GenAI4ELab/papercli-papers huggingface dataset:
Crash2DocAI: Automated Integration of Post-Crash Car Part Images into Technical Reports
Authors: Vaclav Divis; Jessica Giovagnola; Khalil Ben Chikha; Marek Hrúz
Venue: WACV 2026 (main)

Abstract: Car-crash safety assessments require experts to analyze and document numerous vehicle components from various angles, resulting in a large number of post-crash images. Currently, this process relies on manual image classification and integration into structured reports -- a time-consuming and error-prone workflow that limits scalability and consistency. In this paper, we present Crash2DocAI, a tool designed to automate the classification and integration of post-crash car part images into technical reports. Our system leverages ConvNeXt, a state-of-the-art image classification model, which achieves a top-1 accuracy of 94.4% on a newly compiled dataset of 5,772 publicly available post-crash images spanning 32 car part categories. To enable real-time deployment on CPU-only devices, we apply structured pruning and quantization, reducing the model size from 334.3MB to 77.6MB and inference time from 342ms to 94ms per image--while preserving classification performance. To enhance the robustness of our tool, we introduce an Out-of-Model-Scope (OMS) monitor based on Mahalanobis distance, which filters images outside the target domain. This binary detector achieves a precision of 71% and a recall of 95%, with only a 1% overhead on inference time. We further demonstrate the practical utility of Crash2DocAI in real-world scenarios through a user study involving 26 automotive safety experts. The results reflect a 90% speed-up and significantly more consistent completion times. Finally, we release the National Highway Traffic Safety Administration-Post-Crash Car Parts (NHTSA-PCCP) dataset to the research community, together with the application and evaluation materials. https://gitlab.com/divisvaclav/crash2docai
CoL2A: Convolution-free Local Linear Attention for SpatioTemporal Event Processing
Authors: Yusuke Sekikawa; Jun Nagata; Itsumi Araki; Andreu Girbau
Venue: WACV 2026 (main)

Abstract: Linear attention is sparse, recurrent, and GPU-parallel; these are essential features for processing sparse data from event-based cameras. We argue that locality is missing to efficiently model event-to-event relationships for continuous spatiotemporal perception. We propose CoL^2\mkern-3muA by introducing locality into linear attention without using a computationally demanding convolution operation. The key idea for the convolution-free formulation is restricting the positional embedding local convolutional kernel into the special class which can be decomposed into two global positional embeddings which can be absorbed into query and key; this replaces convolution with a local sum. To the best of our knowledge, CoL^2\mkern-3muAis the first to equip sparsity, recurrence, GPU parallelism and locality, simultaneously. We demonstrate CoL^2\mkern-3muA's effectiveness on dense, high-temporal-resolution (> 1000 fps ) prediction task from events, demonstrating real-time capability while maintaining competitive results over the conventional method.
From Prompt to Production: Automating Brand-Safe Marketing Imagery with Text-to-Image Models
Authors: Parmida Atighehchian; Henry Wang; Andrei Kapustin; Boris Lerner; Tiancheng Jiang; Taylor Jensen; Negin Sokhandan
Venue: WACV 2026 (main)

Abstract: Text-to-image models have made significant strides, producing impressive results in generating images from textual descriptions. However, creating a scalable pipeline for deploying these models in production remains a challenge. Achieving the right balance between automation and human feedback is critical to maintain both scale and quality. While automation can handle large volumes, human oversight is still an essential component to ensure that the generated images meet the desired standards and are aligned with the creative vision. This paper presents a new pipeline that offers a fully automated, scalable solution for generating marketing images of commercial products using text-to-image models. The proposed system maintains the quality and fidelity of images, while also introducing sufficient creative variation to adhere to marketing guidelines. By streamlining this process, we ensure a seamless blend of efficiency and human oversight, achieving a 30.77% increase in marketing object fidelity using DINOV2 and a 52.00% increase in human preference over the generated outcome.
2S-CEDiff: A Two-Stage Diffusion Framework for Generating High-Fidelity Contrast-Enhanced CT Images from Non-Contrast Scans
Authors: Yibang Wu; Tzung-Dau Wang; Shang-Hong Lai
Venue: WACV 2026 (main)

