GenAI4ELab/papercli-papers huggingface dataset:
Point2Pose: A Generative Framework for 3D Human Pose Estimation with Multi-View Point Cloud Dataset
Authors: Hyunsoo Lee; Daeum Jeon; Hyeokjae Oh
Venue: WACV 2026 (main)

Abstract: We propose a novel generative approach for 3D human pose estimation. 3D human pose estimation poses several key challenges due to the complex geometry of the human body, self-occluding joints, and the requirement for large-scale real-world motion datasets. To address these challenges, we introduce Point2Pose, a framework that effectively models the distribution of human poses conditioned on sequential point cloud and pose history. Specifically, we employ a spatio-temporal point cloud encoder and a pose feature encoder to extract joint-wise features, followed by an attention-based generative regressor. Additionally, we present a large-scale indoor dataset MVPose3D, which contains multiple modalities, including IMU data of non-trivial human motions, dense multi-view point clouds, and RGB images. Experimental results show that the proposed method outperforms the baseline models, demonstrating its superior performance across various datasets.
GDoFS: Gaussian DoF Separation for Plausible 3D Geometry in Sparse-View 3DGS
Authors: Yongsung Kim; Jooyoung Choi; Sungroh Yoon
Venue: WACV 2026 (main)

Abstract: Recent deep learning-based Multi-View Stereo (MVS) approaches, such as MASt3R and VGGT, have shown strong performance in sparse-view 3D reconstruction. However, refining these outputs with 3D Gaussian Splatting (3DGS) remains non-trivial. Excessive positional degrees of freedom (DoFs) in Gaussians often cause instability and geometric artifacts, sometimes distorting geometry to represent texture patterns. To address this issue, we propose GDoFS (Gaussian DoF Separation), a strategy that divides positional DoFs into two categories--image-plane-parallel and ray-aligned--based on their uncertainty. For each category, GDoFS introduces tailored optimization techniques, including bounded offsets for low-uncertainty DoFs and a visibility-guided loss for ray-aligned DoFs. Experiments on standard benchmarks demonstrate that GDoFS effectively mitigates geometric artifacts and produces reconstructions that are both visually coherent and structurally accurate.
Gen-AFFECT: Generation of Avatar Fine-grained Facial Expressions with Consistent identiTy
Authors: Hao Yu; Rupayan Mallick; Margrit Betke; Sarah Adel Bargal
Venue: WACV 2026 (main)

Abstract: Different forms of customized 2D avatars are widely used in gaming applications, virtual communication, education, and content creation. However, existing approaches often fail to capture fine-grained facial expressions and struggle to preserve identity across different expressions. We propose Gen-AFFECT, a novel framework for personalized avatar generation that generates expressive and identity-consistent avatars with a diverse set of facial expressions. Our framework proposes conditioning a multimodal diffusion transformer on an extracted identity-expression representation. This enables identity preservation and representation of a wide range of facial expressions. Gen-AFFECT additionally employs consistent attention at inference for information sharing across the set of generated expressions, enabling the generation process to maintain identity consistency over the array of generated fine-grained expressions. Gen-AFFECT demonstrates superior performance compared to previous state-of-the-art methods on the basis of the accuracy of the generated expressions, the preservation of the identity and the consistency of the target identity across an array of fine-grained facial expressions.
Distilling Offline Action Detection Models into Real-Time Streaming Models
Authors: Deep Patel; Yasunori Babazaki; Yasuto Nagase; Iain Melvin; Martin Renqiang Min
Venue: WACV 2026 (main)

