Conditional Diffusion Models

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20 papers in the last four weeks, up 233% on the four weeks before. 0.2% of all new papers.

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Position Forcing: Self-Conditioning 3D Generation
Oct 7, 2026Ziheng Ouyang, Zeqiang Lai, Jiarui Chen +73D Shape Generation 3D Diffusion Models
VCIP, Nankai University · Tencent Hunyuan · MMLab, CUHK +3
Noise Your Prompt: Noising Conditioning Tokens in Continuous Diffusion Language Models
Oct 6, 2026Justin JungContinuous Diffusion Language Models Conditional Diffusion Models
Lateral Intelligence
Learning Transition Kernels of Jump-Diffusion Processes with Conditional Diffusion Models
Oct 6, 2026Yuzhen Zhao, Yating Liu, Quentin GuibertStochastic Differential Equations Conditional Distribution Estimation
CEREMADE, CNRS, Universite Paris-Dauphine, PSL, 75016 Paris, France
SNR-Gated LSTM-Conditioned Diffusion Model for MIMO Channel Estimation
Oct 6, 2026Jixing Zhou, Xinming HuangChannel Estimation Conditional Diffusion Models
Worcester Polytechnic Institute Worcester, MA, USA
Feature Information Dynamics in Diffusion
Oct 6, 2026Jia-Shu Pan, Tao Zhang, Yufei Huang +2Conditional Diffusion Models Diffusion Models
Department of Artificial Intelligence, School of Engineering, Westlake University · Zhejiang University · Research School of Astronomy and Astrophysics, Australian National University
SALD: Self-Referenced Advantage Learning for Diffusion Models
Oct 1, 2026Aryan Das, Surjo Dey, Koushik Biswas +4T2I Generation Conditional Diffusion Models
Indian Institute of Technology Kanpur, India · Rajiv Gandhi Institute of Petroleum Technology, India · IIIT Delhi, India +2
Towards Fast and Disentangled Counterfactuals for Visual Foundation Models
Oct 1, 2026Sidney Bender, Benedikt Kunz, Ahmed Zeid +3Disentangled Representation Learning Counterfactual Explanations
Berlin Institute for the Foundations of Learning and Data (BIFOLD), Berlin, Germany · Machine Learning Group, Technische Universität Berlin, Berlin, Germany
MIND: Marginal-Invariant Neural Dependency Diffusion for Mixed-Type Tabular Generation
Sep 30, 2026Pengfei Li, Mohammad KhalilCopula Models Synthetic Tabular Data Generation
Centre for the Science of Learning & Technology (SLATE), University of Bergen, Bergen, Norway
Proper Scoring Rule-based Diffusion for Probabilistic Weather Forecasting
Sep 29, 2026Joonhyeong Park, Giung Nam, Hyungi Lee +3Probabilistic Forecasting Proper Scoring Rules
KAIST · Kookmin University · New York University
ProCTI: Prototype-Refined Global Conditioning for Diffusion-Based Time Series Imputation
Sep 29, 2026Fariza Rashid, Duc Van Le, Rahat Masood +3Time Series Imputation Conditional Diffusion Models
University of Sydney · University of New South Wales
Physics-Guided Conditional Diffusion Model for Rare Event Synthesis and Diagnosis for the Water-Gas Shift Reaction
Sep 28, 2026Md Abrar Rafid Siddique, Bibek Aryal, Qiugang LuPhysics-Informed Generative Modeling Synthetic Anomaly Generation
Department of Chemical Engineering, Texas Tech University, Lubbock, TX 79409, USA
Retrieval-Augmented Diffusion Modeling for Stochastic Discount Factor Portfolios
Sep 28, 2026Kelvin J. L. Koa, Xinyang Li, Ke-Wei HuangQuantitative Finance Portfolio Optimization
National University of Singapore Asian Institute of Digital Finance
Cyclostationary Phase Conditioning for Medical Time Series Diffusion
Sep 28, 2026Samuel Ruiperez-Campillo, Michele Copetti, Jorge da Silva Goncalves +3Time Series Imputation Diffusion-Based Inverse Problems
Dept. of Computer Science ETH Zurich
Admissible Diffusion for Multimodal Interventional Trajectories
Sep 28, 2026Xing Han, Shravan Chaudhari, Jiarui Shao +2Constrained Generative Modeling Causal Inference
Johns Hopkins University · Massachusetts Institute of Technology · Bayesian Health
ResDiffFRG: Residual Diffusion for Multiple Appropriate Facial Reaction Generation
Sep 27, 2026Shizhe Liu, Jiayan Gu, Xiangyu Kong +1Conditional Diffusion Models Diffusion Models
Department of Computer Science, University of Oxford, Oxford, United Kingdom · School of Artificial Intelligence and Big Data, Hefei University, Hefei, China · Department of Computer Science, University of Exeter, Exeter, United Kingdom
Domain-Adapted Diffusion Models for Conditional Independence Testing
Sep 27, 2026Yanfeng Yang, Junda Zhao, Yijie Gao +8Multi-Source Domain Adaptation Domain Adaptation
The Graduate University for Advanced Studies, SOKENDAI, Tokyo, Japan · The Institute of Statistical Mathematics, Tokyo, Japan · East China Normal University, Shanghai, China +4
