GenAI4ELab/papercli-papers huggingface dataset:
Roadside Monocular 3D Detection Prompted by 2D Detection
Authors: Yechi Ma; Wei Hua; Yanan Li; Shu Kong
Venue: WACV 2026 (main)

Abstract: Roadside monocular 3D detection requires detecting objects of predefined classes in an RGB frame and predicting their 3D attributes, such as bird's-eye-view (BEV) locations. It has broad applications in traffic control, vehicle-vehicle communication, and vehicle-infrastructure cooperative perception. To address this task, we introduce Promptable 3D Detector (Pro3D), a novel detector design that leverages 2D detections as prompts. We build our Pro3D upon two key insights. First, compared to a typical 3D detector, a 2D detector is "easier" to train due to fewer loss terms and performs significantly better at localizing objects w.r.t 2D metrics. Second, once 2D detections precisely locate objects in the image, a 3D detector can focus on lifting these detections into 3D BEV, especially when fixed camera pose or scene geometry provide an informative prior. To encode and incorporate 2D detections, we explore three methods: (a) concatenating features from both 2D and 3D detectors, (b) attentively fusing 2D and 3D detector features, and (c) encoding properties of predicted 2D bounding boxes  x, y, width, height, label  and attentively fusing them with the 3D detector feature. Interestingly, the third method significantly outperforms the others, underscoring the effectiveness of 2D detections as prompts that offer precise object targets and allow the 3D detector to focus on lifting them into 3D. Pro3D is adaptable for use with a wide range of 2D and 3D detectors with minimal modifications. Comprehensive experiments demonstrate that our Pro3D significantly enhances existing methods, achieving state-of-the-art results on two contemporary benchmarks.
Divide and Refine: Enhancing Multimodal Representation and Explainability for Emotion Recognition in Conversation
Authors: Anh-Tuan Mai; Cam-Van Thi Nguyen; Duc-Trong Le
Venue: WACV 2026 (main)

Abstract: Multimodal emotion recognition in conversation (MERC) requires representations that effectively integrate signals from multiple modalities. These signals include modality-specific cues, information shared across modalities, and interactions that emerge only when modalities are combined. In information-theoretic terms, these correspond to unique, redundant, and synergistic contributions. An ideal representation should leverage all three, yet achieving such balance remains challenging. Recent advances in contrastive learning and augmentation-based methods have made progress, but they often overlook the role of data preparation in preserving these components. In particular, applying augmentations directly to raw inputs or fused embeddings can blur the boundaries between modality-unique and cross-modal signals. To address this challenge, we propose a two-phase framework Divide and Refine (DnR). In the Divide phase, each modality is explicitly decomposed into uniqueness, pairwise redundancy, and synergy. In the Refine phase, tailored objectives enhance the informativeness of these components while maintaining their distinct roles. The refined representations are plug-and-play compatible with diverse multimodal pipelines. Extensive experiments on IEMOCAP and MELD demonstrate consistent improvements across multiple MERC backbones. These results highlight the effectiveness of explicitly dividing, refining, and recombining multimodal representations as a principled strategy for advancing emotion recognition.
End-to-End Fine-Tuning of 3D Texture Generation using Differentiable Rewards
Authors: AmirHossein Zamani; Tianhao Xie; Amir G. Aghdam; Tiberiu Popa; Eugene Belilovsky
Venue: WACV 2026 (main)

Abstract: While recent 3D generative models can produce high-quality texture images, they often fail to capture human preferences or meet task-specific requirements. Moreover, a core challenge in the 3D texture generation domain is that most existing approaches rely on repeated calls to 2D text-to-image generative models, which lack an inherent understanding of the 3D structure of the input 3D mesh object. To alleviate these issues, we propose an end-to-end differentiable, reinforcement-learning-free framework that embeds human feedback, expressed as differentiable reward functions, directly into the 3D texture synthesis pipeline. By back-propagating preference signals through both geometric and appearance modules of the proposed framework, our method generates textures that respect the 3D geometry structure and align with desired criteria. To demonstrate its versatility, we introduce three novel geometry-aware reward functions, which offer a more controllable and interpretable pathway for creating high-quality 3D content from natural language. By conducting qualitative, quantitative, and user-preference evaluations against state-of-the-art methods, we demonstrate that our proposed strategy consistently outperforms existing approaches. Our implementation code is publicly available at: https://github.com/AHHHZ975/Differentiable-Texture-Learning
ImageNet-sES: A First Systematic Study of Sensor-Environment Simulation Anchored by Real Recaptures
Authors: Ji-yoon Kim; Eunsu Baek; Hyung-Sin Kim
Venue: WACV 2026 (main)

