GenAI4ELab/papercli-papers huggingface dataset:
AuthGuard: Generalizable Deepfake Detection via Language Guidance
Authors: Guangyu Shen; Zhihua Li; Xiang Xu; Tianchen Zhao; Zheng Zhang; Dongsheng An; Zhuowen Tu; Yifan Xing; Qin Zhang
Venue: WACV 2026 (main)

Abstract: Existing deepfake detection techniques struggle to keep-up with the ever-evolving novel, unseen forgeries methods. This limitation stems from their reliance on statistical artifacts learned during training, which are often tied to specific generation processes that may not be representative of samples from new, unseen deepfake generation methods encountered at test time. We propose that incorporating language guidance can improve deepfake detection generalization by integrating human-like commonsense reasoning -- such as recognizing logical inconsistencies and perceptual anomalies -- alongside statistical cues. To achieve this, we train an expert deepfake vision encoder by combining discriminative classification with image-text contrastive learning, where the text is generated by generalist MLLMs using few-shot prompting. This allows the encoder to extract both language-describable, commonsense deepfake artifacts and statistical forgery artifacts from pixel-level distributions. To further enhance robustness, we integrate data uncertainty learning into vision-language contrastive learning, mitigating noise in image-text supervision. Our expert vision encoder seamlessly interfaces with an LLM, further enabling more generalized and interpretable deepfake detection while also boosting accuracy. The resulting framework, AuthGuard, achieves state-of-the-art deepfake detection accuracy in both in-distribution and out-of-distribution settings, achieving AUC gains of 6.15% on the DFDC dataset and 16.68% on the DF40 dataset. Additionally, AuthGuard significantly enhances deepfake reasoning, improving performance by 24.69% on the DDVQA dataset.
Improving Out-of-Distribution Detection Using Segmented Images and Cross-View Attention Fusion
Authors: Alexander Politowicz; Sahisnu Mazumder; Bing Liu
Venue: WACV 2026 (main)

Abstract: Although out-of-distribution (OOD) detection has been extensively studied, it continues to face challenges in handling OOD data semantically similar to In-Distribution (ID) data. Part of the difficulty arises from the model's inability to learn superior ID class discriminative features. We propose to improve this by segmenting input images into foreground and background views and combining them with the original input image (original view) in a multi-view learning approach. We present a novel method, called CASOD (Cross-view Attention of Segmented views for OOD Detection), that learns better discriminative information from all three views and subsequently, fuses them through a novel stacked cross-view attention mechanism to produce the final predictive feature representation. A feature-based method is then applied to the final fused feature for OOD detection, giving major improvements over a range of strong baselines on various near- and far-OOD datasets. CASOD achieves state-of-the-art performance in various experimental settings with challenging ID and OOD datasets. Code for CASOD can be found at https://github.com/alexp205/CASOD.
VIZOR: Viewpoint-Invariant Zero-Shot Scene Graph Generation for 3D Scene Reasoning
Authors: Vivek Madhavaram; Vartika Sengar; Arkadipta De; Charu Sharma
Venue: WACV 2026 (main)

Abstract: Scene understanding and reasoning has been a fundamental problem in 3D computer vision, requiring models to identify objects, their properties, and spatial or comparative relationships among the objects. Existing approaches enable this by creating scene graphs using multiple inputs such as 2D images, depth maps, object labels, and annotated relationships from specific reference view. However, these methods often struggle with generalization and produce inaccurate spatial relationships like "left/right", which become inconsistent across different viewpoints. To address these limitations, we propose Viewpoint-Invariant ZerO-shot scene graph generation for 3D scene Reasoning (VIZOR). VIZOR is a training-free, end-to-end framework that constructs dense, viewpoint-invariant 3D scene graphs directly from raw 3D scenes. The generated scene graph is unambiguous, as spatial relationships are defined relative to each object's front-facing direction, making them consistent regardless of the reference view. Furthermore, it infers open-vocabulary relationships that describe spatial and proximity relationships among scene objects without requiring annotated training data. We conduct extensive quantitative and qualitative evaluations to assess the effectiveness of VIZOR on scene graph generation and downstream tasks, such as query-based object grounding. VIZOR outperforms state-of-the-art methods, showing clear improvements in scene graph generation and achieving 22% and 4.81% gains in zero-shot grounding accuracy on the Replica and Nr3D datasets, respectively.
Understanding Generative AI Capabilities in Everyday Image Editing Tasks
Authors: Brandon Collins; Mohammad Reza Taesiri; Logan Bolton; Viet Dac Lai; Franck Dernoncourt; Trung Bui; Anh Totti Nguyen
Venue: WACV 2026 (main)

