FlowMimic: Mask-free Visual Editing and Generation with Pixel-pair Warped Flow Field for Online Video Editing Data Generation and Modality Mimicry

arXiv:2607.18227 · cs.CV · Submitted 2026-07-20 · Read on arXiv

cs.CV

Submitted: 2026-07-20

Updated: 2026-09-22

Comments: Due to file size constraints, the figures in the arXiv file have been heavily lossy-compressed. Please visit the uncompressed file at: https://huggingface.co/datasets/FlowMimic/Uncompressed/blob/main/main.pdf

Project page: https://rigyourportrait.github.io

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Embeddings — 39× in this paper · explained in 2 episodes
• alpha — 32× in this paper · explained in 1 episode
• Flow Matching — 26× in this paper · explained in 7 episodes
• Alignment — 17× in this paper · explained in 1 episode
• Distillation — 14× in this paper · explained in 2 episodes
• Generalization — 12× in this paper · explained in 13 episodes
• Transformer — 11× in this paper · explained in 2 episodes
• Benchmark — 9× in this paper · explained in 2 episodes
• Core — 9× in this paper · explained in 2 episodes
• Decoupling — 9× in this paper · explained in 2 episodes
• Latent Space — 9× in this paper · explained in 7 episodes
• Abstraction — 8× in this paper · explained in 1 episode
• Consistency — 8× in this paper · explained in 3 episodes
• Self-distillation — 8× in this paper · explained in 6 episodes
• Diffusion Model — 7× in this paper · explained in 8 episodes
• Loss Function — 7× in this paper · explained in 2 episodes
• Attention Mechanism — 6× in this paper · explained in 2 episodes
• Fine-Tuning — 5× in this paper · explained in 5 episodes
• Regularization — 5× in this paper · explained in 3 episodes
• Adaptation — 4× in this paper · explained in 1 episode
• Diffusion Models — 4× in this paper · explained in 6 episodes
• Latent Representation — 4× in this paper · explained in 2 episodes
• On-Policy Distillation — 4× in this paper · explained in 3 episodes
• Reinforcement Learning — 4× in this paper · explained in 4 episodes
• Rectified Flow — 3× in this paper · explained in 3 episodes
• Temporal consistency — 3× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Cross-Attention Mechanism — 2× in this paper · explained in 2 episodes
• In-Context Learning — 2× in this paper · explained in 4 episodes
• Multimodal Large Language Model — 2× in this paper
• Steering — 2× in this paper · explained in 2 episodes
• Transformer Architecture — 2× in this paper · explained in 2 episodes
• 3D Gaussian Splatting — 1× in this paper · explained in 2 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Gaussian Splatting — 1× in this paper · explained in 4 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Instance Segmentation — 1× in this paper · explained in 3 episodes
• Kullback-Leibler (KL) Divergence — 1× in this paper · explained in 3 episodes
• Multimodal Inputs — 1× in this paper · explained in 2 episodes
• On-policy Self-Distillation (OPSD) — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Segmentation Masks — 1× in this paper · explained in 2 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Supervised Fine-Tuning (SFT) — 1× in this paper · explained in 8 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes
• Two-stage training — 1× in this paper · explained in 2 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes
• Vision-Language Models (VLMs) — 1× in this paper · explained in 6 episodes

Sources

• Scaling Instruction-Based Video Editing with a High-Quality Synthetic Dataset
• OpenVE-3M: A Large-Scale High-Quality Dataset for Instruction-Guided Video Editing
• SAM 3: Segment Anything with Concepts
• Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
• Qwen2.5-VL Technical Report
• Qwen3-VL Technical Report
• Wan: Open and Advanced Large-Scale Video Generative Models
• InstructX: Towards Unified Visual Editing with MLLM Guidance
• VINO: A Unified Visual Generator with Interleaved OmniModal Context
• UNIC: Unified In-Context Video Editing
• Pico-Banana-400K: A Large-Scale Dataset for Text-Guided Image Editing
• GPT-IMAGE-EDIT-1.5M: A Million-Scale, GPT-Generated Image Dataset
• ImgEdit: A Unified Image Editing Dataset and Benchmark
• RelightMaster: Precise Video Relighting with Multi-plane Light Images
• The devil is in the details: Enhancing Video Virtual Try-On via Keyframe-Driven Details Injection
• EditVerse: Unifying Image and Video Editing and Generation with In-Context Learning
• Omni-Video 2: Scaling MLLM-Conditioned Diffusion for Unified Video Generation and Editing
• Multimodal Referring Segmentation: A Survey
• From Ideal to Real: Stable Video Object Removal under Imperfect Conditions
• First Frame Is the Place to Go for Video Content Customization

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