Abstract: Variations in environment and sensor (ES) conditions--lighting, ISO, shutter, and aperture--cause domain shifts that degrade visual recognition. While a recent robustness benchmark, ImageNet-ES, shows that these shifts differ from conventional augmentations, collecting physically recaptured data is costly and hard to scale. We present CycleGAN-ES, a per-condition unpaired translation framework that simulates ES-style variations from a small set of real targets. Trained on Tiny-ImageNet and ImageNet-ES domain pairs with as few as 200 images per target domain and minimal tuning, CycleGAN-ES produces a synthetic counterpart, ImageNet-sES (IN-sES). The generated images exhibit high-fidelity ES effects both qualitatively and quantitatively, reproducing characteristic exposure and noise behaviors (e.g., highlight clipping at long exposure and increased high-ISO noise). In benchmark evaluations, augmenting training with ImageNet-sES improves robustness to ES shifts on ImageNet-ES, achieves complementary gains when combined with standard augmentation strategies, and transfers to other corruption domains such as ImageNet-C. The learned translators further transfer to new datsets (e.g., CIFAR-100) without retraining. To the best of our knowledge, this is the first systematic study of ES simulation anchored to real recaptures at ImageNet scale. Our results establish ES simulation as a scalable, practical route to incorporating ES-driven style diversity into training pipelines and lay the groundwork for broader real-world robustness evaluation.
LVM-Lite: Training Large Vision Models with Efficient Sequential Modeling
Authors: Xianhang Li; Hongru Zhu; Sucheng Ren; Linjie Yang; Peng Wang; Heng Wang; Xiaohui Shen; Qing Liu; Cihang Xie
Venue: WACV 2026 (main)

Abstract: Generative pre-training has significantly advanced natural language understanding. Building upon this success, recent research begins to innovate Large Vision Models (LVM) by leveraging large-scale pre-training on visual sequences, where simultaneous consideration of image token sequences within single images and across a set of images is of key importance. This paper shows that sequential modeling on single images and across multiple images can be efficiently and effectively decoupled. We introduce a two-stage learning pipeline, starting with single-image pre-training, followed by fine-tuning on long image/video sequences. We term this method Large Vision Model Lite (LVM-Lite). Extensive experiments showcase the impressive performance of LVM-Lite across various generative and discriminative benchmarks, comparable to specifically trained models without the need for task-specific training. Importantly, LVM-Lite accelerates training speed substantially up to 2.7xand demonstrates strong scalability.
MEDAL: multi-modal MEta-space Distillation and ALignment for Visual Compatibility Learning
Authors: Dween Rabius Sanny; Vinay Kumar Verma; Prateek Sircar; Deepak Gupta
Venue: WACV 2026 (main)