ZoomDiff: A High-Fidelity Diffusion Model for Dual-Camera Smooth Zooming
Sep 23, 2026Jiayi Zhang, Renlong Wu, Yukang Ding +2Diffusion Model Guidance Video Diffusion Models
Harbin Institute of Technology · Taobao, Alibaba Group
CARE: Condition-Aware Representation Regularization for Diffusion Models
Sep 23, 2026Fengjia Guo, Zhuoyi Yang, Jie TangConditional Image Generation Conditional Diffusion Models
Department of Computer Science and Technology, Tsinghua University, Beijing, China.
Conditional Tensor Diffusion: Distributional Counterfactual Learning and Inference
Sep 22, 2026Xinbing Kong, Zeyu Li, Junfan Mao +1Causal Counterfactual Generation Low-Rank Matrix Decomposition
Southeast University · University of Science and Technology of China
MGRD: Compact morphology-gated residual diffusion for variance-aware cross-domain neurite forecasting
Sep 21, 2026Tsung Yeh Hsieh, Cosmin Anitescu, Chunghwan Kim +2Surrogate Modeling Uncertainty Quantification
Department of Mechanical Engineering, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213, USA · Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213, USA · Department of Civil and Environmental Engineering, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213, USA
Rethinking Diffusion Segmentation: When Does It Rely on Its Noisy State, and Does Diffusion Matter?
Sep 21, 2026Hengzhuo Yang, Yuming Zeng, Yuling YangConditional Diffusion Models Diffusion Models
Department of Mathematics, Northeastern University, Boston, MA 02115 USA · Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02115 USA
MUMINS: Metadata-conditioned Uncertainty-aware Medical Image Next-state Synthesis
Sep 15, 2026Anna Oliveras, Roger Marí, Rafael Redondo +7Medical Image Analysis Longitudinal Medical Image Analysis
Eurecat, Centre Tecnol`ogic de Catalunya, Barcelona, Spain · Dept. de Matem`atiques i Inform`atica, Universitat de Barcelona, Barcelona, Spain · Barcelona Supercomputing Center (BSC), Barcelona, Spain +1
FAHCD-Net: Frequency-Adaptive Heatmap-Conditional Diffusion Networks for Robust Facial Landmark Detection
Sep 15, 2026Jun Wan, Jiwei Hu, Shengkai Hu +1Frequency-Domain Feature Learning Conditional Diffusion Models
School of Information Engineering, Zhongnan University of Economics and Law, Wuhan 430073, China
LoaDiff: Conditional Generation of Electricity Consumption Time Series for Energy Analytics
Sep 12, 2026Mariia Baranova, Adrien Petralia, Etienne Le Naour +3Synthetic Data Generation Building Energy Management
EDF R&D, Palaiseau, France · Université Paris Cité, F-75006 Paris, France
RDDMPI: Residual Denoising Diffusion Model for Probabilistic Multivariate Time Series Imputation
Sep 10, 2026Ramiro Valdes Jara, David Chapman, Adam MeyersTime Series Imputation Conditional Diffusion Models
Department of Industrial and Systems Engineering, University of Miami, United States of America · Department of Computer Science, University of Miami, United States of America
Conditioning Degenerate Diffusion Models
Sep 3, 2026Uğur Aydın, Tamer BaşarDiffusion Model Guidance Stochastic Optimal Control
Department of Electrical and Computer Engineering and Coordinated Science Laboratory University of Illinois Urbana Champaign Illinois, IL 61801, USA
SurgeGen: A Hybrid Generative Diffusion Framework for Storm Surge Scenario Synthesis
Sep 3, 2026Shunan Zheng, John J. HasenbeinSurrogate Modeling PDE Surrogate Modeling
Operations Research and Industrial Engineering University of Texas at Austin Austin, TX, USA
A Study of Conditional Diffusion Models for Open-Loop Control under Dry Friction and Stiction
Sep 1, 2026Eric Aislan AntoneloDiffusion Policy Conditional Diffusion Models
Department of Automation and Systems Engineering (EAS) Federal University of Santa Catarina (UFSC) Florian´opolis, SC, Brazil · SC, Brazil
Diffusion-Based Refinement for Kilometer-Scale Probabilistic Precipitation Nowcasting
Aug 31, 2026Dohyun Park, Changhoon Song, Tengyuan Chang +2Precipitation Forecasting Probabilistic Forecasting
Department of Mathematical Sciences, Seoul National University, Seoul, Republic of Korea. · Research Institute of Mathematics, Seoul National University, Seoul,2026 Republic of Korea. · Department of Environmental Management, Seoul National University, Seoul, Republic of Korea.
SimCast-S2S: A Computationally Efficient Diffusion Model for Subseasonal Precipitation Forecasting
Aug 27, 2026Hiep V. Dang, Antonios MamalakisPrecipitation Forecasting Latent Diffusion Models
Department of Environmental Sciences, University of Virginia, Charlottesville, VA 22903, United States · School of Data Science, Department of Environmental Sciences, University of Virginia, Charlottesville, VA 22903, United States