Abstract: Contrast-enhanced Computed Tomography (CT) plays a vital role in modern medical diagnostics, particularly in cardiovascular assessment. However, the use of intravenous contrast agents can pose potential health risks for vulnerable patient populations. To address this limitation, we present 2S-CEDiff, a clinically-inspired image translation framework that synthesizes high-fidelity contrast-enhanced 3D CT volumes from non-contrast inputs. Our method adopts a two-stage architecture. The first stage employs a 2.5D diffusion model to generate anatomically accurate slice-wise predictions, guided by cross-attention-based positional conditioning and structural priors derived from segmentation masks produced by a pre-trained TotalSegmentator model. The second stage involves applying a 3D UNet model trained with a multi-objective loss that jointly optimizes pixel-level fidelity and volumetric coherence.We evaluate 2S-CEDiff on an in-house paired 3D cardiac CT dataset, where it achieves state-of-the-art performance in PSNR (29.87), SSIM (0.89), and inter-slice coherence. Moreover, the synthesized contrast-enhanced images significantly enhance downstream anatomical segmentation accuracy, improving the overall Dice score to 0.9447 and yielding a substantial +0.0798 increase in myocardium segmentation performance with TotalSegmentator -- underscoring their clinical utility and translational potential.
Descrip3D: Enhancing Large Language Model-based 3D Scene Understanding with Object-Level Text Descriptions
Authors: Jintang Xue; Ganning Zhao; Jie-En Yao; Hong-En Chen; Yue Hu; Meida Chen; Suya You; C.-C. Jay Kuo
Venue: WACV 2026 (main)

Abstract: Understanding 3D scenes goes beyond simply recognizing objects; it requires reasoning about the spatial and semantic relationships between them. Current 3D scene-language models often struggle with this relational understanding, particularly when visual embeddings alone do not adequately convey the roles and interactions of objects. In this paper, we introduce Descrip3D, a novel and powerful framework that explicitly encodes the relationships between objects using natural language. Unlike previous methods that rely only on 2D and 3D embeddings, Descrip3D enhances each object with a textual description that captures both its intrinsic attributes and contextual relationships. These relational cues are incorporated into the model through a dual-level integration: embedding fusion and prompt-level injection. This allows for unified reasoning across various tasks such as grounding, captioning, and question answering, all without the need for task-specific heads or additional supervision. When evaluated on five benchmark datasets, including ScanRefer, Multi3DRefer, ScanQA, SQA3D, and Scan2Cap, Descrip3D consistently outperforms strong baseline models, demonstrating the effectiveness of language-guided relational representation for understanding complex indoor scenes.
Unconditional Priors Matter! Improving Conditional Generation of Fine-Tuned Diffusion Models
Authors: Prin Phunyaphibarn; Phillip Y. Lee; Jaihoon Kim; Minhyuk Sung
Venue: WACV 2026 (main)

Abstract: Classifier-Free Guidance (CFG) is a fundamental technique in training conditional diffusion models. The common practice for CFG-based training is to use a single network to learn both conditional and unconditional noise prediction, with a small dropout rate for conditioning. However, we observe that the joint learning of unconditional noise with limited bandwidth in training results in poor priors for the unconditional case. More importantly, these poor unconditional noise predictions become a serious reason for degrading the quality of conditional generation. Inspired by the fact that most CFG-based conditional models are trained by fine-tuning a base model with better unconditional generation, we first show that simply replacing the unconditional noise in CFG with that predicted by the base model can significantly improve conditional generation. Furthermore, we show that a diffusion model other than the one the fine-tuned model was trained on can be used for unconditional noise replacement. We experimentally verify our claim with a range of CFG-based conditional models for both image and video generation, including Zero-1-to-3, Versatile Diffusion, DiT, DynamiCrafter, and InstructPix2Pix.
MuseDance: A Diffusion-based Music-Driven Image Animation System
Authors: Zhikang Dong; Weituo Hao; Ju-Chiang Wang; Peng Zhang; Pawel Polak
Venue: WACV 2026 (main)