Abstract: Visual compatibility recommendation systems aim to surface compatible items (e.g. pants, shoes) that harmonise with a user-selected product (e.g., shirt). Existing methods struggle in three key aspects: they rely on global CNN representations that overlook fine-grained local cues critical for visual pairing; they force all categories into a single latent space, ignoring the fact that compatibility rules differ across product-type pairs; and they demand costly, expert-annotated outfit labels. We introduce MEDAL(Meta-space Distillation and Alignment ), a self-supervised framework that addresses all three challenges simultaneously. MEDAL (i) employs a local-global augmentation curriculum inside a teacher-student ViT to emphasise patch-level texture and pattern similarities while suppressing confounding global shape cues; (ii) partitions the joint feature manifold into learnable, pair-specific meta-spaces so that, for example, (shirt,pants) and (pants,shoes) relationships are modelled with distinct projection masks; and (iii) replaces manual labels with distantly supervised KD, harvesting pseudo-compatible sets via object detection on web images, thus scaling to millions of real-world examples. We further fuse perceptually uniform LUV colour histograms to capture global colour harmony often missed by pure vision transformers. Extensive experiments on Polyvore disjoint/non-disjoint and a 2M-image in-house dataset show state-of-the-art gains of up to +3.72/+2.7FITB and +9.58R@10 over the strongest baseline, whilst cutting annotation cost to zero. Qualitative studies confirm that MEDAL retrieves stylistically coherent outfits and correctly penalises mismatched colour palettes.
Rank-based Geographical Regularization: Revisiting Contrastive Self-Supervised Learning for Multispectral Remote Sensing Imagery
Authors: Tom Burgert; Leonard Hackel; Paolo Rota; Begüm Demir
Venue: WACV 2026 (main)

Abstract: Self-supervised learning (SSL) has become a powerful paradigm for learning from large, unlabeled datasets, particularly in computer vision (CV). However, applying SSL to multispectral remote sensing (RS) images presents unique challenges and opportunities due to the geographical and temporal variability of the data. In this paper, we introduce GeoRank, a novel regularization method for contrastive SSL that improves upon prior techniques by directly optimizing spherical distances to embed geographical relationships into the learned feature space. GeoRank outperforms or matches prior methods that integrate geographical metadata and consistently improves diverse contrastive SSL algorithms (e.g., BYOL, DINO). Beyond this, we present a systematic investigation of key adaptations of contrastive SSL for multispectral RS images, including the effectiveness of data augmentations, the impact of dataset cardinality and image size on performance, and the task dependency of temporal views. Code is available at https://github.com/tomburgert/georank.
LiDAR-DHMT: LiDAR-Adaptive Dual Hierarchical Mask Transformer for Robust Freespace Detection and Semantic Segmentation
Authors: Siyu Chen; Ting Han; Changshe Zhang; Xin Luo; Huan Chen; Meiliu Wu; Guorong Cai; Jinhe Su
Venue: WACV 2026 (main)

Abstract: Inaccurate freespace detection remains a significant challenge to the safety of autonomous driving. However, we observe that current multisource fusion approaches rely on converting LiDAR point clouds into depth maps, often lose crucial 3D geometric cues. This compromises the spatial consistency of predictions, especially in complex urban scenes. To address this limitation, we propose LiDAR-DHMT (LiDAR-Adaptive Dual-branch Hierarchical Mask Transformer), a novel framework designed for spatial-consistent freespace detection and semantic segmentation. Our key innovation lies in the introduction of a 3D Relative Position Bias module, which effectively captures LiDAR's inherent spatial priors. This is coupled with a Dynamic Bias Attention mechanism that adaptively incorporates the 3D positional cues into the Transformer's attention computation, enhancing spatial coherence. Additionally, we employ a Mask Interaction module and a global-local fusion strategy to jointly model contextual semantics and fine-grained structural details. Extensive experiments conducted on the KITTI Road, KITTI-360, Cityscapes datasets demonstrate that LiDAR-DHMT consistently outperforms existing state-of-the-art methods, achieving a competitive 97.59% F1 score in freespace detection and 69.45% and 84.4% mIoU in semantic segmentation. Our findings suggest that LiDAR-DHMT offers a practical solution for deploying robust freespace perception in complex urban driving environments.
SCATR: Mitigating New Instance Suppression in LiDAR-based Tracking-by-Attention via Second Chance Assignment and Track Query Dropout
Authors: Brian Cheong; Letian Wang; Sandro Papais; Steven L. Waslander
Venue: WACV 2026 (main)

