GenAI4ELab/papercli-papers huggingface dataset:
mmWEAVER: Environment-Specific mmWave Signal Synthesis from a Photo and Activity Description
Authors: Mahathir Monjur; Shahriar Nirjon
Venue: WACV 2026 (main)

Abstract: Realistic signal generation and dataset augmentation are essential for advancing mmWave radar applications such as activity recognition and pose estimation, which rely heavily on diverse and environment-specific signal datasets. However, mmWave signals are inherently complex, sparse, and high-dimensional, making physical simulation computationally expensive. This paper presents mmWeaver, a novel framework that synthesizes realistic, environment-specific complex mmWave signals by modeling them as continuous functions using implicit neural representations (INRs), achieving up to 49-fold compression. mmWeaver incorporates hypernetworks that dynamically generate INR parameters based on environmental context (extracted from RGB-D images) and human motion features (derived from text-to-pose generation via MotionGPT), enabling efficient and adaptive signal synthesis. By conditioning on these semantic and geometric priors, mmWeaver generates diverse in-phase and quadrature (I/Q) signals at multiple resolutions, preserving information critical for downstream tasks such as point cloud estimation and activity classification. Extensive experiments show that mmWeaver achieves a complex structural similarity index (SSIM) of 0.88 and a peak signal-to-noise ratio (PSNR) of 35 dB, outperforming existing methods in signal realism while improving activity recognition accuracy by up to 7% and reducing human pose estimation error by up to 15%, all while operating 6-35 times faster than simulation-based approaches.
Optimizing LVLMs with On-Policy Data for Effective Hallucination Mitigation
Authors: Chengzhi Yu; Yifan Xu; Yifan Chen; Wenyi Zhang
Venue: WACV 2026 (main)

Abstract: Recently, large vision-language models (LVLMs) have risen to be a promising approach for multimodal tasks. However, principled hallucination mitigation remains a critical challenge. In this work, we first analyze the data generation process in LVLM hallucination mitigation and affirm that on-policy data significantly outperforms off-policy data, which thus calls for efficient and reliable preference annotation of on-policy data. We then point out that, existing annotation methods introduce additional hallucination in training samples, which may enhance the model's hallucination patterns, to address this problem, we propose training a hallucination classifier giving binary annotations, which guarantee clean chosen samples for the subsequent alignment. To further harness of the power of on-policy data, we design a robust iterative direct preference optimization (DPO) algorithm adopting a dynamic sample reweighting scheme. We conduct comprehensive experiments on three benchmarks with comparison to 8 state-of-the-art baselines. In particular, our approach reduces the hallucination rate of LLaVA-1.5-7B on MMHalBench by 50.8% and the average hallucination rate on Object HalBench by 79.5%; more significantly, our method fully taps into the potential of open-source models, enabling LLaVA-1.5-13B to even surpass the performance of GPT-4V. Our code is available at https://github.com/MilchstraB/LVLM-OnPolicy.
Overcoming Fine-Grained Visual Challenges in Animal Re-Identification via Semantic Feature Alignment
Authors: Yihao Wu; Di Zhao; Yuzhuo Li; Matthew Alajas; Alistair S. Glen; Jingfeng Zhang; Gillian Dobbie; Daniel Wilson; Yun Sing Koh
Venue: WACV 2026 (main)

Abstract: Identifying individual animals at different points in space and time is vital for effective wildlife monitoring and biodiversity conservation. While existing computer vision methods have shown promise in re-identifying animals, their capability in Animal Re-Identification (Animal ReID) remains restricted by the inherent visual variations, specifically high intra- and low inter-identity variations. High intra-identity variations refer to high visual diversity within the same individual due to pose or form changes and occlusions, and low inter-identity variations refer to subtle visual differences between distinct individuals due to fine-grained appearances. To address these challenges, we propose the Clip-based Animal RE-identification (CARE) framework, which leverages the image-conditioned textual description generation and individual-level semantic feature alignment, mitigating the negative impacts of visual variations in Animal ReID. Crucially, we have packaged CARE into a stand-alone toolkit and piloted it with stakeholders, facilitating real-world wildlife monitoring for biodiversity conservation. Extensive experiments on benchmark and in-the-wild datasets further demonstrate that CARE consistently outperforms state-of-the-art methods, validating its effectiveness in Animal ReID. Explore more about CARE at https://ml4sg.auckland.ac.nz/animal-re-identification-model/.
Better Safe Than Sorry? Overreaction Problem of Vision Language Models in Visual Emergency Recognition
Authors: Dasol Choi; Seunghyun Lee; Youngsook Song
Venue: WACV 2026 (main)

