GenAI4ELab/papercli-papers huggingface dataset:
Gyro-based Deep Video Deblurring
Authors: Jaesung Rim; Woohyeok Kim; Haeyun Lee; Heemin Yang; Ke Wang; Sunghyun Cho
Venue: CVPR 2026 (main)

Abstract: Modern cameras, such as smartphone cameras and DSLRs, are equipped with gyro sensors that measure motion of the camera. While the motion information is valuable for deblurring, gyro-based deblurring has not been widely studied, particularly for video. A few gyro-based video deblurring methods have been proposed, but they exhibit inherent limitations. First, gyro sensors capture only rotational motion, leading these methods to ignore translational motion. Second, their dependence on simplified blur models and deconvolution-based solutions restricts overall performance. To address these limitations, we introduce GyroDVD, the first learning-based framework for gyro-based video deblurring. We propose a novel blur kernel construction scheme that jointly accounts for rotational and translational motion. A video deblurring network then restores sharp videos by exploiting the constructed kernels together with the video frames. For training and evaluation, we introduce the GyroVD dataset, a large-scale and realistic dataset specifically designed for gyro-based deblurring. Extensive experiments demonstrate that our method significantly outperforms prior gyro-based image and video deblurring methods.
Detect Anything via Next Point Prediction
Authors: Qing Jiang; Junan Huo; Xingyu Chen; Yuda Xiong; Zhaoyang Zeng; Yihao Chen; Tianhe Ren; Junzhi Yu; Lei Zhang
Venue: CVPR 2026 (main)

Abstract: Object detection has long been dominated by traditional coordinate regression-based models, such as YOLO, DETR, and Grounding DINO. Although recent efforts have attempted to leverage MLLMs to tackle this task, they face challenges like low recall rate, duplicate predictions, coordinate misalignment, etc. In this work, we bridge this gap and propose Rex-Omni, a 3B-scale MLLM that achieves state-of-the-art object perception performance. On benchmarks like COCO and LVIS, Rex-Omni attains performance comparable to or exceeding regression-based models (e.g., DINO, Grounding DINO) in a zero-shot setting. This is enabled by three key designs: 1) Task Formulation: we use special tokens to represent quantized coordinates from 0 to 999, reducing the model's learning difficulty and improving token efficiency for coordinate prediction; 2) Data Engines: we construct multiple data engines to generate high-quality grounding, referring, and pointing data, providing semantically rich supervision for training; 3) Training Pipelines: we employ a two-stage training process, combining supervised fine-tuning on 22 million data with GRPO-based reinforcement post-training. This RL post-training leverages geometry-aware rewards to effectively bridge the discrete-to-continuous coordinate prediction gap, improve box accuracy, and mitigate undesirable behaviors like duplicate predictions that stem from the teacher-guided nature of the initial SFT stage. Beyond conventional detection, Rex-Omni's inherent language understanding enables versatile capabilities such as object referring, pointing, visual prompting, GUI grounding, spatial referring, OCR and key-pointing, all systematically evaluated on dedicated benchmarks. We believe that Rex-Omni paves the way for more versatile and language-aware visual perception systems.
VidEoMT: Your ViT is Secretly Also a Video Segmentation Model
Authors: Narges Norouzi; Idil Esen Zulfikar; Niccolò Cavagnero; Tommie Kerssies; Bastian Leibe; Gijs Dubbelman; Daan de Geus
Venue: CVPR 2026 (main)

