Conditional Generation

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2 papers in the last four weeks, down 33% on the four weeks before. 0.0% of all new papers.

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Latest papers 34

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SAVE: A Generalizable Framework for Multi-Condition Single-Cell Generation with Gene Block Attention
Apr 18, 2026Jiahao Li, Jiayi Dong, Peng Ye +3Single-Cell Perturbation Modeling Conditional Generative Modeling
College of Computer Science and Artificial Intelligence, Fudan University · Shanghai Key Laboratory of Intelligent Information Processing, Fudan University
Track and Caption Any Motion: Open-Vocabulary Spatiotemporal Captioning via Trajectory-Conditioned Generation
Dec 11, 2025Bishoy Galoaa, Sarah OstadabbasPoint Tracking Temporal Video Grounding
Northeastern University
Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization
Aug 3, 2025Xin Ding, Yun Chen, Yongwei Wang +4Conditional Generative Modeling Conditional Generation
School of Artificial Intelligence/School of Future Technology, Nanjing University of Information Science & Technology, Nanjing, China · Perceptual and Generative AI Lab, Nanjing University of Information Science & Technology, Nanjing, China · College of Media, Zhejiang University, Hangzhou, China
MolMiner: Toward Controllable, 3D-Aware, Fragment-Based Molecular Design
Nov 10, 2024Raul Ortega-Ochoa, Tejs Vegge, Jes Frellsen3D Molecular Generation Autoregressive Generation
Department of Energy Conversion and Storage, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark · CAPeX Pioneer Center for Accelerating P2X Materials Discovery · Department of Applied Mathematics and Computer Science, Technical University of Denmark

Apr 18, 2026, cs.AIJ/K move · Enter open · S save

SAVE: A Generalizable Framework for Multi-Condition Single-Cell Generation with Gene Block Attention

Jiahao Li, Jiayi Dong, Peng Ye, Xiaochi Zhou+2

College of Computer Science and Artificial Intelligence, Fudan University · Shanghai Key Laboratory of Intelligent Information Processing, Fudan University

Single-Cell Perturbation Modeling Conditional Generative Modeling Conditional Generation Single-Cell Transcriptomics

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Modeling single-cell gene expression across diverse biological and technical conditions is crucial for characterizing cellular states and simulating unseen scenarios. Existing methods often treat genes as independent tokens, overlooking their high-level biological relationships and leading to poor performance. We introduce SAVE, a unified generative framework based on conditional Transformers for multi-condition single-cell modeling. SAVE leverages a coarse-grained representation by grouping semantically related genes into blocks, capturing higher-order dependencies among gene modules. A Flow Matching mechanism and condition-masking strategy further enhance flexible simulation and enable generalization to unseen condition combinations. We evaluate SAVE on a range of benchmarks, including conditional generation, batch effect correction, and perturbation prediction. SAVE consistently outperforms state-of-the-art methods in generation fidelity and extrapolative generalization, especially in low-resource or combinatorially held-out settings. Overall, SAVE offers a scalable and generalizable solution for modeling complex single-cell data, with broad utility in virtual cell synthesis and biological interpretation. Our code is publicly available at https://github.com/fdu-wangfeilab/sc-save

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Latest papers 34

SAVE: A Generalizable Framework for Multi-Condition Single-Cell Generation with Gene Block Attention

Track and Caption Any Motion: Open-Vocabulary Spatiotemporal Captioning via Trajectory-Conditioned Generation

Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization

MolMiner: Toward Controllable, 3D-Aware, Fragment-Based Molecular Design