Abstract: Vision-Language Models (VLMs) have shown capabilities in interpreting visual content, but their reliability in safety-critical scenarios remains insufficiently explored. We introduce VERI, a diagnostic benchmark comprising 200 synthetic images (100 contrastive pairs) and additional 50 real-world images (25 pairs) for validation. Each emergency scene is paired with a visually similar but safe counterpart through human verification. Using a two-stage evaluation protocol (risk identification and emergency response), we assess 17 VLMs across medical emergencies, accidents, and natural disasters. Our analysis reveals an "overreaction problem": models achieve high recall(70-100%) but suffer from low precision, misclassifying 31-96% of safe situations as dangerous. Seven safe scenarios were universally misclassified by all models. This "better-safe-than-sorry" bias stems from contextual overinterpretation (88-98% of errors). Both synthetic and real-world datasets confirm these systematic patterns, challenging VLM reliability in safety-critical applications. Addressing this requires enhanced contextual reasoning in ambiguous visual situations.
Narrating For You: Prompt-guided Audio-visual Narrating Face Generation Employing Multi-entangled Latent Space
Authors: Aashish Chandra K; Aashutosh A V; Abhijit Das
Venue: WACV 2026 (main)

Abstract: We present a novel approach for generating realistic speaking and talking faces by synthesizing a person's voice and facial movements from a static image, a voice profile, and a target text. The model encodes the prompt/driving text, the driving image, and the voice profile of an individual and then combines them to pass them to the multi-entangled latent space to foster key-value pairs and queries for the audio and video modality generation pipeline. The multi-entangled latent space is responsible for establishing the spatiotemporal person-specific features between the modalities. Further, entangled features are passed to the respective decoder of each modality for output audio and video generation. Our experiments and analysis through standard metrics demonstrate the effectiveness of our model. We also developed a domain-specific dataset for the problem. All model checkpoints, code, and the proposed dataset can be found at https://github.com/narratingForYou/NarratingForYou.
Semi-Supervised Hierarchical Open-Set Classification
Authors: Erik Wallin; Fredrik Kahl; Lars Hammarstrand
Venue: WACV 2026 (main)

Abstract: Hierarchical open-set classification handles previously unseen classes by assigning them to the most appropriate high-level category in a class taxonomy. We extend this paradigm to the semi-supervised setting, enabling the use of large-scale, uncurated datasets containing a mixture of known and unknown classes to improve the hierarchical open-set performance. To this end, we propose a teacher-student framework based on pseudo-labeling. Two key components are introduced: 1) subtree pseudo-labels, which provide reliable supervision in the presence of unknown data, and 2) age-gating, a mechanism that mitigates overconfidence in pseudo-labels. Experiments show that our framework outperforms self-supervised pretraining followed by supervised adaptation, and even matches the fully supervised counterpart when using only 20 labeled samples per class on the iNaturalist19 benchmark. Our code is available at https://github.com/walline/semihoc.
BrightRate: Quality Assessment for User-Generated HDR Videos
Authors: Shreshth Saini; Bowen Chen; Yilin Wang; Neil Birkbeck; Balu Adsumilli; Alan C. Bovik
Venue: WACV 2026 (main)