Abstract: Existing online video segmentation models typically combine a per-frame segmenter with complex specialized tracking modules. While effective, these modules introduce significant architectural complexity and computational overhead. Recent studies suggest that plain Vision Transformer (ViT) encoders, when scaled with sufficient capacity and large-scale pre-training, can conduct accurate image segmentation without requiring specialized modules. Motivated by this observation, we propose the Video Encoder-only Mask Transformer (VidEoMT), a simple encoder-only video segmentation model that eliminates the need for dedicated tracking modules. To enable temporal modeling in an encoder-only ViT, VidEoMT introduces a lightweight query propagation mechanism that carries information across frames by reusing queries from the previous frame. To balance this with adaptability to new content, it employs a query fusion strategy that combines the propagated queries with a set of temporally-agnostic learned queries. As a result, VidEoMT attains the benefits of a tracker without added complexity, achieving competitive accuracy while being 5x-10x faster, running at up to 160 FPS with a ViT-L backbone. Code: \href https://www.tue-mps.org/videomt/  https://www.tue-mps.org/videomt/ .
PSDesigner: Automated Graphic Design with a Human-Like Creative Workflow
Authors: Xincheng Shuai; Song Tang; Yutong Huang; Henghui Ding; Dacheng Tao
Venue: CVPR 2026 (main)

Abstract: Graphic design is a creative and innovative process that plays a crucial role in applications such as e-commerce and advertising. However, developing an automated design system that can faithfully translate user intentions into editable design files remains an open challenge. Although recent studies have leveraged powerful text-to-image models and MLLMs to assist graphic design, they typically simplify professional workflows, resulting in limited flexibility and intuitiveness. To address these limitations, we propose ***PSDesigner***, an automated graphic design system that emulates the creative workflow of human designers. Building upon multiple specialized components, ***PSDesigner*** collects theme-related assets based on user instructions, and autonomously infers and executes tool calls to manipulate design files, such as integrating new assets or refining inferior elements. To endow the system with strong tool-use capabilities, we construct a design dataset, ***CreativePSD***, which contains a large amount of high-quality PSD design files annotated with operation traces across a wide range of design scenarios and artistic styles, enabling models to learn expert design procedures. Extensive experiments demonstrate that ***PSDesigner*** outperforms existing methods across diverse graphic design tasks, empowering non-specialists to conveniently create production-quality designs.
VAST: Video Ability-Stratified Taxonomy for Data-Efficient Video Reasoning
Authors: Zhongan Wang; Xiaoyu Wen; Lingxiao Du; Kun Li; Zhiliang Wu; Xingcheng Xu; Qiaosheng Zhang; Chaochao Lu; Hehe Fan
Venue: CVPR 2026 (main)

Abstract: Reinforcement learning (RL) has emerged as an effective approach for improving video reasoning in multimodal large language models (MLLMs). However, existing methods remain inefficient for two reasons. First, training data are typically organized by task formats rather than underlying reasoning abilities, creating a mismatch that encourages models to learn task-specific patterns instead of transferable capabilities. As a result, improving ability generalization requires broad coverage over many ability-task combinations, making RL training costly. Second, prior work often compensates for this inefficiency with increasingly complex algorithmic designs, such as specialized temporal architectures or multi-objective reward frameworks, which further complicate training. To address these issues, we present VAST, a cognitive taxonomy and data organization framework that structures video understanding into three layers: Perception, Reasoning, and Cognition. Based on this taxonomy, we construct VAST-15K for training and VAST-Bench for evaluation. We further introduce VideoVAST, a reinforcement learning framework that uses consistency rewards to encourage alignment between reasoning traces and final answers, without architectural modifications. Experiments show that VideoVAST achieves 66.3% accuracy on MVBench and 57.4% on VAST-Bench, compared with 62.7% and 54.3% for Video-R1, while using approximately 72% fewer GPU hours and 96% fewer training samples under the same training settings. Code and resources are available at https://zhongan-wang.github.io/VAST.
HQC-NBV: A Hybrid Quantum-Classical View Planning Approach
Authors: Xiaotong Yu; Chang Wen Chen
Venue: CVPR 2026 (main)

