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

CardsList

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

  1. ECTraj: Enhanced Consistency Training for Multi-Agent Trajectory Prediction

    May 9, 2026Alen Mrdovic, Qingze, Liu +6Trajectory GenerationMulti-Agent Trajectory Prediction

    Tony · Rutgers University, New Brunswick · The College of New Jersey

  2. Toward Better Geometric Representations for Molecule Generative Models

    May 8, 2026Shaoheng Yan, Zian Li, Cai Zhou +3Geometric Representation Learning3D Molecular Generation

    Institute for Artificial Intelligence, PKU · ByteDance AI Drug Discovery · School of Intelligence Science and Technology, PKU +3

  3. Excluding the Target Domain Improves Extrapolation: Deconfounded Hierarchical Physics Constraints

    May 8, 2026Tsuyoshi OkitaOOD GeneralizationPhysics-Informed ML

    Kyushu Institute of Technology

  4. Tyche: One Step Flow for Efficient Probabilistic Weather Forecasting

    May 7, 2026Fan Xu, Yuan Gao, Kun Wang +4Flow MatchingProbabilistic Forecasting

    1USTC · 2SLAI · 3THU +3

  5. Flow Matching with Arbitrary Auxiliary Paths

    May 7, 2026Xin Peng, Ang GaoConditional Flow MatchingFlow-Based Generative Modeling

    School of Physical Science and Technology, Beijing University of Posts and Telecommunications, Beijing, China

  6. A Flow Matching Algorithm for Many-Shot Adaptation to Unseen Distributions

    May 7, 2026Tyler Ingebrand, Ruihan Zhao, Kushagra Gupta +3Flow MatchingFlow-Based Generative Modeling

    University of Texas at Austin

  7. Conditional generation of antibody sequences with classifier-guided germline-absorbing discrete diffusion

    May 7, 2026Justin Sanders, Luca Giancardo, Lan Guo +4Diffusion Model GuidanceProtein Language Models

    Paul G. Allen School of Computer Science and Engineering, University of Washington · ∗Work performed during internship at Amazon Web Services · Life Sciences, Amazon Web Services

  8. GCCM: Enhancing Generative Graph Prediction via Contrastive Consistency Model

    May 7, 2026Shaozhen Ma, Wei Huang, Hanchen Wang +2Conditional Generative ModelingConsistency Models

    University of New South Wales · University of Technology Sydney

  9. Active Learning for Conditional Generative Compressed Sensing

    May 6, 2026Alexander DeLise, Nick DexterCompressed SensingConditional Generative Modeling

    Department of Scientific Computing Department of Mathematics Florida State University · Department of Scientific Computing Florida State University

  10. Fine-Grained Graph Generation through Latent Mixture Scheduling

    May 4, 2026Nidhi Vakil, Hadi AmiriGraph GenerationGenerative Modeling

    Department of Computer Science University of Massachusetts Lowell

  11. Arbitrarily Conditioned Hierarchical Flows for Spatiotemporal Events

    May 2, 2026Keyan Chen, Qiwei Yuan, Zhitong Xu +2Temporal Point ProcessesFlow Matching

    Kahlert School of Computing University of Utah · Celonis AI

  12. Co-Generative De Novo Functional Protein Design

    May 1, 2026Xinrui Chen, Yizhen Luo, Siqi Fan +1Protein Language ModelsMolecular Generation

    Institute for AI Industry Research (AIR), Tsinghua University · Department of Computer Science and Technology, Tsinghua University · PharMolix Inc.

  13. Proteo-R1: Reasoning Foundation Models for De Novo Protein Design

    May 1, 2026Fang Wu, Weihao Xuan, Heli Qi +26Molecular GenerationGenerative Modeling

    Stanford University

  14. AMGenC: Generating Charge Balanced Amorphous Materials

    Apr 30, 2026Yan Lin, Jilin Hu, N. M. Anoop Krishnan +1Materials ScienceGenerative Modeling

    Department of Computer Science, Aalborg University, Denmark · School of Data Science and Engineering, East China Normal University, China · Department of Civil Engineering, Indian Institute of Technology Delhi, India +2

  15. Preserving Temporal Dynamics in Time Series Generation

    Apr 29, 2026Ci Lin, Futong Li, Tet Yeap +1Time Series GenerationTime Series Data Augmentation

  16. How to Guide Your Flow: Few-Step Alignment via Flow Map Reward Guidance

    Apr 29, 2026Jerry Y. Huang, Justin Lin, Sheel Shah +2Generative ModelingFlow-Based Generative Modeling

    Carnegie Mellon University

  17. AlphaJet: Automated Conceptual Aircraft Synthesis via Disentangled Generative Priors and Topology-Preserving Evolutionary Search

    Apr 29, 2026Boris KriukVariational Autoencoders3D Shape Generation

    Hong Kong University of Science and Technology Clear Water Bay, Hong Kong, HKSAR

  18. FlowS: One-Step Motion Prediction via Local Transport Conditioning

    Apr 28, 2026Leandro Di Bella, Adrian Munteanu, Bruno CornelisFlow MatchingTrajectory Generation