Abstract: Generative AI (GenAI) holds significant promise for automating everyday image editing tasks, especially following the recent release of GPT-4o on March 25, 2025. However, what subjects do people most often want edited? What kinds of editing actions do they want to perform (e.g., removing or stylizing the subject)? Do people prefer precise edits with predictable outcomes or highly creative ones? By understanding the characteristics of real-world requests and the corresponding edits made by freelance photo-editing wizards, can we draw lessons for improving AI-based editors and determine which types of requests can currently be handled successfully by AI editors? In this paper, we present a unique study addressing these questions by analyzing 83k requests from the past 12 years (2013-2025) on the Reddit community, which collected 305k PSR-wizard edits. According to human ratings, approximately only 33% of requests can be fulfilled by the best AI editors (including GPT-4o, Gemini-2.0-Flash, SeedEdit). Interestingly, AI editors perform worse on low-creativity requests that require precise editing than on more open-ended tasks. They often struggle to preserve the identity of people and animals, and they frequently make non-requested touch-ups. On the other side of the table, VLM judges (e.g., o1) perform differently from human judges and may prefer AI edits more than human edits.
Gaussian Splatting Map Registration with Orthographic Bird's-Eye-View Renderings
Authors: H. Leblond; G. Simon; R. Martins; C. Demonceaux; M.-O. Berger
Venue: WACV 2026 (main)

Abstract: Gaussian Splatting (GS) are promising scene representations for visual localization and SLAM. Recent works have explored loop closure detection via Gaussian registration, improving map consistency and accuracy. However, achieving reliable registration given two GS representations from different acquisitions remains challenging.In this paper, we propose a complete pipeline to perform the matching and registration given two GS maps. The proposed method is grounded in generating orthographic bird's-eye views (BEVs) of optimized Gaussian models. The proposed approach leverages photometric and geometric information extracted directly from the GS to provide a trade-off of accuracy and invariance to different viewing changes (e.g., as type of GS maps, seasons or illumination). Unlike 3D registration methods, which become inefficient as the number of Gaussians grows, our approach leverages 2D orthographic renders thus considerably reducing the registration complexity. Experiments on two public datasets demonstrate that our method achieves higher accuracy than several existing baselines, while also maintaining better registration results when dealing with GS maps learned by different techniques (e.g., 3DGS to LightGaussian), or GS maps presenting viewing changes such as varying illumination conditions. Code and evaluation setup will be made publicly available.
IPCD: Intrinsic Point-Cloud Decomposition
Authors: Shogo Sato; Takuhiro Kaneko; Shoichiro Takeda; Tomoyasu Shimada; Kazuhiko Murasaki; Taiga Yoshida; Ryuichi Tanida; Akisato Kimura
Venue: WACV 2026 (main)

Abstract: Point clouds are widely used in various fields, including augmented reality (AR) and robotics, where relighting and texture editing are crucial for realistic visualization. Achieving these tasks requires accurately separating albedo from shade. However, performing this separation on point clouds presents two key challenges: (1) the non-grid structure of point clouds makes conventional image-based decomposition models ineffective, and (2) point-cloud models designed for other tasks do not explicitly consider global-light direction, resulting in inaccurate shade. In this paper, we introduce Intrinsic Point-Cloud Decomposition (IPCD), which extends image decomposition to the direct decomposition of colored point clouds into albedo and shade. To overcome challenge (1), we propose IPCD-Net that extends image-based model with point-wise feature aggregation for non-grid data processing. For challenge (2), we introduce Projection-based Luminance Distribution (PLD) with a hierarchical feature refinement, capturing global-light ques via multi-view projection. For comprehensive evaluation, we create a synthetic outdoor-scene dataset. Experimental results demonstrate that IPCD-Net reduces cast shadows in albedo and enhances color accuracy in shade. Furthermore, we showcase its applications in texture editing, relighting, and point-cloud registration under varying illumination. Finally, we verify the real-world applicability of IPCD-Net.
Diffusion Noise Optimization for Synthetic VLM Training
Authors: Ren Ohkubo; Rintaro Yanagi; Hirokatsu Kataoka; Yutaka Satoh
Venue: WACV 2026 (main)

