Search: diffusion models

More than 1,000 papers match: here are the 1,000 most recent, ranked by relevance.

• The Surprising Effectiveness of Video Diffusion Models for Hand Motion Reconstruction

  Yuxi Wang, Chengkai Jin, Yufei Liu, Wenqi Ouyang, Tianyi Wei, Zhiwei Zeng, Siyuan Huang, Zhiqi Shen, Xingang Pan · 30 June 2026 · Human Pose and Action Recognition

  4D hand motion reconstruction from egocentric video is bottlenecked by clear limitations of existing methods: image-based pipelines depend on a detector that fails under heavy occlusion, while video-based methods rely on temporal modules learned only from scarce hand-pose annotations, a narrow signa…

• T2LDM++: A Self-Conditioned Representation Guided Diffusion Model for Realistic Text-to-LiDAR Scene Generation

  Wentao Qu, Qi Zhang, Chenxu Wang, Guofeng Mei, Yongfei Liu, Xiaoshui Huang, Gim Hee Lee, Liang Xiao · 30 June 2026 · Generative Adversarial Networks and Image Synthesis

  Recent progress in Text-to-Image generation benefits from large-scale Text-Image pairs. However, the scarcity of Text-LiDAR pairs often causes over-smoothed scenes and limited controllability. In this paper, we rethink the limitations of Text-LiDAR generation task, focusing on alleviating insufficie…

• FDM-MFVT: Few-step Sampling Diffusion Model for Mask-Free Virtual Try-On

  Jiaxin Liu, Xiaoye Liang, Lai Jiang, Mai Xu, Jun Liu · 30 June 2026 · Generative Adversarial Networks and Image Synthesis

  Image-based Virtual Try-On (IVTON) has greatly advanced through diffusion models, yet existing methods require many sampling steps and depend on masks with costly auxiliary networks. In addition, the absence of large-scale mask-free paired datasets further limits the development of mask-free IVTON. …

• SATB-VR: Training Few-Step Video Restoration Diffusion Model using SNR-Aware Trajectory Blending

  Haoran Bai, Xiaoxu Chen, Xiaoyu Liu, Zongsheng Yue, Sibin Deng, Wangmeng Zuo, Ying Chen · 30 June 2026 · Generative Adversarial Networks and Image Synthesis

  While diffusion models excel in video restoration, their reliance on extensive iterative steps limits efficiency. Conversely, aggressive single-step distillation often compromises fine texture recovery. To achieve an optimal balance, we present SATB-VR, a few-step paradigm that jump-starts the denoi…

• Diffusion Models Observe Only Gradients: A Geometric Perspective on Score Matching Errors

  Na\"il B. Khelifa, Richard E. Turner, Ramji Venkataramanan · 30 June 2026 · Stochastic Gradient Optimization Techniques

  Score-based diffusion models are typically trained by minimizing the $L^2$ score matching error, and standard theoretical analyses rely on this quantity to bound the sampling discrepancy between the learned and target distributions. We show the $L^2$ score error is not the right intrinsic measure of…

• General and Efficient Steering of Diffusion Models

  Qingsong Wang, Mikhail Belkin, Yusu Wang · 30 June 2026 · Generative Adversarial Networks and Image Synthesis

  Steering diffusion models toward conditions unseen during training typically requires either retraining with conditional inputs or per-step gradient computations, both of which incur substantial computational overhead. We present Noise-Aligned RFM Steering (NA-RFM), a general recipe for efficiently …

• Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation

  Yi Zhang, Peng Wang, Difan Zou · 30 June 2026 · Model Reduction and Neural Networks

  Modeling physical systems in a generative manner offers several advantages, including the ability to handle partial observations, generate diverse solutions, and address both forward and inverse problems. Recently, diffusion models have gained increasing attention in the modeling of physical systems…

• Lie Group Diffusion Models for Hardware-Aware Quantum Circuit Synthesis

  Jyotirmai Singh · 30 June 2026 · Quantum Computing Algorithms and Architecture

  An important task in quantum computing is unitary circuit synthesis compatible with physical hardware constraints. This problem has a natural hybrid structure as local single-qubit gates are continuous variables on the Lie group $SU(2)$ while the entangling circuit structure is discrete and hardware…

• Entropy-Regularized Reinforcement Learning for Linear-Quadratic Stackelberg Differential Games in Regime-Switching Diffusion Models