Abstract: Image animation is a rapidly developing area in multimodal research, with a focus on generating videos from reference images. While much of the work has emphasized generic video generation guided by text, music-driven dance image animation remains underexplored. In this paper, we introduce MuseDance, an end-to-end model that animates reference images using both music and text inputs. By integrating music as a conditioning modality, MuseDance generates personalized videos that not only adhere to textual descriptions but also synchronize character movements with the rhythm and dynamics of the music. Unlike existing methods, MuseDance eliminates the need for explicit motion guidance, such as pose sequences or depth maps, reducing the complexity of video generation while enhancing accessibility and flexibility. To support further research in this field, we present a new multimodal dataset comprising of 3,122 dance videos, each paired with the corresponding background music and text descriptions. Our approach leverages diffusion-based methods to achieve robust generalization, precise control, and temporal consistency, setting a new benchmark for the task of music-driven image animation. The dataset of this work is available at https://github.com/Dongzhikang/musedance.
1LoRA: Summation Compression for Very Low-Rank Adaptation
Authors: Alessio Quercia; Zhuo Cao; Arya Bangun; Richard D. Paul; Abigail Morrison; Ira Assent; Hanno Scharr
Venue: WACV 2026 (main)

Abstract: Parameter-Efficient Fine-Tuning (PEFT) methods have transformed the approach to fine-tuning large models for downstream tasks by enabling the adjustment of significantly fewer parameters than those in the original model matrices. In this work, we study the "very low rank regime", where we fine-tune the lowest amount of parameters per linear layer for each considered PEFT method. We propose 1LoRA (Summation Low-Rank Adaptation), a compute, parameter and memory efficient fine-tuning method which uses the feature sum as fixed compression and a single trainable vector as decompression. Differently from state-of-the-art PEFT methods like LoRA, VeRA, and the recent MoRA, 1LoRA uses fewer parameters per layer, reducing the memory footprint and the computational cost. We extensively evaluate our method against state-of-the-art PEFT methods on multiple fine-tuning tasks, and show that our method not only outperforms them, but is also more parameter, memory and computationally efficient. Moreover, thanks to its memory efficiency, 1LoRA allows to fine-tune more evenly across layers, instead of focusing on specific ones (e.g. attention layers), improving performance further.
MaxInfo: A Training-Free Key-Frame Selection Method Using Maximum Volume for Enhanced Video Understanding
Authors: Pengyi Li; Irina Abdullaeva; Alexander Gambashidze; Andrey Kuznetsov; Ivan Oseledets
Venue: WACV 2026 (main)

Abstract: Modern Video Large Language Models (VLLMs) often rely on uniform frame sampling for video understanding, but this approach frequently fails to capture critical information due to frame redundancy and variations in video content. We propose MaxInfo, the first training-free method based on the maximum volume principle, which is available in Fast and Slow versions and a Chunk-based version that selects and retains the most representative frames from a video. By maximizing the geometric volume formed by selected embeddings, MaxInfo ensures that the chosen frames cover the most informative regions of the embedding space, effectively reducing redundancy while preserving diversity. This method enhances the quality of input representations and improves long video comprehension performance across benchmarks. For instance, MaxInfo achieves a 3.28% improvement on LongVideoBench and a 6.4% improvement on EgoSchema for LLaVA-Video-7B. Moreover, MaxInfo boosts LongVideoBench performance by 3.47% on LLaVA-Video-72B and 3.44% on MiniCPM4.5. The approach is simple to implement and works with existing VLLMs without the need for additional training and very lower latency, making it a practical and effective alternative to traditional uniform sampling methods. Our code are available at https://github.com/FusionBrainLab/MaxInfo.git
Conditional Text-to-Image Generation with Reference Guidance
Authors: Taewook Kim; Ze Wang; Zhengyuan Yang; Jiang Wang; Lijuan Wang; Zicheng Liu; Qiang Qiu
Venue: WACV 2026 (main)