Abstract: Recent advances in image generation models have enabled the production of high-quality images, making synthetic images a promising alternative to real images for dataset construction. However, a critical challenge remains in that the performance of Vision-Language Models (VLMs) tends to degrade as the proportion of synthetic images in a dataset increases in conventional approaches. To alleviate the challenge, we introduce a plug-and-play dataset construction framework that enhances text-to-image diffusion models by optimizing their initial noise. Our method treats the initial noise as a learnable parameter and iteratively updates it to maximize text-image alignment based on multiple embedding models without retraining the generator. Since the initial noise plays a crucial role in determining the quality of the synthetic image, its optimization enables the search for initial conditions that yield semantically faithful and realistic images. By improving FID and text-image alignment compared to conventional latent diffusion model (LDM)-based methods, our approach produces synthetic images better suited for training. When CLIP models were trained on such images, they achieved up to +5.09% higher Average R@1 in zero-shot retrieval, +2.88% higher Average top-1 accuracy in zero-shot classification, and +5.05% higher performance in linear-probing. These results demonstrate that initial noise optimization is an effective and scalable strategy for enabling robust VLM training with synthetic images.
DCSHARP: 3D Gaussian Splatting with Direction Cosine Spherical Harmonics and Shape-Aware Pruning
Authors: Ahmed Hasssan; Jian Meng; Yuanbo Xiangli; Jae-sun Seo
Venue: WACV 2026 (main)

Abstract: 3D Gaussian Splatting (3DGS) shows outstanding rendering quality for novel view synthesis. Despite its performance, the massive amount of Gaussian blobs leads to expensive run-time sorting and irregular memory access during rendering. Although 3DGS-based pruning algorithm has been widely explored, most of the current research has mainly focused on designing a proper pruning metric and the root cause behind the inevitable quality degradation remains underexplored for highly-sparse 3DGS. In particular, our investigation shows that the Spherical Harmonics (SH) of 3DGS is insufficient to capture high-frequency anisotropic reflections and specular highlights during rendering, especially with sparsified Gaussians. Motivated by that, this work proposes Direction Cosine Spherical Harmonics with Shape-Aware Pruning (DCSHARP). Specifically, the proposed Direction Cosine Spherical Harmonics (DCSH) replaces the vanilla spherical harmonics by facilitating the expressiveness of 3DGS on high-frequency and highly reflective scenes. Unlike recent works that rely on trainable masks or pseudo-rendering scores, the proposed Shape-aware Pruning method enables "pruning on-the-fly" while achieving high quality rendering. As a combined scheme, the proposed DCSHARP reduces the number of active Gaussians by up to 3.9x and improves rendering throughput by 1.9x with ZERO quality degradation compared to the vanilla 3DGS. Furthermore, the proposed DCSH scheme outperforms the vanilla 3DGS on all the mainstream benchmarks by simply replacing the vanilla SH with the DCSH. The source code of the proposed method will be open-sourced.
Distilling Diversity and Control in Diffusion Models
Authors: Rohit Gandikota; David Bau
Venue: WACV 2026 (main)

Abstract: Distilled diffusion models generate images in far fewer timesteps but suffer from reduced sample diversity when generating multiple outputs from the same prompt. To understand this phenomenon, we first investigate whether distillation damages concept representations by examining if the required diversity is properly learned. Surprisingly, distilled models retain the base model's representational structure: control mechanisms like Concept Sliders and LoRAs transfer seamlessly without retraining, and SliderSpace analysis reveals distilled models possess variational directions needed for diversity yet fail to activate them. This redirects our investigation to understanding how the generation dynamics differ between base and distilled models. Using x0 trajectory visualization, we discover distilled models commit to their final image structure almost immediately at the first timestep, while base models distribute structural decisions across many steps. To test whether this first-step commitment causes the diversity loss, we introduce diversity distillation, a hybrid approach using the base model for only the first critical timestep before switching to the distilled model. This single intervention restores sample diversity while maintaining computational efficiency. We provide both causal validation and theoretical support showing why the very first timestep concentrates the diversity bottleneck in distilled models. Our code and data are available at https://distillation.baulab.info/
KFS-Bench: Comprehensive Evaluation of Key Frame Sampling in Long Video Understanding
Authors: Zongyao Li; Kengo Ishida; Satoshi Yamazaki; Xiaotong Ji; Jianquan Liu
Venue: WACV 2026 (main)