Abstract: Vision Transformers (ViTs) have achieved state-of-the-art performance in offline video action detection, but their reliance on processing fixed-size clips with full spatio-temporal attention makes them computationally expensive and ill-suited for real-time streaming applications due to massive computational redundancy. This paper introduces a novel framework to adapt these powerful offline models into efficient, online student models through knowledge distillation. We propose two causal, streaming-friendly attention architectures that replace the full self-attention mechanism: (1) a lightweight Temporal Shift Attention that integrates past context with minimal overhead, and (2) a Decomposed Spatial-Temporal Attention that combines intra-frame spatial attention with an LSTM for temporal modeling. Both architectures utilize caching to eliminate redundant operations on a frame-by-frame basis. To maximize knowledge transfer, we introduce an uncertainty-guided distillation process, which formulates the training as a multi-task learning problem. Our resulting models demonstrate significant efficiency gains, achieving up to a 4x improvement in latency and throughput compared to the original offline teacher while ensuring state-of-the-art performance for online methods. Our work provides a practical and effective methodology for deploying high-accuracy transformer models in latency-sensitive, real-world video analysis systems.
From Bands to Depth: Understanding Bathymetry Decisions on Sentinel-2
Authors: Satyaki Roy Chowdhury; Aswathnarayan Radhakrishnan; Hari Subramoni
Venue: WACV 2026 (main)

Abstract: Deploying Sentinel-2 satellite-derived bathymetry (SDB)robustly across sites remains challenging. We analyze aSwin-Transformer U-Net (Swin-BathyUNet) to understandhow it infers depth and when its predictions are trustwor-thy. A leave-one-band-out study ranks spectral importanceto the different bands consistent with shallow-water op-tics. We adapt ablation-based CAM to regression (A-CAM-R) and validate faithfulness via a performance-retentiontest: keeping only the top-p% salient pixels while neutral-izing the rest causes large, monotonic RMSE increases,indicating explanations localize causal evidence. Atten-tion ablations show decoder-conditioned cross-attention onskips is the most cost-effective upgrade, improving robust-ness to glint/foam. Cross-region inference (train one site,test another) reveals depth-dependent degradation: MAErises nearly linearly with depth, and bimodal depth dis-tributions exacerbate mid/deep errors. Practical guidancefollows: maintain wide receptive fields, preserve radiomet-ric fidelity in green/blue channels, pre-filter bright high-variance near shore, and pair light target-site fine-tuningwith depth-aware calibration to transfer across regions.
Remote Sensing Forestry Similarity Convolution
Authors: Shikuan Wang; Yuangong Chen; Jianzhou Gong; Lingyi Meng; Mengquan Wu; Longxing Liu; Haiwei Yuan; Mingbin Guo
Venue: WACV 2026 (main)

Abstract: Recent advancements in convolutional neural networks (CNNs) have significantly propelled the field of remote sensing forestry mapping. However, traditional convolution operations exhibit inherent limitations in extracting complex forest features: their fixed receptive fields struggle to accommodate multi-scale forest attributes, and their insufficient focus on background information impairs the overall feature representation. To address these challenges, we propose Similar Convolution (SimConv), which introduces dynamic convolution kernel size selection by modeling feature relationships. SimConv adaptively adjusts the receptive field based on the semantic relevance of input features, enhancing the capture of forestry background information and improving the distinction between target features. Building upon this, we introduce SIMNet, a feature extraction network that integrates SimConv at its core. Experimental results across multiple remote sensing datasets demonstrate that SIMNet outperforms existing methods in terms of feature extraction accuracy. code in https://github.com/WangShiK/SimConv.
Conjuring Positive Pairs for Efficient Unification of Representation Learning and Image Synthesis
Authors: Imanol G. Estepa; Jesús M. Rodríguez-de-Vera; Ignacio Sarasúa; Bhalaji Nagarajan; Petia Radeva
Venue: WACV 2026 (main)

