Contrastive Flow Matching

Latest papers

9 papers2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

SynerGest: Temporal-Spatial Synergistic Gesture Generation for Dyadic Conversations
Oct 16, 2026Chengxiang Guo, Jian Wang, Lanmiao Liu +3Contrastive Flow Matching Co-Speech Gesture Generation
Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming, China · Yunnan Key Laboratory of Artificial Intelligence · Utrecht University Utrecht, Netherlands +2
Smoother Flow Matching via Contrastive Trajectory Repulsion
Oct 1, 2026Ziqi Jiang, Zhenqi He, Long ChenFlow Matching Contrastive Flow Matching
Department of CSE, The Hong Kong University of Science and Technology
FlowCPO: A Unified Divergence View of Preference Alignment for Flow Models
Sep 9, 2026Yansen Han, Shengyi Liao, Peng Sun +4Pairwise Preference Learning Flow Matching
Westlake University · Zhejiang University · Kling Team, Kuaishou Technology
Contrastive-Augmented Flow Matching for Style-Content Disentanglement
Jul 14, 2026Yusong Li, Pingchuan Ma, Ming Gui +2Flow Matching Disentangled Representation Learning
Computer Vision & Learning Group at the University of Munich
High-Fidelity One-Step Generative Visuomotor Policy via Recursive Correction, Frequency Consistency, and Contrastive Flow Matching
Jul 4, 2026Yuran Chen, Xinye Cai, Zhonglin Gong +1Flow Matching Contrastive Flow Matching
School of Safety Science and Engineering, Anhui University of Science and Technology, Huainan 232001, China · State Key Laboratory of Digital Intelligent Technology for Unmanned Coal Mining, Anhui University of Science and Technology, Huainan 232001, China · School of Artificial Intelligence, Anhui University of Science and Technology, Hefei 231131, China
Geometric Erasure by Contrastive Velocity Matching in Rectified Flows
May 29, 2026Jonas Henry Grebe, Tobias Braun, Anna Rohrbach +1Contrastive Flow Matching Rectified Flow
Technical University of Darmstadt & hessian.AI, Germany.
RobustSpeechFlow: Learning Robust Text-to-Speech Trajectories via Augmentation-based Contrastive Flow Matching
May 21, 2026Jinhyeok Yang, Hyeongju Kim, Yechan Yu +3Flow Matching Contrastive Flow Matching
Supertone Inc, South Korea · Independent Researcher, South Korea
SemConFlow: Semantic Grounding of Holistic Co-Speech Gesture Generation with Contrastive Flow-Matching
Mar 27, 2026Lanmiao Liu, Esam Ghaleb, Aslı Özyürek +1Multimodal Grounding Flow Matching
Max Planck Institute for Psycholinguistics · Donders Institute for Brain Cognition and Behaviour · Utrecht University
DiReCT: Disentangled Regularization of Contrastive Trajectories for Physics-Refined Video Generation
Mar 26, 2026Abolfazl Meyarian, Amin Karimi Monsefi, Rajiv Ramnath +1Contrastive Flow Matching Physical Consistency in Video Generation
Path Robotics, Columbus, OH, USA · The Ohio State University, Columbus, OH, USA · University of Central Florida, Orlando, FL, USA

Oct 16, 2026J/K move · Enter open · S save

SynerGest: Temporal-Spatial Synergistic Gesture Generation for Dyadic Conversations

Chengxiang Guo, Jian Wang, Lanmiao Liu, Zhan Tang+2

Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming, China · Yunnan Key Laboratory of Artificial Intelligence · Utrecht University Utrecht, Netherlands+2

Contrastive Flow Matching Co-Speech Gesture Generation Human Motion Generation Flow Matching for Action Generation

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Co-speech gesture generation in dyadic conversations for creating immersive virtual avatars remains highly challenging. Existing models often struggle to balance kinematic stability with rhythmic responsiveness and suffer from semantic entanglement, leading to either generic mean-pose regression or unnatural micro-jitters. To address these challenges, we propose SynerGest, a high-fidelity dyadic gesture generation framework grounded in Contrastive Flow Matching (CFM). Specifically, our CFM objective explicitly penalizes mismatched semantic pairs during training, discouraging spatial shortcuts and over-smoothed predictions while improving conditional semantic alignment. Furthermore, to overcome architectural bottlenecks, we introduce the Impulse-Trend Synergistic Attention (ITSA) module. Grounded in the key insight of decoupling "what moves" from "when it moves," ITSA models sharp, speech-driven rhythmic impulses separately from continuous kinematic trends, supporting precise audio-motion synchronization and global structural stability. Extensive qualitative and quantitative evaluations on two public datasets demonstrate that SynerGest achieves state-of-the-art performance, delivering contextually responsive and kinematically fluid dyadic interactions.