Abstract: We propose KFS-Bench, the first benchmark for key frame sampling in long video question answering (QA), featuring multi-scene annotations to enable direct and robust evaluation of sampling strategies. Key frame sampling is crucial for efficient long-form video understanding. In long video QA, selecting informative frames enables multimodal large language models (MLLMs) to improve both accuracy and efficiency. KFS-Bench addresses the limitation of prior works that only indirectly assess frame selection quality via QA accuracy. By providing ground-truth annotations of multiple disjoint scenes required per question, KFS-Bench allows us to directly analyze how different sampling approaches capture essential content across an entire long video. Using KFS-Bench, we conduct a comprehensive study of key frame sampling methods and identify that not only sampling precision but also scene coverage and sampling balance are the key factors influencing QA performance. Regarding all the factors, we design a novel sampling quality metric that correlates with QA accuracy. Furthermore, we develop a novel key frame sampling method that leverages question-video relevance to balance sampling diversity against question-frame similarity, thereby improving coverage of relevant scenes. Our adaptively balanced sampling approach achieves superior performance in both key frame sampling and QA performance. The benchmark is available at https://github.com/NEC-VID/KFS-Bench.
TacticalCalib: End-to-End 6-DoF Camera Pose Regression for Tactical Camera Calibration
Authors: Liang Fan; Xiaoqian Liu; Zhi Chen; Lingkai Yang
Venue: WACV 2026 (main)

Abstract: Sports field calibration is critical for mapping image coordinates to standardized coordinates, enabling precise analysis of player trajectories and tactical formations. However, traditional methods designed for TV broadcast footage rely on sparse field features (corners, lines) that are susceptible to occlusion and viewpoint variations, limiting their effectiveness for tactical camera calibration. To address these limitations, we propose a novel pose-based calibration framework that directly regresses the 6-DoF camera pose from tactical view images. The proposed framework consists of three novel components: (1) a Lanczos-based spatial encoding module that preserves fine-grained geometric structures in the field representation, (2) an offset-based subpixel localization strategy that enhances occlusion robustness by refining keypoints' position to sub-pixel accuracy, and (3) a query-driven pose regression head with self-attention mechanisms that directly estimates camera pose without requiring additional calibration metadata. Extensive experiments on the SoccerNet-2023 and World Cup 2014 benchmarks demonstrate that our method achieves state-of-the-art performance in terms of Jaccard Index at threshold t = 5 (JaC@t), establishing superior accuracy and cross-dataset generalization capabilities.
FlowCLAS: Enhancing Normalizing Flow-Based Anomaly Segmentation Via Contrastive Learning
Authors: Chang Won Lee; Selina Leveugle; Paul Grouchy; Chris Langley; Svetlana Stolpner; Jonathan Kelly; Steven L. Waslander
Venue: WACV 2026 (main)

Abstract: Anomaly segmentation is a critical capability for safety-critical robotics applications that must be aware of unexpected events. Normalizing flows (NFs), a class of generative models, are a promising approach for this task due to their ability to model the inlier data distribution efficiently. However, their performance falters in dynamic scenes, where complex, multi-modal data distributions cause them to struggle with out-of-distribution samples, leaving a performance gap to leading discriminative methods.To address this limitation, we introduce FlowCLAS, a hybrid framework that enhances the traditional maximum likelihood objective of NFs with a discriminative, contrastive loss. Leveraging Outlier Exposure, this objective explicitly enforces a separation between normal and anomalous features in the latent space, retaining the probabilistic foundation of NFs while embedding the discriminative power they lack.The strength of this approach is demonstrated by FlowCLAS establishing new state-of-the-art (SOTA) performance across multiple challenging anomaly segmentation benchmarks for robotics, including Fishyscapes Lost & Found, Road Anomaly, SegmentMeIfYouCan-ObstacleTrack, and ALLO. Our experiments also show that this contrastive approach is more effective than other outlier-based training strategies for NFs, successfully bridging the performance gap to leading discriminative methods.
SuperRivolution: Fine-Scale Rivers from Coarse Temporal Satellite Imagery
Authors: Rangel Daroya; Subhransu Maji
Venue: WACV 2026 (main)

