{"ID":5935639,"CreatedAt":"2026-07-07T01:22:02.77346169Z","UpdatedAt":"2026-07-07T02:10:06.972658124Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.03524","arxiv_id":"2607.03524","title":"Perceptual Flow Matching for Few-Step Generative Modeling","abstract":"We propose Perceptual Flow Matching (PFM), a simple yet effective framework for few-step generation in flow-matching models. Rather than performing velocity regression in the conventional VAE latent space, PFM supervises flow matching in a perceptual feature space using pretrained perceptual models. This simple change substantially improves the few-step generation capability of flow-matching models, reducing the number of sampling steps from 35-50 to 4-8 while preserving generation quality. Unlike existing acceleration and distillation approaches, PFM requires neither teacher models nor auxiliary score networks and can be integrated into standard flow-matching training pipelines with minimal modifications. Extensive experiments on image generation, video generation, and image editing tasks demonstrate that PFM consistently produces high-quality results while producing fewer artifacts than existing distillation-based methods. We further show that perceptual supervision shifts the regression minimizer from mean-seeking to mode-seeking, biasing predictions toward on-manifold modes that remain accurate under coarse few-step integration. Our results reveal that standard flow-matching training can naturally yield high-quality few-step generators when supervised in an appropriate representation space. We hope this insight inspires future research into representation-aware objectives for efficient generative modeling.","short_abstract":"We propose Perceptual Flow Matching (PFM), a simple yet effective framework for few-step generation in flow-matching models. Rather than performing velocity regression in the conventional VAE latent space, PFM supervises flow matching in a perceptual feature space using pretrained perceptual models. This simple change...","url_abs":"https://arxiv.org/abs/2607.03524","url_pdf":"https://arxiv.org/pdf/2607.03524v1","authors":"[\"Chuyang Zhao\",\"Yifei Song\",\"Hongfa Wang\",\"Jianlong Yuan\",\"Yuan Zhang\",\"Siming Fu\",\"Zhineng Chen\",\"Huilin Deng\",\"Haoyang Huang\",\"Nan Duan\"]","published":"2026-07-03T17:55:01Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Variational Autoencoder\"]","has_code":false}