Title: DreamSwapV: Mask-guided Subject Swapping for Any Customized Video Editing
Primary area: generative models
Keywords: Video Editing, Video Customization, Video Inpainting, Diffusion Transformer, Computer Vision

Abstract: With the rapid progress of video generation, demand for customized video editing is surging, where subject swapping constitutes a key component yet remains under-explored. Prevailing swapping approaches either specialize in narrow domains—such as human-body animation or hand-object interaction—or rely on some indirect editing paradigm or ambiguous text prompts that compromise final fidelity. In this paper, we propose DreamSwapV, a mask-guided, subject-agnostic, end-to-end framework that swaps any subject in any video for customization with a user-specified mask and reference image. To inject fine-grained guidance, we introduce multiple conditions and a dedicated condition fusion module that integrates them efficiently. In addition, an adaptive mask strategy is designed to accommodate subjects of varying scales and attributes, further improving interactions between the swapped subject and its surrounding context. Through our elaborate two-phase dataset construction and training scheme, our DreamSwapV outperforms existing methods, as validated by comprehensive experiments on VBench indicators and our first introduced DreamSwapV-Benchmark.

TL;DR: We present a mask-guided, subject-agnostic framework dedicated to end-to-end generic subject swapping for video customization, achieving state-of-the-art results in the video subject swapping task.