GenAI4ELab/papercli-papers huggingface dataset:
SegMango: Early Deep Mango Yield Prediction based on Flower Segmentation and Weather Data
Authors: Janaksinh Ven; Charu Sharma; Azeemuddin Syed
Venue: WACV 2026 (main)

Abstract: Early-stage fruit yield prediction plays a key role in supporting timely agronomic decisions, enhancing market planning, and empowering farmers with data-driven insights. Over the years, most approaches to yield estimation have focused on fruit counting techniques, typically performed just before harvest. While these methods have proven useful, they often come into play late in the cultivation cycle, limiting their impact on early planning and resource optimization. In this work, we introduce a comprehensive baseline framework for predicting mango yield at an earlier stage - during flowering - using image-based learning. Our contributions are twofold. (i) Our approach combines a SegFormer-based segmentation model with a regression pipeline to estimate yield from images, while also exploring the role of contextual features such as weather and scale. (ii) This work introduces a novel benchmark and an enriched dataset, paving the way for scalable, automated tools that can assist farmers and stakeholders in making proactive decisions throughout the mango growing season. Our work demonstrates that for multi-modal yield prediction, integrating features that complement visual representations (like scale) can be more impactful than using features with a stronger standalone linear correlation (like weather). Our single-image model, based on the SegFormer-B1 encoder, achieved a mean absolute error (MAE) of 7.68, R2 of 0.76, and mean squared error (MSE) of 115.48. These results highlight the promise of vision-based models for yield estimation from early-stage flowering cues. To the best of our knowledge, this is the first work to address the prediction of mango yield using images from the flowering stage and weather data.
From Detection to Anticipation: Online Understanding of Struggles across Various Tasks and Activities
Authors: Shijia Feng; Michael Wray; Walterio Mayol-Cuevas
Venue: WACV 2026 (main)

Abstract: Understanding human skill performance is essential for intelligent assistive systems, with struggle recognition offering a natural cue for identifying user difficulties. While prior work focuses on offline struggle classification and localization, real-time applications require models capable of detecting and anticipating struggle online. We reformulate struggle localization as an online detection task and further extend it to anticipation--predicting struggle moments before they occur. We adapt two off-the-shelf models as baselines for online struggle detection and anticipation. Online struggle detection achieves 70-80% per-frame mAP, while struggle anticipation up to 2 seconds ahead yields comparable performance with slight drops. We further examine generalization across tasks and activities and analyse the impact of skill evolution. Despite larger domain gaps in activity-level generalization, models still outperform random baselines by 4-20%. Our feature-based models run at up to 143 FPS, and the whole pipeline, including feature extraction, operates at around 20 FPS -- sufficient for real-time assistive applications.
D2Mamba: Dual Domain Guided Informed Search in State Space Model for Underwater Image Enhancement
Authors: Alik Pramanick; Soumajit Roy; Arijit Sur
Venue: WACV 2026 (main)

Abstract: Underwater images suffer from color distortion, haziness, and low-contrast due to light absorption and scattering. Despite deep learning advances in enhancement, challenges persist in efficiency, global context modeling, spatial-spectral consistency, and perceptually accurate detail recovery. To address these challenges, we design a novel underwater image enhancement framework, D2Mamba, adopting a dual-domain information (spatial and frequency) with state space models (SSMs), enabling efficient global context modeling while preserving local details. Unlike conventional SSMs that rely on raster, bidirectional, cross or diagonal scans, D2Mamba uses an A* search guided by physics-based Geodesic Information-Field Heuristic (GIFH) scan for feature traversal based on input degradation characteristics. GIFH combines feature gradients, high-frequency heterogeneity, and low-frequency semantic distance to compute adaptive costs, enabling the capture of both spatial and spectral dependencies. Further, a Spectral Wasserstein Attenuation Loss (SWAL) is introduced to enforce distributional alignment in the spectral domain, enabling perceptually consistent and physically consistent color restoration in enhanced underwater images. Extensive experiments on benchmark datasets demonstrate that D2Mamba achieves state-of-the-art performance with only 788K parameters and 7.06 GFLOPs. The code is available at https://github.com/Alik033/D2Mamba.
RAT4D: Rig and Animate Objects without Surface Templates in 4D
Authors: Mosam Dabhi; Simon Lucey; Laszlo A. Jeni
Venue: WACV 2026 (main)