  Congde Hu, Danping Li, Lin Xu, Wenying Xu · 30 June 2026 · Model Reduction and Neural Networks

  Stackelberg differential games (SDGs) provide a powerful framework for hierarchical decision-making in stochastic and continuous-time environments, yet their solution remains computationally challenging due to the complexity of traditional dynamic programming and Hamilton-Jacobi-Bellman-Isaacs (HJBI…

• Diffusion Model Attribution via Spectral Coupling of Denoiser Responses

  Pragati Shuddhodhan Meshram, Varun Chandrasekaran · 29 June 2026 · Cell Image Analysis Techniques

  Attributing a generated image to its source diffusion model is a fundamental challenge in provenance verification and intellectual property protection. This problem is particularly difficult because diffusion models trained on different datasets can converge to similar score functions and thus simil…

• SIFT: Self-Imagination Fine-Tuning for Physically Plausible Motion in Video Diffusion Models

  Ruoyu Wang, Jialun Liu, Huayang Huang, Haibin Huang, Jiepeng Wang, Chi Zhang, Xuelong Li, Yu Wu · 29 June 2026 · Generative Adversarial Networks and Image Synthesis

  Recent advances in video diffusion models have greatly improved visual fidelity, yet their generated motions often violate physical plausibility. We observe a common kinematic failure, "motion entanglement", the unintended coupling of independent motion sources, such as camera movement and object mo…

• A Gaussian Perspective for Distributional Discrepancy in Generative Diffusion Models

  Qiang Sun, H. Vincent Poor, Wenyi Zhang · 29 June 2026 · Generative Adversarial Networks and Image Synthesis

  This paper introduces an analytical approach to quantifying and optimizing the distributional discrepancy in generative diffusion models. For a multivariate Gaussian source, we explicitly derive the closed-form evolution trajectory and the resulting Kullback-Leibler (KL) divergence between the distr…

• VGB for Masked Diffusion Model: Efficient Test-time Scaling for Reward Satisfaction and Sample Editing

  Kijung Jeon, Thuy-Duong Vuong, Molei Tao · 29 June 2026 · Generative Adversarial Networks and Image Synthesis

  Inference-time scaling is a promising paradigm to improve generative models, especially when outputs must satisfy structural constraints or optimize downstream rewards. We consider Masked Diffusion Model (MDM) and introduce MDM-VGB, a discrete diffusion sampler that augments unmasking generation wit…

• Quantum Generative Diffusion Model for Real-World Time Series

  Jack Waller, Filippo Caruso, Dimitrios Makris, Rajagopal Nilavalan, Xing Liang · 29 June 2026 · Quantum Computing Algorithms and Architecture

  Generative models have achieved remarkable success in data synthesis, though recent advances driven by increasing model scale have introduced challenges in computational cost and efficiency. Quantum machine learning offers a promising alternative, representing complex data distributions using compac…

• Class-frequency Guided Noise Schedule for Diffusion Models

  Jiequan Cui, Beier Zhu, Qingshan Xu, Xiaojuan Qi, Bei Yu, Hanwang Zhang · 29 June 2026 · Generative Adversarial Networks and Image Synthesis

  In this paper, we are the first to examine the correlations between class frequency and the multi-scale noise schedule within diffusion models. For score-based generative models, low-density regions often lead to inaccurately estimated scores, thereby compromising the generation quality. Although th…

• LearniBridge: Learnable Calibration of Feature Caching for Diffusion Models Acceleration

  Xuyue Huang, Zhe Chen, Wang Shen, Xiao-Ping Zhang · 26 June 2026 · Generative Adversarial Networks and Image Synthesis

  Diffusion Transformers (DiTs) have driven substantial progress in image and video generation but suffer from prohibitive computational costs. Feature caching accelerates inference by reusing intermediate representations. Existing methods rely on historical features for implementation simplicity, yet…

• MLFFM-SegDiff: A Multi-Level Feature Fusion Diffusion Model for Skin Lesion Segmentation

  Jingjun Gu, Chaojie Shen, Yifeng Cao, Wei Zhang, Yiliu Li, Aobo Fan · 26 June 2026 · Cutaneous Melanoma Detection and Management

  Skin lesion segmentation is a key task in computer-aided dermatological diagnosis, where accuracy directly impacts downstream analysis and disease classification. However, dermoscopic images are challenging due to blurred boundaries, low contrast, large shape variations, and artifacts such as hair a…

