Conditional Generative Modeling

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Overview

Conditional generative modeling studies how to generate plausible data given information such as labels, text, images, measurements, or physical parameters. Common approaches include conditional GANs and VAEs, normalizing flows, diffusion and score-based models, flow matching, and autoregressive or masked models. These methods support tasks such as image reconstruction, time-series forecasting, molecular design, and scientific inverse problems, where many possible outputs may fit the same input. A central challenge is balancing fidelity to the condition and output diversity with fast sampling, reliable uncertainty, and generalization to unseen conditions.

Latest papers

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393 papers53 papers in the last four weeks, up 39% on the four weeks before. 0.3% of all new papers.

  1. Image Modeling: From Handcrafted Priors to Foundation Image Models

    Oct 27, 2026A. Murat TekalpImage Inverse ProblemsConditional Generative Modeling

    Department of Electrical and Electronics Engineering, Koc¸ University, Istanbul, Turkey

  2. HELICS: Biobank-scale Conditional Synthetic Genome Generation via Latent Flow Matching

    Oct 26, 2026Ahmad Abdel-Azim, Xihong LinSynthetic Data GenerationGenomics

    Department of Biostatistics, Harvard University, Boston, MA 02135 · Department of Statistics, Biostatistics, Harvard University, Boston, MA 02135

  3. EVA-Flow: Learning Shared Chemistry for Unified Environment-Aware Molecular Conformation Generation

    Oct 26, 2026Bing Yan, Anuroop Sriram, Zachary Ward Ulissi +33D Molecular GenerationMolecular Generation

    FAIR at Meta · New York University

  4. Active Learning of Conditional Generative Models via the Transport Neural Tangent Kernel

    Oct 26, 2026Jayoung Ryu, Kyunghyun Cho, Romain LopezActive LearningNeural Tangent Kernel

    Department of Computer Science, Courant Institute School of Mathematics, Computing, and Data Science, New York University · Department of Biology, New York University

  5. Minimax Rates for Conditional Generative Learning under Wasserstein Loss

    Oct 26, 2026Sungwon Park, Kyoung-Sook Moon, Hongjoong KimConditional Generative ModelingConditional Distribution Estimation

    Department of Mathematics, Korea University · Department of Finance and Big Data, Gachon University

  6. Sequential versus fully conditional specification for tree-based synthesis of health data

    Oct 26, 2026R{é}my Chapelle, Mohammed SedkiSynthetic Clinical Data GenerationTabular Data Synthesis

    CB, CESP, EVDG · Université Paris-Saclay, UVSQ, Inserm, CESP, 94807, Villejuif, France · Université Paris-Saclay, Université Paris Cité, ENS Paris-Saclay, CNRS, SSA, Inserm, Centre Borelli, 91190, Gif-sur-Yvette, France +2

  7. Condition-Enhanced Generative Adversarial Network for Aerodynamic Configuration Design of Commercial Aircraft

    Oct 25, 2026Youzheng Yu, Jiaqing Kou, Weiwei ZhangConditional Generative ModelingGenerative Adversarial Networks

    School of Aeronautics, Northwestern Polytechnical University, Xi’an, 710072, People’s Republic of China · International Joint Institute of Artificial Intelligence on Fluid Mechanics, Northwestern Polytechnical University, Xi’an, 710072, People’s Republic of China · National Key Laboratory of Aircraft Configuration Design, Xi’an, 710072, People’s Republic of China

  8. Synthetic Data Augmentation for Functional Data: Wasserstein Risk Bounds and Diffusion-Based Conditional Generation

    Oct 25, 2026Yuhao Qi, Wenlin Dai, Xuehu ZhuSynthetic Data AugmentationData Augmentation

    Institute of Statistics and Big Data, Renmin University of China, Beijing, China · School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, China

  9. On the Error Analysis of Triangular Transport Maps for Conditional Generation via MMD-based Objectives

    Oct 24, 2026Michele Martino, Bamdad Hosseini, Amir Sagiv +1Conditional Generative ModelingConditional Distribution Estimation

    Department of Applied Mathematics, University of Washington, Seattle, WA, USA · Department of Mathematical Sciences, New Jersey Institute of Technology, Newark, NJ, USA · Department of Aeronautics and Astronautics, University of Washington, Seattle, WA, USA

  10. Nowcasting aerosol particle size distributions from PM2.5 with physics-regularized flow matching

    Oct 24, 2026Zhi-Song Liu, Jenni Köykkä, Wenqing Peng +3Physics-Informed Generative ModelingAir Quality Prediction

    Department of Computational Engineering, LUT University, Finland · Atmospheric Modelling Center, Lahti (AMC-Lahti), Finland · Institute for Atmospheric and Earth System Research (INAR), University of Helsinki, Finland

  11. Conditional Neural Processes for Self-Supervised Goal Discovery in Action Imitation

    Oct 23, 2026Marco Gabriele Fedozzi, Yukie Nagai, Francesco Rea +1Action Representation LearningRobot Imitation Learning

    DIBRIS Department, University of Genoa · CONTACT Unit, Italian Institute of Technology · International Research Center for Neurointelligence, The University of Tokyo

  12. REFLEX: RNA Ensemble Generation via Flexibility-Calibrated Stochastic Bridge

    Oct 19, 2026Wanli Ma, Tianmeng Hu, Ke LiMolecular GenerationBioinformatics

    University of Exeter · Earlham Institute

  13. FracTS: Hierarchical and Autoregressive Time Series Generation

    Oct 18, 2026Jiayu Li, Umair Afzal, Zilong Zhao +3Time Series GenerationAutoregressive Generation