Abstract: Efficient view planning is a fundamental challenge in computer vision and robotic perception, critical for tasks ranging from search and rescue operations to autonomous navigation. While classical approaches, including sampling-based and deterministic methods, have shown promise in planning camera viewpoints for scene exploration, they often struggle with computational scalability and solution optimality in complex settings. This study introduces HQC-NBV, a hybrid quantum-classical framework for view planning that leverages quantum properties to efficiently explore the parameter space while maintaining robustness and scalability. We propose a specific Hamiltonian formulation with multi-component cost terms and a parameter-centric variational ansatz with bidirectional alternating entanglement patterns that capture the hierarchical dependencies between viewpoint parameters. Comprehensive experiments demonstrate that quantum-specific components provide measurable performance advantages. Compared to the classical methods, our approach achieves 7.9-49.2% higher exploration efficiency across diverse environments. Our analysis of entanglement architecture and coherence-preserving terms provides insights into the mechanisms of quantum advantage in robotic exploration tasks. This work represents a significant advancement in integrating quantum computing into robotic perception systems, offering a paradigm-shifting solution for various robot vision tasks.
TWEO: Transformers Without Extreme Outliers Enables FP8 Training And Quantization For Dummies
Authors: Guang Liang; Jie Shao; Ningyuan Tang; Xinyao Liu; Jianxin Wu
Venue: CVPR 2026 (main)

Abstract: Native FP8 support in modern hardware is essential for training large Transformers, but is severely hindered by extreme activation outliers. Existing solutions either rely on complex mixed-precision engineering or invasive architectural modifications. This paper fundamentally challenges the conventional wisdom that outliers are data-driven. We demonstrate that extreme outliers are a data-independent, mechanically-produced artifact of training, originating from specific structural properties of the weight matrices (i.e., colinearity). Based on this insight, we propose TWEO (Transformers Without Extreme Outliers), a novel, non-invasive loss function. TWEO effectively prevents extreme outliers via a very simple loss term, which reduces outliers from 10000+ to less than 20. TWEO then enables full-model FP8 pre-training with neither engineering tricks nor architectural changes for both LLM and ViT. When standard FP8 training catastrophically collapses, TWEO achieves performance comparable to the BF16 baseline while delivering a 36% increase in training throughput. Also, TWEO enables a new quantization paradigm. Hardware-friendly W8A8 per-tensor static quantization of LLMs, previously considered completely unusable due to outliers, achieves SOTA performance for the first time on TWEO-trained models.
Multi-view Consistent 3D Gaussian Head Avatars 'without' Multi-view Generation
Authors: Aviral Chharia; Fernando De la Torre
Venue: CVPR 2026 (main)

Abstract: High-fidelity 3D Gaussian head avatar generation is critical for applications such as AR/VR, telepresence, and digital humans. Existing methods depend on multi-view datasets, 3D captures, or intermediate 2D view synthesis. In contrast, we learn both conditional and unconditional 3D head models from randomly sampled 2D images alone, without using multi-view data, 3D supervision, or intermediate view generation. We introduce MVCHead, a single-shot state space model that enforces multi-view consistency (MVC) directly in the 3D representation while regressing 3D Gaussians under these constraints. At its core, we propose a Hierarchical State Space (HiSS) block that progressively refines Gaussians from coarse to fine, while capturing long-range dependencies. Within each HiSS block, we modify Mamba's standard unidirectional scan with the proposed Hierarchical Bi-directional State Scan (HiBiSS) that aligns recurrence with the axes along which multi-view inconsistencies are strongest. Finally, we design an SE(3) Multi-view Critic that judges whether a set of self-renders arises from a single underlying 3D configuration, rewarding cross-view pixel alignment without observing real multi-view pairs. MVCHead achieves state-of-the-art perceptual quality, surpasses prior methods in both texture and geometric consistency, and maintains comparable shape consistency. To demonstrate scalability, we release FaceGS-10K, the first large-scale dataset of ready-to-use 3D Gaussian head assets for training and evaluation of 3D head models. Project Page and code: https://humansensinglab.github.io/MVCHead/
HandVQA: Diagnosing and Improving Fine-Grained Spatial Reasoning about Hands in Vision-Language Models
Authors: MD Khalequzzaman Chowdhury Sayem; Mubarrat Tajoar Chowdhury; Yihalem Yimolal Tiruneh; Muneeb A. Khan; Muhammad Salman Ali; Binod Bhattarai; Seungryul Baek
Venue: CVPR 2026 (main)