Oct 7, 2026, cs.CVJ/K move · Enter open · S save

Position Forcing: Self-Conditioning 3D Generation

Ziheng Ouyang, Zeqiang Lai, Jiarui Chen, Jiangshan Wang+6

VCIP, Nankai University · Tencent Hunyuan · MMLab, CUHK+3

3D Shape Generation 3D Diffusion Models Diffusion Model Guidance Conditional Diffusion Models

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Recent single-stage 3D generative models commonly adopt VecSet representations, encoding 3D shapes as unordered sets of latent tokens. However, compared with two-stage methods that provide explicit positional guidance, these models must implicitly infer token positions throughout denoising, limiting their generation quality. We observe that, despite the absence of explicit positional conditioning, VecSet tokens retain recoverable spatial correspondences. Building on this observation, we propose Position Forcing, a position-based self-conditioning framework. During denoising, Position Forcing recovers token positions from the current clean latent estimate, quantizes them at progressively finer resolutions according to the denoising stage, and feeds the resulting positional encodings back into the diffusion Transformer. This progressively refined positional feedback provides spatial guidance at a granularity appropriate to each denoising stage, guiding shape generation along a coarse-to-fine trajectory and substantially improving generation quality without a separate position generation stage. Experiments demonstrate that Position Forcing achieves strong performance among single-stage 3D generative methods and outperforms several competitive multi-stage approaches.

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