rectifying map, modulo translations, can also be fit directly to local observations of the differential of the deformation, without access to the rectified texture or need for phase correlation. Self-rectifying textures can be used for communication, watermarking, authentication, surface identification, calibration, and geometry processing.