Abstract: LiDAR-based tracking-by-attention (TBA) frameworks inherently suffer from high false negative errors, leading to a significant performance gap compared to traditional LiDAR-based tracking-by-detection (TBD) methods. This paper introduces SCATR, a novel LiDAR-based TBA model designed to address this fundamental challenge systematically. SCATR leverages recent progress in vision-based tracking and incorporates targeted training strategies specifically adapted for LiDAR. Our work's core innovations are two architecture-agnostic training strategies for TBA methods: Second Chance Assignment and Track Query Dropout. Second Chance Assignment is a novel ground truth assignment that concatenates unassigned track queries to the proposal queries before bipartite matching, giving these track queries a second chance to be assigned to a ground truth object and effectively mitigating the conflict between detection and tracking tasks inherent in tracking-by-attention. Track Query Dropout is a training method that diversifies supervised object query configurations to efficiently train the decoder to handle different track query sets, enhancing robustness to missing or newborn tracks. Experiments on the nuScenes tracking benchmark demonstrate that SCATR achieves state-of-the-art performance among LiDAR-based TBA methods, outperforming previous works by 7.6% AMOTA and successfully bridging the long-standing performance gap between LiDAR-based TBA and TBD methods. Ablation studies further validate the effectiveness and generalization of Second Chance Assignment and Track Query Dropout. Code can be found at the following link: \href https://github.com/TRAILab/SCATR  https://github.com/TRAILab/SCATR
GeneVA: A Dataset of Human Annotations for Generative Text to Video Artifacts
Authors: Jenna Kang; Maria Beatriz Silva; Patsorn Sangkloy; Kenneth Chen; Niall L. Williams; Qi Sun
Venue: WACV 2026 (main)

Abstract: Video generated by the current state-of-the-art generative models contain undesirable artifacts. We introduce GeneVA, the first large-scale dataset of human-annotated artifact bounding boxes in AI-generated videos. The dataset consists of 16,356 AI-generated videos, each labeled by a human annotator with per-frame artifact bounding boxes, their labels and descriptions, and video quality ratings. A custom data collection pipeline was developed in Prolific, and a novel taxonomy for spatio-temporal artifacts present in AI-generated videos was defined. The videos were from the VidProM dataset, with text prompts from this dataset then used to generate an additional subset of videos using Sora. We trained an artifact detector and caption generator using a pre-trained image-based model, and a custom temporal fusion module. The dataset can be found at https://www.immersivecomputinglab.org/publication/geneva. We hope that datasets like GeneVA will encourage improvements in artifact detection in AI-generated video towards applications such as deepfake detection.
Domain Generalizing DINO for Visual Regression via Latent Distractor Subspace Consistency
Authors: Nikhil Reddy; Chetan Arora; Mahsa Baktashmotlagh
Venue: WACV 2026 (main)

Abstract: Vision Foundation Models, such as \dino[??], have demonstrated remarkable generalization in classification; however, their application to out-of-domain visual regression tasks remains a significant and underexplored challenge. Unlike classification, domain generalization in regression poses distinct challenges: regression produces continuous outputs and is particularly sensitive to high-variance, label-irrelevant factors (e.g., illumination, blur, or contrast). These factors can entangle with task-relevant features and induce spurious correlations. While recent regression methods [??] have shown promise, they often rely on CNN backbones and require the pre-specification of known distractors. This demands significant domain expertise and fails to address spurious correlations that emerge during training. To address these challenges, we propose \proposedapproach, a Latent Distractor Subspace Consistency framework that disentangles intermediate feature representation into task-relevant and latent distractor subspaces, and regularizes the latter under photometric perturbations to suppress spurious correlations while preserving discriminative features during training. Our proposed method, \proposedapproach, is the first to effectively adapt the powerful \dino backbone for domain generalized visual regression. \proposedapproach achieves state-of-the-art results on seven benchmark regression datasets, demonstrating its strong performance in domain generalization for visual regression with percentage improvements of (41.75%, 20.12%, 52.05%, 8.27%, 22.21%, 3.55%) over state-of-the-art \DG regression methods, respectively. Project page is available: \href https://ldsc-iitd.github.io  ldsc-iitd.github.io .
Dragonite: Single-Step Drag-based Image Editing with Geometric-Semantic Guidance
Authors: Meng-Ting Jhong; Tai-Ming Huang; Shang-Fu Chen; Wen-Huang Cheng; Kai-Lung Hua
Venue: WACV 2026 (main)