Abstract: While representation learning and generative modeling seek to understand visual data, unifying both domains remains unexplored. Recent Unified Self-Supervised Learning (SSL) methods have started to bridge the gap between both paradigms. However, they either extract information from discriminative pretrained models or rely solely on semantic token reconstruction, which requires an external tokenizer during training --- introducing a significant computational overhead. In this work, we introduce Sorcen, a novel Unified SSL framework, incorporating a synergic Contrastive-Reconstruction objective. Our novel Contrastive objective, leverages the generative capabilities of Sorcen and eliminates the need for additional image crops or augmentations during training. Sorcen generates contrastive positive samples, called Echoes, directly in the semantic token space using the reconstruction objective. This on-the-fly Echo generation, enables Sorcen to operate exclusively on precomputed tokens, eliminating the need for an online tokenizer during training. Sorcen significantly reduces the computational overhead by 60.8% compared to token reconstruction SoTA. Extensive experiments on ImageNet-1k demonstrate that Sorcen outperforms the previous Unified SSL SoTA by 0.4%,1.48 FID, 1.76%, and 1.53% on linear probing, unconditional image generation, few-shot learning, and transfer learning, respectively. Additionally, Sorcen establishes as a new single-crop MIM SoTA in linear probing and achieves SoTA performance in unconditional image generation, highlighting significant improvements and breakthroughs in Unified SSL models.
Root Completion from Intraoral Scans of Tooth Crowns using Diffusion with Patch Perturbation
Authors: Yohan Jang; In-Seok Song; Seung Jun Baek
Venue: WACV 2026 (main)

Abstract: Intraoral scan (IoS) provides high-resolution data on the tooth crown, but does not contain information on the tooth root and thus has limitations in applications requiring 3D models of the whole tooth, e.g., virtual dental simulators. In this paper, we consider a diffusion-based model for root completion from IoS crowns. A key challenge is the lack of ground truth, i.e., the scan data of roots are typically unavailable. To train our model, we instead use the Cone-Beam CT (CBCT) data matched to IoS images, and use its crown as input and root as the pseudo-ground truth. Due to the difference in input data between training (CBCT crown) and inference (IoS crown), there is an issue of domain shift. To address the issue, we take a coarse-to-fine approach: we make a coarse prediction of roots using Coarse Estimator; introduce Perturbed Patch Generator (PPG) which generates patches from coarse points and perturbs them with noise for a robust prediction against the domain shift; and use Transformer denoiser for refined reconstruction. We also propose loss functions designed to facilitate the training of the denoiser with perturbed patches. Experiments show that our method outperforms prior techniques in various benchmark evaluations, demonstrating its robust performance in generating high-quality root data. Our code is available at https://github.com/yhJang94/RootCompletion.git.
Multimodal Graph Representation Learning over Arbitrary Sets of Modalities
Authors: Santosh Patapati; Trisanth Srinivasan
Venue: WACV 2026 (main)

Abstract: We introduce CLARGA, a general-purpose multimodal fusion architecture for multimodal representation learning that works with any number and type of modalities without changing the underlying framework. Given a supervised dataset, CLARGA can be applied to virtually any machine learning task to fuse different multimodal representations for processing by downstream layers. On a sample-by-sample basis, CLARGA learns how modalities should inform one another by building an attention weighted graph over their features and passing messages along this graph with a multi-head Graph Attention Network. Not only does this make CLARGA highly adaptive, as it constructs unique graphs for different samples, it makes for efficient fusion with sub-quadratic complexity as the number of modalities grows. Through a learnable mask, it can also adapt to missing modality inputs. The model is trained with a hybrid objective that combines a supervised task loss with contrastive InfoNCE loss, improving cross-modal consistency and robustness to noisy inputs. We demonstrate CLARGA's effectiveness in diverse multimodal representation learning tasks across 7 datasets spanning finance, human-computer interaction, general multimedia classification, and affective computing. It consistently outperforms baselines, state-of-the-art models, and ablations. Additional experiments also demonstrate its robustness to missing inputs and ability to excel on niche tasks. Overall, CLARGA can be easily plugged into machine learning models for effective and efficient learning of representations effective and efficient learning of representations across a wide variety of tasks.
FlowMorph: Revealing an Optimizable Flow Latent Space for Controlled Image Morphing
Authors: Yan Zheng; Yi Yang; Lanqing Guo; Zhangyang Wang
Venue: WACV 2026 (main)