    Department of Electronics and Informatics, Vrije Universiteit Brussel, Pleinlaan 2, B-1050 Brussels, Belgium · IMEC, Kapeldreef 75, B-3001 Leuven, Belgium

  19. PhyloSDF: Phylogenetically-Conditioned Neural Generation of 3D Skull Morphology via Residual Flow Matching

    Apr 28, 2026Kaikwan Lau, Gary P. T. ChoiFlow Matching3D Mesh Generation

    Department of Mathematics The Chinese University of Hong Kong

  20. IAM: Identity-Aware Human Motion and Shape Joint Generation

    Apr 28, 2026Wenqi Jia, Zekun Li, Abhay Mittal +6Text-to-Motion GenerationHuman Motion Generation

    UIUC · Meta Reality Labs · Brown University

  21. Identity-Decoupled Anonymization for Visual Evidence in Multi-modal Retrieval-Augmented Generation

    Apr 26, 2026Zehua Cheng, Wei Dai, Jiahao SunMultimodal RAGPrivacy-Preserving ML

    Department of Computer Science · University of Oxford,, UK · FLock.io, London, UK

  22. Generative Modeling of Neurodegenerative Brain Anatomy with 4D Longitudinal Diffusion Model

    Apr 24, 2026Nivetha Jayakumar, Swakshar Deb, Bahram Jafrasteh +2Longitudinal Medical Image AnalysisMedical Image Synthesis

    Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, 22903, VA, USA · Department of Radiology, Weill Cornell Medical College, Cornell University, Ithaca, 14850, NY, USA · Department of Electrical and Computer Engineering, Department of Computer Science, University of Virginia, Charlottesville, 22903, VA, USA

  23. AI-Driven Performance-to-Design Generation and Optimization of Marine Propellers

    Apr 24, 2026Leah Chen, Keni Chih-Hua Wu, Boon Tat Chia +2Conditional Generative ModelingInverse Design

    Mencast Marine, Singapore · of High Performance Computing, Agency for Science, Technology and Research, Singapore

  24. Materialistic RIR: Material Conditioned Realistic RIR Generation

    Apr 22, 2026Mahnoor Fatima Saad, Sagnik Majumder, Kristen Grauman +1Generative ModelingConditional Generative Modeling

    University of Utah · UT Austin

  25. A Wasserstein GAN-based climate scenario generator for risk management and insurance: the case of soil subsidence

    Apr 22, 2026Antoine Heranval, Olivier Lopez, Didier Ngatcha +1Financial ServicesTime Series Forecasting

    BioSP · Biostatistiques et Processus Spatiaux (BioSP), INRAE, Avignon, France · CREST +3

  26. Generative Augmentation of Imbalanced Flight Records for Flight Diversion Prediction: A Multi-objective Optimisation Framework

    Apr 22, 2026Karim Aly, Alexei Sharpanskykh, Jacco HoekstraSynthetic Data AugmentationSynthetic Data Generation

    Control and Operations, Faculty of Aerospace Engineering, Delft University of Technology (TU Delft), Kluyverweg 1, Delft, 2629 HS, South Holland, Netherlands

  27. CrackForward: Context-Aware Severity Stage Crack Synthesis for Data Augmentation

    Apr 21, 2026Nassim Sadallah, Mohand Saïd AlliliSynthetic Data AugmentationImage Generation

    LARIVIA Lab, Department of Computer Science and Engineering · Gatineau, QC, Canada

  28. Structure-guided molecular design with contrastive 3D protein-ligand learning

    Apr 21, 2026Carles Navarro, Philipp Tholke, Gianni de FabritiisContrastive LearningVirtual Screening

    Acellera Labs, C/ Doctor Trueta 183, 08005 Barcelona, Spain · Computational Science Laboratory, Universitat Pompeu Fabra, PRBB, C/ Doctor Aiguader 88, 08003 Barcelona, Spain · Instituci´o Catalana de Recerca i Estudis Avan¸cats (ICREA), Passeig Llu´ıs Companys 23, 08010 Barcelona, Spain

  29. On the Conditioning Consistency Gap in Conditional Neural Processes

    Apr 21, 2026Robin YoungNeural ProcessesConditional Generative Modeling

    Department of Computer Science and Technology University of Cambridge

  30. An Integrated Deep-Learning Framework for Peptide-Protein Interaction Prediction and Target-Conditioned Peptide Generation with ConGA-PepPI and TC-PepGen

    Apr 20, 2026Chupei Tang, Junxiao Kong, Moyu Tang +5Molecular GenerationConditional Generative Modeling

    School of Mathematics and Statistics, Lanzhou University, 222 South Tianshui Road, Lanzhou 730000, Gansu, China · Cuiying Honors College, Lanzhou University, Lanzhou, Gansu, China · Shanghai Innovation Institute, Shanghai, China +1