Abstract: Recent interactive image editing methods have made notable progress, yet achieving both precise control and real-time performance remains a challenge. Drag-based methods offer detailed geometric manipulations but suffer from low image fidelity and slow runtime performance, while text-based approaches enhance realism but limit precise and pixel-level control. To overcome these limitations, we introduce Dragonite, an intuitive and efficient framework that seamlessly unifies geometric and semantic manipulation for image editing. Dragonite leverages a Dual Guidance Module that fuses geometric deformation vectors with semantic guidance cues into a joint representation space, ensuring precise manipulation of both content and semantics. By combining a single-step latent optimization mechanism with a enhanced interpolation method, Dragonite achieves efficient interactive image editing while maintaining high precision through integrated geometric and semantic guidance. Extensive evaluations on the DragBench benchmark demonstrate that Dragonite effectively resolves the trade-off between speed and accuracy, enabling real-time, high-fidelity image editing.
Synthesizing Compositional Videos from Text Description
Authors: Prajwal Singh; Kuldeep Kulkarni; Shanmuganathan Raman; Harsh Rangwani
Venue: WACV 2026 (main)

Abstract: Existing pre-trained text-to-video diffusion models can generate high-quality videos, but often struggle with misalignment between the generated content and the input text, particularly while composing scenes with multiple objects. To tackle this issue, we propose a straightforward, training-free approach for compositional video generation from text. We introduce Video-ASTAR for test-time aggregation and segregation of attention with a novel centroid loss to enhance alignment, which enables the generation of multiple objects in the scene, modeling the actions and interactions. Additionally, we extend our approach to the Multi-Action video generation setting, where only the specified action should vary across a sequence of prompts. To ensure coherent action transitions, we introduce a novel token-swapping and latent interpolation strategy. Extensive experiments and ablation studies show that our method significantly outperforms baseline methods, generating videos with improved semantic and compositional consistency alongside improved temporal coherence.
Layout Anything: One Transformer for Universal Room Layout Estimation
Authors: Md Sohag Mia; Muhammad Abdullah Adnan
Venue: WACV 2026 (main)

Abstract: We present Layout Anything, a transformer-based framework for indoor layout estimation that adapts the OneFormer's universal segmentation architecture to geometric structure prediction. Our approach integrates OneFormer's task-conditioned queries and contrastive learning with two key modules: (1) a layout degeneration strategy that augments training data while preserving Manhattan-world constraints through topology-aware transformations, and (2) differentiable geometric losses that directly enforce planar consistency and sharp boundary predictions during training. By unifying these components in an end-to-end framework, the model eliminates complex post-processing pipelines while achieving high-speed inference at 114ms. Extensive experiments demonstrate state-of-the-art performance across standard benchmarks, with pixel error (PE) of 5.43% and corner error (CE) of 4.02% on the LSUN, PE of 7.04% (CE 5.17%) on the Hedau and PE of 4.03% (CE 3.15%) on the Matterport3D-Layout datasets. The framework's combination of geometric awareness and computational efficiency makes it particularly suitable for augmented reality applications and large-scale 3D scene reconstruction tasks.
TS-PCI: Point Cloud Frame Interpolation with Time-Aware Point Cloud Sampling and Self-Supervised Learning Strategy
Authors: Kohei Matsuzaki; Keisuke Nonaka
Venue: WACV 2026 (main)

Abstract: Recent point cloud frame interpolation methods predict an interpolated frame through the merging of two intermediate frames constructed by scene flow estimation. However, these methods introduce errors due to generation since they adopt a generative approach to merge the frames, degrading frame interpolation performance. In this paper, we propose a point cloud frame interpolation method with time-aware point cloud sampling and a self-supervised learning strategy, termed TS-PCI. The proposed method introduces a time-aware learning-based point cloud sampling model to merge the two frames into a single frame in a non-generative approach. The proposed method also introduces an attention-based geometry refinement model to improve the geometric quality of the sampled point clouds. Furthermore, the proposed method adopts a self-supervised strategy that dynamically creates ground truth labels for point cloud sampling, allowing the models to be trained in an end-to-end manner. Experimental results on three large-scale datasets show that the proposed method achieves superior performance compared to state-of-the-art methods.