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

Key points

The core claim is that video editing training does not need truly natural video pairs, only pixel correspondences from the first frame that remain temporally consistent.
It converts image-editing samples into video-editing samples in real time by turning both the input image and the target image into 3D grids and building a 4D temporally warped flow field over pixel pairs through grid inverse sampling.
It treats an image as a one-frame video and uses two modality-mimic losses so that T2I mimics the realism of T2V and V2V editing mimics I2I editing, which converges faster.
It introduces sense-related tasks such as referring expression segmentation, together with edit-region-aware latent-level and attention-level losses, so the model can locate the edit region without a separate MLLM or mask input at inference time.
It continues training from Wan2.1-T2V-1.3B, and when the learning rate is warmed up from 5e-6 to 1e-5, video-editing behavior appears within a few thousand steps.
Without using any specially made video-editing data, it still handles a wide range of editing tasks and even shows SAM3-like ability to segment and track objects specified by language in video.

External research summaries. These are not HDATF publications or measured product results.