Abstract: Text-to-image diffusion models have demonstrated tremendous success in synthesizing visually stunning images given textual instructions. Despite remarkable progress in creating high-fidelity visuals, text-to-image models can still struggle with precisely rendering subjects, such as text spelling. To address this challenge, this paper explores using additional conditions of an image that provides visual guidance of the particular subjects for diffusion models to generate. In addition, this reference condition empowers the model to be conditioned in ways that the vocabularies of the text tokenizer cannot adequately represent, and further extends the model's generalization to novel capabilities such as generating non-English text spellings. We develop several small-scale expert plugins that efficiently endow a Stable Diffusion model with the capability to take different references. Each plugin is trained with auxiliary networks and loss functions customized for applications such as English scene-text generation, multi-lingual scene-text generation, and logo-image generation. Our expert plugins demonstrate superior results than the existing methods on all tasks, each containing only 28.55M trainable parameters.
SDT-6D: Fully Sparse Depth-Transformer for Staged End-to-End 6D Pose Estimation in Industrial Multi-View Bin Picking
Authors: Nico Leuze; Maximilian Hoh; Samed Doğan; Nicolas R.-Peña; Alfred Schoettl
Venue: WACV 2026 (main)

Abstract: Accurately recovering 6D poses in densely packed industrial bin-picking environments remain a serious challenge, owing to occlusions, reflections, and textureless parts. We introduce a holistic depth-only 6D pose estimation approach that fuses multi-view depth maps into either a fine-grained 3D point cloud in its vanilla version, or a sparse Truncated Signed Distance Field (TSDF). At the core of our framework lies a staged heatmap mechanism that yields scene-adaptive attention priors across different resolutions, steering computation toward foreground regions, thus keeping memory requirements at high resolutions feasible. Along, we propose a density-aware sparse transformer block that dynamically attends to (self-) occlusions and the non-uniform distribution of 3D data. While sparse 3D approaches has proven effective for long-range perception, its potential in close-range robotic applications remains underexplored. Our framework operates fully sparse, enabling high-resolution volumetric representations to capture fine geometric details crucial for accurate pose estimation in clutter. Our method processes the entire scene integrally, predicting the 6D pose via a novel per-voxel voting strategy, allowing simultaneous pose predictions for an arbitrary number of target objects. We validate our method on the recently published IPD and MV-YCB multi-view datasets, demonstrating competitive performance in heavily cluttered industrial and household bin picking scenarios.
VideoSketcher: A Training-Free Approach for Coherent Video Sketch Transfer
Authors: Huining Li; Bangzhen Liu; Rui Yang; Yang Zhou; Chenshu Xu; Xufang Pang; Shengfeng He
Venue: WACV 2026 (main)

Abstract: Generating high-quality sketches from video requires a nuanced understanding of semantic content and visual structure, particularly for complex scenes across diverse sketch styles. Efficient and flexible video-to-sketch style transformation remains a significant challenge. We introduce VideoSketcher, a training-free framework for style-controllable sketch video generation that preserves frame structure while applying specified sketch aesthetics. Leveraging text-to-image diffusion models, VideoSketcher utilizes strong semantic priors without the need for extensive training. Our approach enforces temporal consistency by retaining latent information across frames and employs a Time-Linked Attention mechanism to capture structural elements from the source video and inject stylistic information from the reference image. To bridge the semantic gap between sketches and original video content, we introduce Sketch Directive Amplification for selective transfer of stylistic features. Additionally, a Stroke Graph Regularization strategy, comprising line and point loss, refines line consistency in the latent space. Extensive experiments validate VideoSketcher's superior temporal stability and fidelity across diverse sketch styles and content. Video demos can be found in the supplementary materials.
From Darkness to Detail: Frequency-Aware SSMs for Low-Light Vision
Authors: Eashan Adhikarla; Kai Zhang; Gong Chen; John Nicholson; Brian D. Davison
Venue: WACV 2026 (main)