• Sampling sea state using a diffusion model

  Jiarong Wu, Bertrand Chapron, Laure Zanna · 26 June 2026 · Ocean Waves and Remote Sensing

  Sea state prediction is essential for operational maritime applications and coupled earth system modeling, yet current spectral wave models remain computationally prohibitive for many use cases, including online coupling to climate simulations and making probabilistic (ensemble-based) predictions. W…

• TEMPO-Diffusion: Temporally Exposed Malicious Poisoning of Diffusion Models

  William Aiken, Paula Branco, Guy-Vincent Jourdan, Iosif-Viorel Onut · 26 June 2026 · Adversarial Robustness in Machine Learning

  Noise-based backdoor attacks on diffusion models typically rely on input-time trigger injection, untargeted activation, and out-of-distribution target generation. Such assumptions reduce both the stealthiness and the practical relevance of these attacks. In this work, we present TEMPO-Diffusion, a t…

• TensorLDM: A Component-Wise Latent Diffusion Model for Volumetric DTI Reconstruction from Sparse DWIs

  Junhyeok Lee, Kyu Sung Choi · 25 June 2026 · Advanced Neuroimaging Techniques and Applications

  Reconstructing diffusion tensors from sparse DWIs is critical for accelerating Diffusion Tensor Imaging (DTI) in clinical settings, yet current deep learning approaches frequently yield anatomically inconsistent or physically implausible tensors. We introduce TensorLDM, a component-wise latent diffu…

• From Uncertain to Safe: Conformal Adaptation of Diffusion Models for Safe PDE Control

  Peiyan Hu, Xiaowei Qian, Wenhao Deng, Rui Wang, Haodong Feng, Ruiqi Feng, Tao Zhang, Long Wei, Yue Wang, Zhi-Ming Ma, Tailin Wu · 25 June 2026 · Advanced Control Systems Optimization

  The application of deep learning for partial differential equation (PDE)-constrained control is gaining increasing attention. However, existing methods rarely consider safety requirements crucial in real-world applications. To address this limitation, we propose Safe Diffusion Models for PDE Control…

• Anatomically-conditioned Latent Diffusion Model for Data-Efficient Few-Shot Cross-Domain 3D Glioma MRI Synthesis

  Salman Shaik, Truong Thanh Hung Nguyen, Hung Cao · 25 June 2026 · Glioma Diagnosis and Treatment

  Accurate classification of diffuse gliomas is often hindered by domain shifts across centers and a lack of large, annotated datasets. We propose the Anatomically-conditioned Latent Diffusion Model (ALDM), a novel framework for data-efficient, few-shot 3D volumetric MRI synthesis. ALDM utilizes a two…

• Improved Large Language Diffusion Models

  Shen Nie, Qiyang Min, Shaoxuan Xu, Zihao Huang, Yuxuan Song, Yong Shan, Yankai Lin, Wayne Xin Zhao, Chongxuan Li, Ji-Rong Wen · 25 June 2026 · Large Language Models

  Modern large language models are predominantly trained with autoregressive factorization and causal attention. We present \emph{iLLaDA}, an 8B masked diffusion language model trained from scratch with fully bidirectional attention. iLLaDA keeps the masked diffusion objective throughout pre-training …

• Cyclic Denoising Reveals Ultrastable Memories in Diffusion Models

  Rishabh Sharma, Stefano Martiniani · 24 June 2026 · Generative Adversarial Networks and Image Synthesis

  We introduce cyclic denoising -- repeated forward and reverse diffusion at controlled noise amplitudes -- as an extraction attack for image diffusion models. Inspired by random organization in disordered solids, cyclic denoising exposes regions of the learned distribution that are largely inaccessib…

• EMFusion: Uncertainty-Aware Conditional Diffusion Model for Multivariate Narrow-band Exposure Forecasting

  Zijiang Yan, Yixiang Huang, Jianhua Pei, Hina Tabassum, Luca Chiaraviglio · 24 June 2026 · Traffic Prediction and Management Techniques

  The rapid growth in wireless infrastructure has increased the need to accurately estimate and forecast electromagnetic field (EMF) levels to ensure ongoing compliance, assess potential health impacts, and support efficient network planning. While existing studies rely on univariate forecasting of wi…