Abstract: We present FlowMorph, a simple and training-free framework for geometry-preserving and semantics-aware image interpolation. The key idea is to separate two factors inside the flow model's latent space: an offset that captures shape and geometry, and a one-step vector that carries semantic meaning. By keeping the flow model frozen and only optimizing these two variables, FlowMorph exposes a stable and interpretable neighborhood around each image. This leads to two complementary modes. Flow-Optimizer directly fits a source image toward a target image and naturally supports multi-objective combinations, producing stable reconstructions. Flow-Interpolation mixes the offset linearly and the semantic vector spherically, generating smooth and coherent transitions between images. Across a wide range of tasks including object morphing, pose changes, and scene transitions, FlowMorph outperforms prior interpolation-based methods. Quantitative experiments show that our method achieves lower perceptual error, better image fidelity, and smoother transitions. Landmark-based analysis further confirms that FlowMorph preserves geometry more effectively. We also ablate the effect of the backward step size, showing that longer steps increase semantic expressiveness and allow interpolations that move beyond trivial shape blending, enabling richer morphs across object positions and photo layouts. FlowMorph provides an interpretable and controllable tool for high-quality image morphing without the need for additional training.
Crafting Descriptive Information for a Zero-shot Method to Improve Knowledge-Based Visual Question Answering Performance
Authors: Mohammad Mahdi Moradi; Sudhir Mudur
Venue: WACV 2026 (main)

Abstract: We present GC-KBVQA, a zero-shot framework for knowledge-based visual question answering (KB-VQA) that requires no additional training. GC-KBVQA leverages pre-trained models together with carefully designed, context-aware descriptive information. The framework integrates three modules--(i) question-guided visual grounding, (ii) semantics-based caption filtering, and (iii) inter-stage feedback--that work together to generate concise, relevant prompts while reducing hallucinations and noisy auxiliary text. Despite its lightweight design, GC-KBVQA outperforms strong zero-shot baselines by up to +10.97% on OK-VQA, A-OKVQA, and VQAv2, and approaches the performance of few-shot systems without labeled data. The framework is model-agnostic, maintaining effectiveness across LLMs from TinyLLaMA-1B to Llama3-8B with minimal degradation. Ablation studies confirm that grounding, dual-caption generation, and both intra-stage filtering and inter-stage feedback each contribute to accuracy improvements. By combining efficiency, robustness, and modularity, GC-KBVQA provides a practical and scalable direction for zero-shot KB-VQA.
Boosting Medical Vision-Language Pretraining via Momentum Self-Distillation under Limited Computing Resources
Authors: Phuc Pham; Nhu Pham; Ngoc Quoc Ly
Venue: WACV 2026 (main)

Abstract: In medical healthcare, obtaining detailed annotations is challenging, highlighting the need for robust Vision-Language Models (VLMs). Pretrained VLMs enable fine-tuning on small datasets or zero-shot inference, achieving performance comparable to task-specific models. On the one hand, contrastive learning (CL) is a key paradigm for training VLMs but inherently requires large batch sizes for effective learning, making it computationally demanding and often limited to well-resourced institutions. On the other hand, with limited data in healthcare, it is important to prioritize knowledge extraction from both data and models during training to improve performance. Therefore, we focus on leveraging the momentum method combined with distillation to simultaneously address computational efficiency and knowledge exploitation. Our contributions can be summarized as follows: (1) leveraging momentum self-distillation to enhance multimodal learning, and (2) integrating momentum mechanisms with gradient accumulation to enlarge the effective batch size without increasing resource consumption. Our method surpasses state-of-the-art (SOTA) approaches in few-shot learning with over 90% AUC ROC and improves retrieval tasks by 2-3% across multiple datasets. Importantly, our method achieves high training efficiency with a single GPU while maintaining reasonable training time. Our approach aims to advance efficient multimodal learning by reducing resource requirements while improving performance over SOTA methods.
Uncertainty-Aware Vision-Language Segmentation for Medical Imaging
Authors: Aryan Das; Tanishq Rachamalla; Koushik Biswas; Swalpa Kumar Roy; Vinay Kumar Verma
Venue: WACV 2026 (main)