Abstract: Low-light image enhancement remains a persistent challenge in computer vision, where state-of-the-art models are often hampered by hardware constraints and computational inefficiency, particularly at high resolutions. While foundational architectures like transformers and diffusion models have advanced the field, their computational complexity limits their deployment on edge devices. We introduce ExpoMamba, a novel architecture that integrates a frequency-aware state-space model within a modified U-Net. ExpoMamba is designed to address mixed-exposure challenges by decoupling the modeling of amplitude (intensity) and phase (structure) in the frequency domain. This allows for targeted enhancement, making it highly effective for real-time applications, including downstream tasks like object detection and segmentation. Our experiments on six benchmark datasets show that ExpoMamba is up to 2-3x faster than competing models and achieves a 6.8% PSNR improvement, establishing a new state-of-the-art in efficient, high-quality low-light enhancement.
Deepfake Detection that Generalizes Across Benchmarks
Authors: Andrii Yermakov; Jan Cech; Jiri Matas; Mario Fritz
Venue: WACV 2026 (main)

Abstract: The generalization of deepfake detectors to unseen manipulation techniques remains a challenge for practical deployment. Although many approaches adapt foundation models by introducing significant architectural complexity, this work demonstrates that robust generalization is achievable through a parameter-efficient adaptation of one of the foundational pre-trained vision encoders. The proposed method, GenD, fine-tunes only the Layer Normalization parameters (0.03 %of the total) and enhances generalization by enforcing a hyperspherical feature manifold using L2 normalization and metric learning on it. We conducted an extensive evaluation on 14 benchmark datasets spanning from 2019 to 2025. The proposed method achieves state-of-the-art performance, outperforming more complex, recent approaches in average cross-dataset AUROC. Our analysis yields two primary findings for the field: 1) training on paired real-fake data from the same source video is essential for mitigating shortcut learning and improving generalization, and 2) detection difficulty on academic datasets has not strictly increased over time, with models trained on older, diverse datasets showing strong generalization capabilities. This work delivers a computationally efficient and reproducible method, proving that state-of-the-art generalization is attainable by making targeted, minimal changes to a pre-trained foundational image encoder model. The code is at: https://github.com/yermandy/GenD
Mean-Shift Distillation for Diffusion Mode Seeking
Authors: Vikas Thamizharasan; Nikitas Chatzis; Iliyan Georgiev; Matthew Fisher; Evangelos Kalogerakis; Difan Liu; Nanxuan Zhao; Michal Lukáč
Venue: WACV 2026 (main)

Abstract: We present mean-shift distillation, a novel diffusion distillation technique that provides a provably good proxy for the gradient of the diffusion output distribution. This is derived directly from mean-shift mode seeking on the distribution, and we show that its extrema are aligned with the modes. We further derive an efficient product distribution sampling procedure to evaluate the gradient. Our method is formulated as a drop-in replacement for score distillation sampling (SDS), requiring neither model retraining nor extensive modification of the sampling procedure. We show that it exhibits superior mode alignment as well as improved convergence in both synthetic and practical setups, yielding higher-fidelity results when applied to both text-to-image and text-to-3D applications with Stable Diffusion.
ISALux: Illumination and Semantics-Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement
Authors: Raul Balmez; Alexandru Brateanu; Ciprian Orhei; Codruta O. Ancuti; Cosmin Ancuti
Venue: WACV 2026 (main)

Abstract: In this paper we introduce ISALux, a novel transformer-based approach for Low-Light Image Enhancement (LLIE) that seamlessly integrates illumination and semantic priors. Our architecture includes an original self-attention block, Hybrid Illumination and Semantics-Aware Multi-Headed Self-Attention (HISA-MSA), which integrates illumination and semantic segmentation maps for enhanced feature extraction. ISALux employs two self-attention modules to independently process illumination and semantic features, selectively enriching each other to regulate luminance and highlight structural variations in real-world scenarios. A Mixture of Experts (MoE)-based Feed-Forward Network (FFN) enhances contextual learning, with a gating mechanism conditionally activating the top K experts for specialized processing. To address overfitting in LLIE methods caused by distinct light patterns in benchmarking datasets, we enhance the HISA-MSA module with low-rank matrix adaptations (LoRA). Extensive qualitative and quantitative evaluations across multiple specialized datasets demonstrate that ISALux is competitive with state-of-the-art (SOTA) methods. Additionally, an ablation study highlights the contribution of each component in the proposed model.