Abstract: Satellite missions provide valuable optical data for monitoring rivers at diverse spatial and temporal scales. However, accessibility remains a challenge: high-resolution imagery is ideal for fine-grained monitoring but is typically scarce and expensive compared to low-resolution imagery. To address this gap, we introduce SuperRivolution, a framework that improves river segmentation resolution by leveraging information from time series of low-resolution satellite images. We contribute a new benchmark dataset of 9,810 low-resolution temporal images paired with high-resolution labels from an existing river monitoring dataset. Using this benchmark, we investigate multiple strategies for river segmentation, including ensembling single-image models, applying image super-resolution, and developing end-to-end models trained on temporal sequences. SuperRivolution significantly outperforms single-image methods and baseline temporal approaches, narrowing the gap with supervised high-resolution models. For example, the F1 score for river segmentation improves from 60.9% to 80.5%, while the state-of-the-art model operating on high-resolution images achieves 94.1%. Similar improvements are also observed in river width estimation tasks. Our results highlight the potential of publicly available low-resolution satellite archives for fine-scale river monitoring.
Network-agnostic Distortion-robust Projections for Wide-angle Image Understanding
Authors: Akshaya Athwale; Ola Ahmad; Jean-Francois Lalonde
Venue: WACV 2026 (main)

Abstract: Due to their increased field of view, wide-angle lenses are increasingly used in applications such as VR, security, or in autonomous driving. Typically, existing models either ignore wide-angle distortions or "undistort" images to a perspective projection, often resulting in severe stretching. More recent distortion-aware architectures address these issues, yet they impose substantial computational burdens and limit the use of powerful pre-trained vision backbones. In this work, we revisit the undistortion strategy by exploring alternative projection functions beyond the conventional perspective model.Specifically, we investigate square-to-disc mapping functions, most notably, the elliptical grid map (EGM) projection, which minimizes stretching. We show how EGM projection can be combined with the known lens distortion curves to achieve distortion invariance directly in image space. This network-agnostic approach seamlessly integrates with existing deep learning architectures, allowing fine-tuning on pre-trained models trained on large perspective datasets, while adapting to both seen and unseen wide-angle lenses without re-training each time the lens changes during evaluation. We perform experiments on the semantic segmentation task, comparing methods on zero-shot adaptation to unseen lenses from different wide-angle lenses. Our extensive experiments show that using the EGM projection with existing segmentation models significantly outperforms baselines when trained on bounded distortion levels and tested across both seen and out-of-distribution distortions. Furthermore, EGM projection achieves improved performance on real-world datasets, highlighting the robustness and practicality of our approach in real-world applications.
Interleaved Vision-and-Language Generation via Generative Voken
Authors: Kaizhi Zheng; Xuehai He; Xin Eric Wang
Venue: WACV 2026 (main)

Abstract: The effectiveness of Multimodal Large Language Models (MLLMs) demonstrates a profound capability in multimodal understanding. However, the simultaneous generation of images with coherent texts is still underdeveloped. Addressing this, we introduce a novel interleaved vision-and-language generation method, centered around the concept of "generative vokens". These vokens serve as pivotal elements contributing to coherent image-text outputs. Our method is marked by a unique two-stage training strategy for description-free multimodal generation, which does not necessitate extensive descriptions of images. We integrate classifier-free guidance to enhance the alignment of generated images and texts, ensuring more seamless and contextually relevant multimodal interactions. Our model, MiniGPT-5, exhibits substantial improvement over the baseline models on multimodal generation datasets, including MMDialog and VIST. The human evaluation shows MiniGPT-5 is better than the baseline model on more than 56% of cases for multimodal generation, highlighting its efficacy across diverse benchmarks.