Abstract: High Dynamic Range (HDR) videos offer superior luminance and color fidelity as compared to Standard Dynamic Range (SDR) content. The rapid growth of User-Generated Content (UGC) on platforms such as YouTube, Instagram, and TikTok has brought a significant increase in the volumes of streamed and shared UGC videos. This newer category of videos brings new challenges to the development of effective No-Reference (NR) video quality assessment (VQA) models specialized to HDR UGC, because of the extreme variety and severities of distortions, arising from diverse capture, editing, and processing outcomes. Towards addressing this issue, we introduce BrightVQ, a sizeable new psychometric data resource. It is the first large-scale subjective video quality database dedicated to the quality modelling of HDR UGC videos. BrightVQ comprises 2,100 videos, on which we collected 73,794 perceptual quality ratings. Using this dataset, we also developed BrightRate, a novel video quality prediction model designed to capture both UGC-specific distortions coexisting with HDR-specific artifacts. Extensive experimental results demonstrate that BrightRate achieves state-of-the-art performance across HDR databases.
Guiding What Not to Generate: Automated Negative Prompting for Text-Image Alignment
Authors: Sangha Park; Eunji Kim; Yeongtak Oh; Jooyoung Choi; Sungroh Yoon
Venue: WACV 2026 (main)

Abstract: Despite substantial progress in text-to-image generation, achieving precise text-image alignment remains challenging, particularly for prompts with rich compositional structure or imaginative elements. To address this, we introduce Negative Prompting for Image Correction (NPC), an automated pipeline that improves alignment by identifying and applying negative prompts that suppress unintended content. We begin by analyzing cross-attention patterns to explain why both targeted negatives--those directly tied to the prompt's alignment error--and untargeted negatives--tokens unrelated to the prompt but present in the generated image--can enhance alignment. To discover useful negatives, NPC generates candidate prompts using a verifier-captioner-proposer framework and ranks them with a salient text-space score, enabling effective selection without requiring additional image synthesis. On GenEval++ and Imagine-Bench, NPC outperforms strong baselines, achieving 0.571 vs. 0.371 on GenEval++ and the best overall performance on Imagine-Bench. By guiding what not to generate, NPC provides a principled, fully automated route to stronger text-image alignment in diffusion models. Code is released at https://github.com/wiarae/NPC.
LASER: Lip Landmark Assisted Speaker Detection for Robustness
Authors: Le Thien Phuc Nguyen; Zhuoran Yu; Yong Jae Lee
Venue: WACV 2026 (main)

Abstract: Active Speaker Detection (ASD) aims to identify who is speaking in complex visual scenes. While humans naturally rely on lip-audio synchronization, existing ASD models often misclassify non-speaking instances when lip movements and audio are unsynchronized. To address this, we propose Lip landmark Assisted Speaker dEtection for Robustness (LASER), which explicitly incorporates lip landmarks during training to guide the model's attention to speech-relevant regions. Given a face track, LASER extracts visual features and encodes 2D lip landmarks into dense maps. To handle failure cases such as low resolution or occlusion, we introduce an auxiliary consistency loss that aligns lip-aware and face-only predictions, removing the need for landmark detectors at test time. LASER outperforms state-of-the-art models across both in-domain and out-of-domain benchmarks. To further evaluate robustness in realistic conditions, we introduce LASER-bench, a curated dataset of modern video clips with varying levels of background noise. On the high-noise subset, LASER improves mAP by 3.3 and 4.3 points over LoCoNet and TalkNet, respectively, demonstrating strong resilience to real-world acoustic challenges.
Spacewalk-18: A Benchmark for Multimodal and Long-form Procedural Video Understanding in Novel Domains
Authors: Zitian Tang; Rohan Myer Krishnan; Zhiqiu Yu; Chen Sun
Venue: WACV 2026 (main)

Abstract: Learning from (procedural) videos has increasingly served as a pathway for embodied agents to acquire skills from human demonstrations. To do this, video understanding models must be able to obtain structured understandings, such as the temporal segmentation of a demonstration into sequences of actions and skills, and to generalize the understandings to novel environments, tasks, and problem domains. In pursuit of this goal, we introduce Spacewalk-18, a benchmark containing two tasks: (1) step recognition and (2) video question answering, over a dataset of temporally segmented and labeled tasks in International Space Station spacewalk recordings. In tandem, the two tasks quantify a model's ability to: (1) generalize to novel domains; (2) utilize long temporal context and multimodal (e.g., visual and speech) information. Our extensive experimental analysis highlights the challenges of Spacewalk-18, but also suggests best practices for domain generalization and long-form understanding. Notably, we discover a promising adaptation via summarization technique that leads to significant performance improvement without model fine-tuning. The Spacewalk-18 benchmark is released at https://brown-palm.github.io/Spacewalk-18.
Extreme Amodal Face Detection
Authors: Changlin Song; Yunzhong Hou; Michael Randall Barnes; Rahul Shome; Dylan Campbell
Venue: WACV 2026 (main)