Abstract: Understanding the fine-grained articulation of human hands is critical in high-stakes settings such as robot-assisted surgery, chip manufacturing, and AR/VR-based human-AI interaction. Despite achieving near-human performance on general vision-language benchmarks, current vision-language models (VLMs) struggle with fine-grained spatial reasoning, especially in interpreting complex, articulated hand poses. We introduce HandVQA, a large-scale diagnostic benchmark designed to evaluate VLMs' understanding of detailed hand anatomy through visual question answering. Built upon high-quality 3D hand datasets (FreiHAND, InterHand2.6M, FPHA), our benchmark includes over 1.6M controlled multiple-choice questions that probe spatial relationships between hand joints, such as angles, distances, and relative positions. We evaluate several state-of-the-art VLMs (LLaVA, DeepSeek and Qwen-VL) in both base and fine-tuned settings, using lightweight fine-tuning via LoRA. Our findings reveal systematic limitations in current models, including hallucinated finger parts, incorrect geometric interpretations, and poor generalization. HandVQA not only exposes these critical reasoning gaps but provides a validated path to improvement. We demonstrate that the 3D-grounded spatial knowledge learned from our benchmark transfers in a zero-shot setting, significantly improving accuracy of model on novel downstream tasks like hand gesture recognition (+10.33%) and hand-object interaction (+2.63%).
FALCON: False-Negative Aware Learning of Contrastive Negatives in Vision-Language Alignment
Authors: Myunsoo Kim; Seongwoong Shim; Byung-Jun Lee
Venue: CVPR 2026 (main)

Abstract: False negatives pose a critical challenge in vision-language pretraining (VLP) due to the many-to-many correspondence between images and texts in large-scale datasets. These false negatives introduce conflicting supervision signals that degrade the learned embedding space and diminish the effectiveness of hard negative sampling. In this paper, we propose FALCON (False-negative Aware Learning of COntrastive Negatives), a learning-based mini-batch construction strategy that adaptively balances the trade-off between hard and false negatives during VLP. Rather than relying on fixed heuristics, FALCON employs a negative mining scheduler that dynamically selects negative samples of appropriate hardness for each anchor instance during mini-batch construction, guided by a proxy for cross-modal alignment improvement. Experimental results demonstrate that FALCON significantly improves performance across three vision-language learning frameworks (ALBEF, BLIP-2, SigLIP-2) and a broad range of downstream tasks and evaluation settings, underscoring its effectiveness and robustness in mitigating the impact of false negatives.
D2T2 - Multimodal Automated Planning for Brachytherapy
Authors: Lance C. Moore; Aranyo Mitra; Ryan Truong; Karoline Kallis; Kelly Kisling; Sandra M. Meyers; Nuno Vasconcelos
Venue: CVPR 2026 (main)

Abstract: Brachytherapy is a complex radiation oncology problem that requires the simultaneous prediction of radiation dose, which is used for treatment planning, and a set of machine parameters, known as dwell times, used for treatment delivery. We propose Direct Dwell Time Transformer (D2T2), the first deep learning architecture that directly predicts dwell times during dose prediction. D2T2 is a two stage model, where the first stage predicts a vector of dwell times and the second implements the physical model of radiation delivery, namely a linear combination of radiation dose kernels. Besides the automatic prediction of dwell times, this has the benefit of constraining the model to make physically plausible dose predictions, when trained end-to-end. To enhance this training, we also propose a new loss function, denoted as the gamma loss, based on the prediction of the gamma index, which is the gold standard of dose comparisons. This is implemented by training a model to predict the latter using a synthetic dataset of groundtruth and predicted dose pairs. We train D2T2 on a large dataset of ~5,000 clinical brachytherapy plans---the largest such dataset to date---spanning gynecological, breast, and other treatment sites. Results demonstrate that D2T2 outperforms existing methods in both accuracy and speed. Notably, D2T2 produces deliverable plans and physically valid dose distributions in a single forward pass, for any application of brachytherapy, hours faster than manual planning and minutes faster than more recent automated methods.
Toward Early Quality Assessment of Text-to-Image Diffusion Models
Authors: Huanlei Guo; Hongxin Wei; Bingyi Jing
Venue: CVPR 2026 (main)