Abstract: We present a surface-template-free method for reconstructing dense, rigged (re-animatable) 3D models from monocular videos. By combining robust pose optimization with differentiable Gaussian splatting, this work bridges the gap between flexible template-free approaches and the visual quality of template-based methods. Starting from noisy 2D keypoints, we refine 3D poses through kinematic and temporal constraints, then attach Gaussian primitives to the optimized skeleton for differentiable supervision and rendering. We demonstrate dense, rigged (re-animatable) 3D models without surface templates across humans, animals, insects, and everyday articulated objects, and we empirically show that closing the rendering-pose loop improves 3D lifting from noisy landmarks. A key enabler of these template-free reconstructions is our kinematic optimization, which reduces 3D pose error by 20-25% relative to template-free baselines; at the same time, our results approach template-based visual metrics (PSNR/SSIM within 5%). We also demonstrate the method's practical utility by detecting and correcting geometric inconsistencies in AI-generated videos. While limited to articulated subjects with detectable keypoints, the approach provides a practical pipeline that serves as a drop-in refinement to improve 3D lifting in existing pipelines and enables the creation of rigged 3D assets from casual captures when expensive surface templates (e.g., MoCap-derived) are unavailable.
Reinforcement Learning-based Adaptive Control of Classifier-Free Guidance and Timestep Embeddings in Diffusion Models
Authors: Haochen You; Baojing Liu; Hongyang He
Venue: WACV 2026 (main)

Abstract: Recent advances in reinforcement learning (RL) have enabled effective reward-based finetuning of text-to-image diffusion models, improving their alignment with user preferences. However, existing RL methods typically optimize only the denoising UNet while relying on fixed generation strategies, limiting their flexibility and controllability. In this work, we propose ADOPT, an adaptive diffusion policy training framework that unifies the optimization of Classifier-Free Guidance (CFG) scaling and timestep embedding modulation within a single RL paradigm. Specifically, ADOPT learns a prompt-conditioned policy to adjust the CFG strength dynamically and to modulate timestep embeddings via learnable curve-based scaling, enhancing both semantic guidance and temporal understanding of the diffusion process. Extensive experiments demonstrate that ADOPT consistently improves semantic alignment, aesthetic quality, and human preference scores across diverse prompt datasets, while maintaining efficient inference cost. Our results highlight the potential of jointly optimizing adaptive control strategies to unlock greater flexibility and performance for reward-driven diffusion generation.
ImageChain: Advancing Sequential Image-to-Text Reasoning in Multimodal Large Language Models
Authors: Danae Sanchez Villegas; Ingo Ziegler; Desmond Elliott
Venue: WACV 2026 (main)

Abstract: Reasoning over sequences of images remains a challenge for multimodal large language models (MLLMs). While recent models incorporate multi-image data during pre-training, they still struggle to recognize sequential structures, often treating images independently. This work introduces ImageChain, a framework that enhances MLLMs with sequential reasoning capabilities over image data by modeling visual sequences as a multi-turn conversation. In ImageChain, images are interleaved with corresponding textual descriptions to form a controlled dialogue that explicitly captures temporal dependencies and narrative progression. Our method optimizes for the task of next-scene description, where the model generates a context-aware description of an upcoming scene based on preceding visual and textual cues. We demonstrate that our approach improves performance on the next-scene description task -- achieving an average improvement from 3.7% to 19% in SimRate, a metric that quantifies semantic similarity to human-annotated ground truths. Moreover, ImageChain achieves robust zero-shot out-of-domain performance in applications ranging from comics to robotics. Extensive experiments validate that instruction-tuning in a multimodal, multi-turn conversation design is key to bridging the gap between static image understanding and temporally-aware reasoning.
Blur2Sharp: Human Novel Pose and View Synthesis with Generative Prior Refinement
Authors: Chia-Hern Lai; I-Hsuan Lo; Yen-Ku Yeh; Thanh-Nguyen Truong; Ching-Chun Huang
Venue: WACV 2026 (main)