Abstract: Extreme amodal detection is the task of inferring the 2D location of objects that are not fully visible in the input image but are visible within an expanded field-of-view. This differs from amodal detection, where the object is partially visible within the input image, but is occluded. In this paper, we consider the sub-problem of face detection, since this class provides motivating applications involving safety and privacy, but do not tailor our method specifically to this class. Existing approaches rely on image sequences so that missing detections may be interpolated from surrounding frames or make use of generative models to sample possible completions. In contrast, we consider the single-image task and propose a more efficient, sample-free approach that makes use of the contextual cues from the image to infer the presence of unseen faces. We design a heatmap-based extreme amodal object detector that addresses the problem of efficiently predicting a lot (the out-of-frame region) from a little (the image) with a selective coarse-to-fine decoder. Our method establishes strong results for this new task, even outperforming less efficient generative approaches. Code, data, and models are available at https://charliesong1999.github.io/exaft_web/.
Event-based Graph Representation with Spatial and Motion Vectors for Asynchronous Object Detection
Authors: Aayush Atul Verma; Arpitsinh Vaghela; Bharatesh Chakravarthi; Kaustav Chanda; Yezhou Yang
Venue: WACV 2026 (main)

Abstract: Event-based sensors offer high temporal resolution and low latency by generating sparse, asynchronous data. However, converting this irregular data into dense tensors for use in standard neural networks diminishes these inherent advantages, motivating research into graph representations. While such methods preserve sparsity and support asynchronous inference, their performance on downstream tasks remains limited due to suboptimal modeling of spatiotemporal dynamics. In this work, we propose a novel spatiotemporal multigraph representation to better capture spatial structure and temporal changes. Our approach constructs two decoupled graphs: a spatial graph leveraging B-spline basis functions to model global structure, and a temporal graph utilizing motion vector-based attention for local dynamic changes. This design enables the use of efficient 2D kernels in place of computationally expensive 3D kernels. We evaluate our method on the Gen1 automotive and eTraM datasets for event-based object detection, achieving over a 6% improvement in detection accuracy compared to previous graph-based works, with a 5xspeedup, reduced parameter count, and no increase in computational cost. These results highlight the effectiveness of structured graph modeling for asynchronous vision. Project page: https://eventbasedvision.github.io/eGSMV.
PHYSPLAT: a Framework for Photorealistic Hybrid Simulation of Real and Synthetic Elements using 3D Gaussian Splatting
Authors: Mario Alfonso-Arsuaga; Henar Dominguez-Elvira; Jorge Casas-Guerrero; Andrea Castiella-Aguirrezabala; Lorenzo Costábile Domínguez; Jorge García-González; Maria Naranjo-Almeida; Marc Comino-Trinidad; Jorge Lopez-Moreno
Venue: WACV 2026 (main)

Abstract: We present an integrated, end-to-end system that enables photorealistic real-world objects--reconstructed using 3D Gaussian Splatting (3DGS)--to interact seamlessly with synthetic elements such as polygonal meshes, fluids, fabrics, and robotic systems within a unified simulation environment. By leveraging Material Point Method (MPM) simulation, our system ensures the compatibility of the 3DGS representation with established physics engines while remaining extensible.Our workflow begins by capturing real-world scenes "in the wild" using 3DGS, from which we derive a simplified, appearance-agnostic particle proxy suitable for physics simulation. These particles, along with synthetic primitives, are imported into the system, where the simulator computes positions and deformation gradients for all bodies--including 3DGS-derived particles--at each timestep.We validate our system through collision and deformation scenarios, and showcase a robotics application in which a manipulator plans and executes tasks involving both captured objects and synthetic elements. By selecting the most appropriate solver and constitutive model for each material--such as MPM for granular media and deformables, PBD for cloth, or SPH for fluids--our approach delivers: (i) high visual fidelity, (ii) accurate, material-specific physical behavior, and (iii) minimal performance overhead.Our pipeline streamlines scene preparation, offering a significant advantage over traditional mesh-centric photogrammetry for time-sensitive reconstruction and emergency scenarios. This combination of flexibility and realism makes our system well-suited for robot task planning, photorealistic multiview dataset generation for autonomous navigation, and other embodied AI applications.
VOCAL: Visual Odometry via ContrAstive Learning
Authors: Chi-Yao Huang; Zeel Bhatt; Yezhou Yang
Venue: WACV 2026 (main)