    University of Illinois Urbana-Champaign · Betterdata AI · National University of Singapore

  14. Knowledge Diversion: A Novel Paradigm for Pre-training Decomposable Image Generation Models

    Oct 17, 2026Yucheng Xie, Fu Feng, Ruixiao Shi +3Image GenerationConditional Generative Modeling

    School of Computer Science and Engineering, Southeast University, Nanjing, China and the Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China

  15. HilbertGen-3D:Hilbert--Multifractal Conditioning for Topology-Aware 3D Generation

    Oct 15, 2026Ziyi Zhang, Shixuan Li, Yorie Nakahira +23D Shape GenerationPoint Cloud Generation

    Department of Electrical and Computer Engineering, Carnegie Mellon University · Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California

  16. Normalizing Flow Maps

    Oct 15, 2026Francesco M. Ruscio, Timothy James Hitge, Fred Zhangzhi Peng +6Normalizing FlowsFlow Map Learning

    AITHYRA · Imperial College London · TU Wien +5

  17. Generative adversarial networks with Monte Carlo dropout uncertainty quantification for structural health monitoring: Fibre Bragg grating data augmentation comparison in Hawk T1A aircraft

    Oct 15, 2026Alvaro Peiro, Marios ImpraimakisSynthetic Data AugmentationSynthetic Data Generation

    Department of Mechanical Engineering, University of Bath, Bath, BA2 7AY, UK

  18. Are We Overthinking One-Step Generation?

    Oct 14, 2026Woobin Park, Jaeyeon Kim, Yilun Du +2One-Step Generative ModelingConditional Generative Modeling

    Seoul National University · Harvard University

  19. Predicting Unseen Scientific Interactions: Conditional Flow Models Hallucinate Non-Additive Effects

    Oct 14, 2026Sam Gijsen, Moritz Seiler, Kerstin RitterConditional Generative ModelingFeature Interaction Modeling

  20. InstructPro2: Natural Language and Biological Grounding Guided De Novo Protein Design

    Oct 14, 2026Charles Novak, Zhenqiao Song, Chuan Li +2Protein DesignProtein Language Models

    Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA · Language Technologies Institute, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA · Lambda Inc, San Francisco, CA 94105, USA

  21. BridgeTwist: Twisting Schrödinger Bridges for Training-Free Conditional Sampling

    Oct 13, 2026Mengyu Li, Qianqian Qu, Jun S. LiuConditional Generative ModelingPosterior Sampling

    Department of Statistics and Data Science, Tsinghua University, Beijing, China

  22. MissPath-FM: Flow Matching with Structured Priors for Partially Observed Time Series

    Oct 13, 2026Genpei ZhangFlow MatchingConditional Generative Modeling

    Department of Computer Sciences, University of Wisconsin–Madison

  23. Masked Diffusion for Travel Diaries Synthesis

    Oct 12, 2026Pascal Jutras-Dubé, Raziq Zaman, Fabian Bastin +2Conditional Generative ModelingMasked Diffusion Models

    Department of Computer Science, Purdue University, West Lafayette, United States · Department of Civil and Environmental Engineering, University of Maryland, College Park, United States · Department of Computer Science and Operations Research, Université de Montréal, Montréal, Canada

  24. PerturbBridge: Direct Phenotypic Supervision for Cellular Perturbation Generation

    Oct 12, 2026Yucheng Yang, Xiaoxiao Sun, Huangqingbo Sun +3Single-Cell Perturbation ModelingImage Generation

    Stanford University

  25. Behavior-Conditioned Animal Vocalization Generation: Event-Aware Modeling and Multidimensional Evaluation

    Oct 12, 2026Xinlong Deng, Yong Tian, Jian Guan +3Audio GenerationConditional Generative Modeling

    College of Artificial Intelligence, China University of Petroleum, Beijing · College of Computer Science and Technology, Harbin Engineering University, Harbin, China · Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China +1

  26. Probabilistic Simulation of Gas-Liquid Interfaces with Coupled Flow Matching on Spherical Harmonics

    Oct 12, 2026Kiet Bennema ten Brinke, Rachna Ramesh, Douwe Orij +2Neural Surrogate ModelingComputational Fluid Dynamics

    Department of Mathematics and Computer Science, Eindhoven University of Technology · Department of Chemical Engineering and Chemistry, Eindhoven University of Technology

  27. TabMosaic: Unified In-Context Diffusion for Tabular Generative Tasks

    Oct 11, 2026Jayoung Kim, Sanghyun Hong, Noseong ParkSynthetic Tabular Data GenerationTabular Diffusion Models

    KAIST · Oregon State University

  28. Depth-progressive 3D seismic velocity model building via 2D generative diffusion models

    Oct 10, 2026Shijun Cheng, Randy Harsuko, Mohammad H. Taufik +1Seismic ImagingDiffusion Models

    Division of Physical Science and Engineering, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia

  29. MontageFlow: Physically Valid High Fidelity EEG Generation

    Oct 10, 2026Sudarshan Regmi, Nabaraj Subedi, Arvind Pillai +6EEG Signal GenerationTime Series Generation

    Dartmouth College

  30. ClothCode: Generative Modeling for Garment State Estimation and Dynamics Prediction

    Oct 9, 2026Yingdong Ru, Lipeng Zhuang, Shiyu Fan +3Generative ModelingConditional Generative Modeling

    The authors are with the University of Glasgow, Glasgow, United Kingdom.