Abstract: The creation of lifelike human avatars capable of realistic pose variation and viewpoint flexibility remains a fundamental challenge in computer vision and graphics. Current approaches typically yield either geometrically inconsistent multi-view images or sacrifice photorealism, resulting in blurry outputs under diverse viewing angles and complex motions. To address these issues, we propose Blur2Sharp, a novel framework integrating 3D-aware neural rendering and diffusion models to generate sharp, geometrically consistent novel-view images from only a single reference view. Our method employs a dual-conditioning architecture: initially, a Human NeRF model generates geometrically coherent multi-view renderings for target poses, explicitly encoding 3D structural guidance. Subsequently, a diffusion model conditioned on these renderings refines the generated images, preserving fine-grained details and structural fidelity. We further enhance visual quality through hierarchical feature fusion, incorporating texture, normal, and semantic priors extracted from parametric SMPL models to simultaneously improve global coherence and local detail accuracy. Extensive experiments demonstrate that Blur2Sharp consistently surpasses state-of-the-art techniques in both novel pose and view generation tasks, particularly excelling under challenging scenarios involving loose clothing and occlusions.
SkelSplat: Robust Multi-view 3D Human Pose Estimation with Differentiable Gaussian Rendering
Authors: Laura Bragagnolo; Leonardo Barcellona; Stefano Ghidoni
Venue: WACV 2026 (main)

Abstract: Accurate 3D human pose estimation is fundamental for applications such as augmented reality and human-robot interaction. State-of-the-art multi-view methods learn to fuse predictions across views by training on large annotated datasets, leading to poor generalization when the test scenario differs. To overcome these limitations, we propose SkelSplat, a novel framework for multi-view 3D human pose estimation based on differentiable Gaussian rendering. Human pose is modeled as a skeleton of 3D Gaussians, one per joint, optimized via differentiable rendering to enable seamless fusion of arbitrary camera views without 3D ground-truth supervision. Since Gaussian Splatting was originally designed for dense scene reconstruction, we propose a novel one-hot encoding scheme that enables independent optimization of human joints. SkelSplat outperforms approaches that do not rely on 3D ground truth in Human3.6M and CMU, while reducing the cross-dataset error up to 47.8% compared to learning-based methods. Experiments on Human3.6M-Occ and Occlusion-Person demonstrate robustness to occlusions, without scenario-specific fine-tuning. Our project page is available here: https://skelsplat.github.io.
ControlEvents: Controllable Synthesis of Event Camera Data with Foundational Prior from Image Diffusion Models
Authors: Yixuan Hu; Yuxuan Xue; Simon Klenk; Daniel Cremers; Gerard Pons-Moll
Venue: WACV 2026 (main)

Abstract: Event cameras have gained significant attention due to their bio-inspired properties, such as high temporal resolution and high dynamic range. However, obtaining large-scale labeled ground-truth data for event-based vision tasks remains challenging and costly, which hinders the development of the event-based algorithm. In this paper, we present ControlEvents, a diffusion-based generative model designed to synthesize unlimited high-quality event data guided by diverse control signals such as class text labels, 2D skeletons, and 3D body poses. Our key insight is to leverage the diffusion prior from foundation models, such as Stable Diffusion, enabling high-quality event data generation with minimal fine-tuning and limited labeled data. Our method streamlines the data generation process and significantly reduces the cost of producing labeled event datasets. We demonstrate the effectiveness of our approach by synthesizing event data for visual recognition, 2D skeleton estimation, and 3D body pose estimation. Our experiments show that the synthesized labeled event data enhances model performance in all tasks. Additionally, our approach can generate events based on unseen text labels during training, illustrating the powerful text-based generation capabilities inherited from foundation models.
TM-Adapter: Temporal Merge Adapter for Efficient Global Temporal Modeling
Authors: Woo Joo Hahm; Seungwoo Jang; Hyeon Tak Kim; Daeun Lee; Kwangsu Kim
Venue: WACV 2026 (main)