Abstract: Medical image segmentation is crucial for computer-aided diagnosis, surgical planning, and clinical research, requiring precise delineation of anatomical structures and pathological regions across various imaging modalities. Traditional deep learning approaches, primarily based on convolutional neural networks (CNNs) and transformers, have shown high performance but are limited by their reliance on visual features alone, which restricts their generalization and integration of clinical knowledge. The increasing availability of multimodal medical data, including paired image-text records from electronic health systems, offers a promising solution to these limitations. Vision-language segmentation (VLS) leverages natural language inputs, such as radiology reports or anatomical queries, to guide the segmentation process. This multimodal approach bridges the gap between low-level visual cues and high-level clinical concepts, reduces the need for task-specific supervision, and facilitates more intuitive human-AI interaction in medical workflows. Despite recent advancements, VLS in the medical domain faces significant challenges, including the subtlety of pathological features, inter-reader variability, and the need for fine-grained spatial accuracy. Privacy constraints and the scarcity of well-aligned, high-quality image-text pairs further complicate model training and evaluation, limiting the applicability of general-purpose VLS models in clinical settings. This paper introduces an innovative uncertainty-guided multimodal vision-language segmentation model designed to address these challenges. Our model integrates visual and textual data through advanced cross-modal learning techniques, enhancing segmentation accuracy and robustness. By incorporating uncertainty guidance, the model improves spatial precision and better captures domain-specific visual-linguistic relationships, making it more suitable for clinical applications.
MR-Pruner: Training-free Multi-resolution Visual Token Pruning for Multi-modal Large Language Models
Authors: Seunghoon Han; Hyewon Lee; Soyoung Park; Jong-Ryul Lee; Sungsu Lim
Venue: WACV 2026 (main)

Abstract: Large Language Models (LLMs) extended to multi-modal inputs have led to Multi-modal LLMs (MLLMs) that perform strongly on vision-language tasks. Recent MLLMs adopt multi-resolution inputs to capture both global context and local details, but this substantially increases visual tokens and computational cost. Existing pruning methods reduce redundancy but are designed for single-resolution settings, overlooking the characteristics of multi-resolution tokens. We observe two key properties: tokens from different resolutions follow distinct distributions of information content, and tokens across resolutions exhibit mutual complementarity, such that pruning one type can often be compensated by the other. Based on this observation, we propose Multi-Resolution Token Pruning method (MR-Pruner), a training-free, graph-based pruning framework for multi-resolution MLLMs. MR-Pruner incorporates three components--Intra-resolution, Cross-resolution Token Scoring, and Informativeness-aware Token Pruning--that adaptively allocate pruning ratios and facilitate information propagation across resolutions. Experiments on eight benchmarks show that MR-Pruner achieves superior efficiency-performance trade-offs. For example, when only 10% of the visual tokens are retained, it leads to an average performance degradation of 3.6%. For reproducibility, the source code is available at https://github.com/gooriiie/MR-Pruner.
SeqFeedNet: Sequential Feature Feedback Network for Background Subtraction
Authors: Yu-Shun Huang; Jing-Ming Guo; Yi-Xiang Yang
Venue: WACV 2026 (main)

Abstract: Background subtraction (BGS) is a fundamental task in computer vision with applications in video surveillance, object tracking, and recognition. Despite recent advancements, many deep learning-based BGS algorithms rely on large models to extract high-level representations, demanding significant computational resources and leading to inefficiencies in processing video streams. To address these limitations, we introduce the Sequential Feature Feedback Network (SeqFeedNet), a novel supervised algorithm for BGS in unseen videos that operates without additional pre-processing models. SeqFeedNet innovatively incorporates time-scale diverse sequential features and employs a feedback mechanism for each iteration. Moreover, we propose the Sequential Fit Training (SeqFiT) technique, enhancing model convergence during training. Evaluated on the CDNet 2014 dataset, SeqFeedNet not only achieves ~5 times increase in inference speed but also outperforms F-Measure scores of the leading supervised algorithms, making it highly suitable for real-world applications. Our experiment demonstrates that SeqFeedNet surpasses state-of-the-art network without pre-trained segmentation model by 3.83% F-Measure on the CDnet 2014 dataset. Leading the way to establish a new benchmark for efficient and effective BGS in unseen videos. The code is released at https://github.com/tw-yshuang/SeqFeedNet.