Abstract: Recent text-to-image (T2I) diffusion and flow-matching models can produce highly realistic images from natural language prompts. In practical scenarios, T2I systems are often run in a "generate--then--select" mode: many seeds are sampled and only a few images are kept for use. However, this pipeline is highly resource-intensive since each candidate requires tens to hundreds of denoising steps, and evaluation metrics such as CLIPScore and ImageReward are post-hoc. In this work, we address this inefficiency by introducing Probe-Select, a plug-in module that enables efficient evaluation of image quality within the generation process. We observe that certain intermediate denoiser activations, even at early timesteps, encode a stable coarse structure, object layout and spatial arrangement--that strongly correlates with final image fidelity. Probe-Select exploits this property by predicting final quality scores directly from early activations, allowing unpromising seeds to be terminated early. Across diffusion and flow-matching backbones, our experiments show that early evaluation at only 20% of the trajectory accurately ranks candidate seeds and enables selective continuation. This strategy reduces sampling cost by over 60% while improving the quality of the retained images, demonstrating that early structural signals can effectively guide selective generation without altering the underlying generative model. Code is available at https://github.com/Guhuary/ProbeSelect.
Semantic Foam: Unifying Spatial and Semantic Scene Decomposition
Authors: Amr Sharafeldin; Aryan Mikaeili; Thomas Walker; Shrisudhan Govindarajan; Daniel Rebain; Kwang Moo Yi; Andrea Tagliasacchi
Venue: CVPR 2026 (main)

Abstract: Modern scene reconstruction methods, such as 3D Gaussian Splatting, deliver photo-realistic novel view synthesis at real-time speeds, yet their adoption in interactive graphics applications has been limited.  A major bottleneck is the difficulty of interacting with these representations compared to traditional, human-authored 3D assets. While previous research has attempted to impose semantic decomposition on these models, significant challenges remain regarding segmentation quality and consistency. To address this, we introduce Semantic Foam, extending the recently proposed Radiant Foam representations to semantic decomposition tasks.Our approach integrates the natural spatial volumetric decomposition of Radiant Foam's Voronoi mesh with an explicit semantic feature field parameterized at the cell level. This explicit structure enables direct spatial regularization, which prevents artifacts caused by occlusion or inconsistent supervision across views - common pitfalls for other point-based representations. Experimental results show that our method achieves superior object-level segmentation performance compared to state-of-the-art methods like Gaussian Grouping and SAGA.
MMFace-DiT: A Dual-Stream Diffusion Transformer for High-Fidelity Multimodal Face Generation
Authors: Bharath Krishnamurthy; Ajita Rattani
Venue: CVPR 2026 (main)

Abstract: Recent multimodal face generation models address the spatial control limitations of text-to-image diffusion models by augmenting text-based conditioning with spatial priors such as segmentation masks, sketches, or edge maps. This multimodal fusion enables controllable synthesis aligned with both high-level semantic intent and low-level structural layout. However, most existing approaches typically extend pre-trained text-to-image pipelines by appending auxiliary control modules or stitching together separate uni-modal networks. These ad hoc designs inherit architectural constraints, duplicate parameters, and often fail under conflicting modalities or mismatched latent spaces, limiting their ability to perform synergistic fusion across semantic and spatial domains. We introduce MMFace-DiT, a unified dual-stream diffusion transformer engineered for synergistic multimodal face synthesis. Its core novelty lies in a dual-stream transformer block that processes spatial (mask/sketch) and semantic (text) tokens in parallel, deeply fusing them through a shared Rotary Position-Embedded (RoPE) Attention mechanism. This design prevents modal dominance and ensures strong adherence to both text and structural priors to achieve unprecedented spatial-semantic consistency for controllable face generation. Furthermore, a novel Modality Embedder enables a single cohesive model to dynamically adapt to varying spatial conditions without retraining. MMFace-DiT achieves a 40% improvement in visual fidelity and prompt alignment over six state-of-the-art multimodal face generation models, establishing a flexible new paradigm for end-to-end controllable generative modeling.