Abstract: We propose the Temporal Merge Adapter (TM-Adapter), a novel framework for image-to-video parameter-efficient transfer learning (PETL) specifically designed for temporal representation learning in video understanding. PETL has emerged as a practical strategy for adapting large-scale vision models to video tasks under limited computational budgets. However, existing PETL approaches suffer from local redundancy caused by highly similar consecutive frames, which limits the modeling of diverse temporal dependencies. To address this limitation, we introduce a lightweight merge-unmerge mechanism that temporally aggregates and redistributes token embeddings, enabling the model to capture diverse temporal patterns by mitigating redundancy. Furthermore, to effectively handle diverse temporal dependencies across different time scales, TM-Adapter introduces a single adapter module with two parallel branches, local and global adapters, each specialized in capturing complementary patterns at different temporal ranges. We validate our approach through experiments on Kinetics-400, Something-Something V2, and HMDB-51, demonstrating competitive performance compared to existing methods while maintaining high parameter efficiency.
From Few-Shot to Zero-Shot Pallet Load Recognition: A Deployed Embedding-Based Vision System for Industrial Logistics
Authors: Juan Jesús Losada del Olmo; Emilio Pardo Ballesteros; Pedro E. López-de-Teruel; Alberto Ruiz
Venue: WACV 2026 (main)

Abstract: Automated pallet load recognition is a critical task in industrial logistics, but the deployment of conventional deep learning systems is often unfeasible. Their reliance on large, manually annotated datasets creates a prohibitive bottleneck in terms of cost and time, especially in dynamic environments where product lines frequently change. To overcome this challenge, we introduce a highly flexible, dual-mode vision system built upon dense patch embeddings. Our primary, few-shot approach leverages features from the CAPI vision model to construct a compact memory bank from as little as a single labeled example per class. Classification is then performed via a simple yet highly effective k-nearest neighbor search. For annotation-free scenarios, we also propose a zero-shot mode that identifies the load by finding the rectangular region that minimizes intra-class feature variance. We demonstrate state-of-the-art performance on a new, challenging industrial dataset, where our few-shot method attains a mAP_ 50-95  over 90% with only one support image per class. Additionally, the fully unsupervised approach achieves a notable mAP_ 50-95  of up to 75%. The system's robustness and practical value were validated through its successful deployment in high-stakes, real-world scenarios. Our findings establish a basis for lightweight solutions that support the rapid, data-efficient integration of new vision systems into industrial workflows. To facilitate reproducibility, the source code is released at  \normalsizehttps://github.com/juanjesus-ldo/F2ZLR .
AFRAgent : An Adaptive Feature Renormalization Based High Resolution Aware GUI agent
Authors: Neeraj Anand; Rishabh Jain; Sohan Patnaik; Balaji Krishnamurthy; Mausoom Sarkar
Venue: WACV 2026 (main)

Abstract: There is a growing demand for mobile user interface (UI) automation, driven by its broad applications across industries. With the advent of visual language models (VLMs), GUI automation has progressed from generating text-based instructions for humans to autonomously executing tasks, thus optimizing automation workflows. Recent approaches leverage VLMs for this problem due to their ability to 1) process on-screen content directly, 2) remain independent of device-specific APIs by utilizing human actions (e.g., clicks, typing), and 3) apply real-world contextual knowledge for task understanding. However, these models often have trouble accurately identifying widgets and determining actions due to limited spatial information in vision encoder features. Additionally, top-performing models are often large, requiring extensive training and resulting in inference delays. In this work, we introduce AFRAgent, an instruct-BLIP-based multimodal architecture that achieves superior performance in GUI automation while being less than one-fourth the size of its nearest competitor. To enhance image embeddings in the large language model (LLM) pipeline, we propose an adaptive feature renormalization-based (a token-level affine transformation) technique that effectively enriches low-resolution image embeddings and fuses high-resolution details. We evaluate AFRAgent on Meta-GUI and AITW benchmarks, establishing a new state-of-the-art baseline for smartphone automation.
SUGAR: A Sweeter Spot for Generative Unlearning of Many Identities
Authors: Dung Thuy Nguyen; Quang Nguyen; Preston K. Robinette; Eli Jiang; Taylor T. Johnson; Kevin Leach
Venue: WACV 2026 (main)