Abstract: Breakthroughs in visual odometry (VO) have fundamentally reshaped the landscape of robotics, enabling ultra-precise camera state estimation that is crucial for modern autonomous systems. Despite these advances, many learning-based VO techniques rely on rigid geometric assumptions, which often fall short in interpretability and lack a solid theoretical basis within fully data-driven frameworks. To overcome these limitations, we introduce VOCAL (Visual Odometry via ContrAstive Learning), a novel framework that reimagines VO as a label ranking challenge. By integrating Bayesian inference with a representation learning framework, VOCAL organizes visual features to mirror camera states. The ranking mechanism compels similar camera states to converge into consistent and spatially coherent representations within the latent space. This strategic alignment not only bolsters the interpretability of the learned features but also ensures compatibility with multimodal data sources. Extensive evaluations on the KITTI dataset highlight VOCAL's enhanced interpretability and flexibility, pushing VO toward more general and explainable spatial intelligence.
Multi-view Stereo with Multiple Projectors for Oneshot Entire Shape Scan based on Neural SDF and DSSS Demultiplexing
Authors: Kota Nishihara; Ryo Furukawa; Ryusuke Sagawa; Hiroshi Kawasaki
Venue: WACV 2026 (main)

Abstract: 3D reconstruction has been widely studied and applied in various fields. Multi-view stereo (MVS) methods can recover dense geometry from multiple views but often fail for texture-less objects due to unreliable feature matching. Active stereo with structured light (SL) addresses this limitation, however, when using multiple cameras and projectors for entire shape acquisition, overlapped SL patterns interfere with one another, leading to decoding failures.We propose a novel MVS framework based on neural signed distance fields (Neural SDF) with multiple projectors that employs Direct Sequence Spread Spectrum (DSSS) to separate multiplexed patterns. This approach enables robust and accurate 3D shape reconstruction through Neural SDF optimization with a photometric loss that accounts for both the positions and the patterns of the projectors. We built real scanning devices, and experiments on several objects demonstrated the effectiveness of the proposed method.
Self-Supervised Visual Prompting for Cross-Domain Road Damage Detection
Authors: Xi Xiao; Zhuxuanzi Wang; Mingqiao Mo; Chen Liu; Chenrui Ma; Yanshu Li; Smita Krishnaswamy; Xiao Wang; Tianyang Wang
Venue: WACV 2026 (main)

Abstract: The deployment of automated pavement defect detection is often hindered by poor cross-domain generalization. Supervised detectors achieve strong in-domain accuracy but require costly re-annotation for new environments, while standard self-supervised methods capture generic features and remain vulnerable to domain shift. We propose \ours, a self-supervised framework that visually probes target domains without labels. \ours introduces a Self-supervised Prompt Enhancement Module (SPEM), which derives defect-aware prompts from unlabeled target data to guide a frozen ViT backbone, and a Domain-Aware Prompt Alignment (DAPA) objective, which aligns prompt-conditioned source and target representations. Experiments on four challenging benchmarks show that \ours consistently outperforms strong supervised, self-supervised, and adaptation baselines, achieving robust zero-shot transfer, improved resilience to domain variations, and high data efficiency in few-shot adaptation. These results highlight self-supervised prompting as a practical direction for building scalable and adaptive visual inspection systems. Source code is publicly available: https://github.com/xixiaouab/PROBE/tree/main