Flow-Based Generative Modeling

Overview

Flow-based generative modeling represents generation as a transformation of probability mass, often by integrating a continuous-time velocity field from noise to data or composing invertible normalizing-flow layers. Flow matching and rectified flow train velocity fields by regression along selected paths; optimal-transport couplings, stochastic interpolants, discrete Markov flows, and flow maps extend the toolkit to different data types and sampling budgets. Researchers apply these methods to images, language, molecules, physical fields, and robot actions, assessing fidelity and diversity alongside likelihood, constraint satisfaction, calibration, and runtime. A central challenge is achieving one- or few-step generation without sacrificing quality or mode coverage, while improving path design, robustness, geometric and physical consistency, and theoretical guarantees.

Latest papers

CardsList

563 papers125 papers in the last four weeks, up 131% on the four weeks before. 0.7% of all new papers.

  1. Attention-Guided Flow-Matching for Sparse 3D Geological Generation

    Apr 7, 2026Zhixiang Lu, Mengqi Han, Peixin Guo +4Flow-Based Generative ModelingFlow Matching

  2. Generative Path-Law Jump-Diffusion: Sequential MMD-Gradient Flows and Generalisation Bounds in Marcus-Signature RKHS

    Apr 6, 2026Daniel BlochFlow-Based Generative ModelingStochastic Differential Equations

  3. FlowLM: Few-Step Language Modeling via Diffusion-to-Flow Adaptation

    Apr 6, 2026Runzhe Zhang, Letian Chen, Wenpeng Zhang +2Language ModelingFlow-Based Generative Modeling

  4. FAVE: Flow-based Average Velocity Establishment for Sequential Recommendation

    Apr 6, 2026Ke Shi, Yao Zhang, Feng Guo +4Sequential RecommendationGenerative Recommendation

  5. Beyond Fixed Inference: Quantitative Flow Matching for Adaptive Image Denoising

    Apr 2, 2026Jigang Duan, Genwei Ma, Xu Jiang +3Image DenoisingFlow Matching

  6. SteerFlow: Steering Rectified Flows for Faithful Inversion-Based Image Editing

    Apr 2, 2026Thinh Dao, Zhen Wang, Kien T. Pham +1Text-Guided Image EditingFlow-Based Generative Modeling

  7. Flow Learners for PDEs: Toward a Physics-to-Physics Paradigm for Scientific Computing

    Apr 2, 2026Yilong Dai, Shengyu Chen, Xiaowei Jia +1Neural PDE SolversPDE Solving

  8. Hierarchical Discrete Flow Matching for Graph Generation

    Mar 31, 2026Yoann Boget, Pablo Strasser, Alexandros KalousisGraph GenerationFlow-Based Generative Modeling

  9. Stepwise Credit Assignment for GRPO on Flow-Matching Models

    Mar 30, 2026Yash Savani, Branislav Kveton, Yuchen Liu +5RL for Diffusion ModelsStep-Level Credit Assignment in RL

  10. ConceptWeaver: Weaving Disentangled Concepts with Flow

    Mar 30, 2026Jintao Chen, Aiming Hao, Xiaoqing Chen +6Subject-Driven Image GenerationCompositional Image Generation

  11. HiFlow: Tokenization-Free Scale-Wise Autoregressive Policy Learning via Flow Matching

    Mar 28, 2026Daichi Yashima, Koki Seno, Shuhei Kurita +2Visuomotor Policy LearningFlow-Based Policy Learning

  12. Hierarchy-Guided Topology Latent Flow for Molecular Graph Generation

    Mar 28, 2026Urvi Awasthi, Alexander Arjun Lobo, Leonid ZhukovMolecular Graph Generation3D Molecular Generation

  13. Property-Guided Molecular Generation and Optimization via Latent Flows

    Mar 27, 2026Alexander Arjun Lobo, Urvi Awasthi, Leonid ZhukovMolecular GenerationMolecular Optimization

  14. GVCC: Zero-Shot Video Compression via Codebook-Driven Stochastic Rectified Flow

    Mar 27, 2026Ziyue Zeng, Xun Su, Haoyuan Liu +3Video CompressionGenerative Video Compression

  15. OsteoFlow: Lyapunov-Guided Flow Distillation for Predicting Bone Remodeling after Mandibular Reconstruction

    Mar 23, 2026Hamidreza Aftabi, Faye Yu, Brooke Switzer +6Longitudinal Medical Image AnalysisFlow-Based Generative Modeling

  16. From Diffusion to Flow: Efficient Motion Generation in MotionGPT3

    Mar 23, 2026Jaymin Bhan, JiHong Jeon, SangYeop JeongText-to-Motion GenerationMotion Diffusion Models

  17. SetFlow: Generating Structured Sets of Representations for Multiple Instance Learning

    Mar 20, 2026Nikola Jovišić, Milica Škipina, Vanja ŠvendaGenerative ModelingSynthetic Data Augmentation

  18. DynFlowDrive: Flow-Based Dynamic World Modeling for Autonomous Driving

    Mar 20, 2026Xiaolu Liu, Yicong Li, Song Wang +3World Models for Autonomous DrivingLatent World Models

  19. Few-shot Acoustic Synthesis with Multimodal Flow Matching

    Mar 19, 2026Amandine BrunettoAudio GenerationSpatial Audio

  20. InjectFlow: Weak Guides Strong via Orthogonal Injection for Flow Matching

    Mar 19, 2026Dayu Wang, Jiaye Yang, Weikang Li +2Flow MatchingFlow-Based Generative Modeling

  21. Few-Step Diffusion Sampling Through Instance-Aware Discretizations

    Mar 18, 2026Liangyu Yuan, Ruoyu Wang, Tong Zhao +4Few-Step Diffusion SamplingDiffusion Timestep Scheduling

  22. Large-Scale 3D Ground-Motion Synthesis with Physics-Inspired Latent Operator Flow Matching

    Mar 18, 2026Yaozhong Shi, Grigorios Lavrentiadis, Konstantinos Tsalouchidis +5Physics-Informed Generative ModelingSeismology