Abstract: Recent advances in 3D-aware generative models have enabled high-fidelity image synthesis of human identities. However, this progress raises urgent questions around user consent and the ability to remove specific individuals from a model's output space. We address this by introducing SUGAR, a framework for scalable generative unlearning that enables the removal of many identities (simultaneously or sequentially) without retraining the entire model. Rather than projecting unwanted identities to unrealistic outputs or relying on static template faces, SUGAR learns a personalized surrogate latent for each identity, diverting reconstructions to visually coherent alternatives while preserving the model's quality and diversity. We further introduce a continual utility preservation objective that guards against degradation as more identities are forgotten. SUGAR achieves state-of-the-art performance in removing up to 200 identities, while delivering up to a 700% improvement in retention utility compared to existing baselines. Our code is publicly available at https://github.com/judydnguyen/SUGARGenerative-Unlearn.
LASOR: Towards Clinically Transparent and Explainable Ophthalmic Report Generation via Lesion-Aware Segmentation
Authors: Jian Park; Hyunseon Won; JeeEun Kim; Joon Seo Hwang; Jeong Mo Han; Ji In Park; Daniel Duck-Jin Hwang; Jinyoung Han
Venue: WACV 2026 (main)

Abstract: Automated ophthalmic report generation aims to reduce the diagnostic burden on retinal specialists by producing clinically accurate and standardized descriptions from medical imaging. However, current research predominantly remains fundus-centric and rarely exploits OCT-derived spatial evidence, limiting clinical transparency by obscuring which anatomical regions drive diagnostic decisions. To address these limitations, we propose LASOR (Lesion-Aware Segmentation-Guided Ophthalmic Report Generation), which extracts multi-scale features to robustly capture both small focal abnormalities and broader anatomical structures, generating reliable segmentation masks as spatial priors for report generation. Specifically, we utilize a lesion-aware patch weighting module to emphasize abnormal regions and leverage a curated instruction dataset incorporating spatial mask information to enhance the diagnostic capabilities of the proposed model. In addition, we introduce a mask-guided cross-modal consistency loss that strengthens vision-language alignment between pathological regions and their diagnostic descriptions. Extensive experiments on a retinal OCT dataset that includes twenty pathological conditions exhibit state-of-the-art performance, underscoring LASOR's potential to advance clinically transparent ophthalmic report generation systems.
ST-Think: How Multimodal Large Language Models Reason About 4D Worlds from Ego-Centric Videos
Authors: Peiran Wu; Yunze Liu; Miao Liu; Junxiao Shen
Venue: WACV 2026 (main)

Abstract: Humans excel at spatial-temporal reasoning, effortlessly interpreting dynamic visual events from an egocentric viewpoint. However, whether multimodal large language models (MLLMs) can similarly understand the 4D world remains uncertain. This paper explores multimodal spatial-temporal reasoning from an egocentric perspective, aiming to equip MLLMs with human-like reasoning capabilities. To support this objective, we introduce Ego-ST Bench, a novel benchmark containing over 5,000 question-answer pairs across four categories, systematically evaluating spatial, temporal, and integrated spatial-temporal reasoning. Additionally, we propose ST-R1 training paradigm, a video-based reasoning model that incorporates reverse thinking into its reinforcement learning process, significantly enhancing performance. We combine long-chain-of-thought (long-CoT) supervised fine-tuning with Group Relative Policy Optimization (GRPO) reinforcement learning, achieving notable improvements with limited high-quality data. Ego-ST Bench and ST-R1 provide valuable insights and resources for advancing video-based spatial-temporal reasoning research.