PEMF-VTO: Point-Enhanced Video Virtual Try-on via Mask-free Paradigm

TL;DR

PEMF-VTO introduces a mask-free video virtual try-on method that uses sparse point alignments and a novel transformer architecture to improve garment transfer accuracy and temporal coherence in complex, real-world videos.

Contribution

The paper presents a new Point-Enhanced Transformer framework that guides garment transfer using sparse point alignments, overcoming limitations of mask-based methods in dynamic scenes.

Findings

Outperforms state-of-the-art methods in naturalness and coherence

Effective in complex in-the-wild scenarios

Enhances temporal stability in try-on videos

Abstract

Video Virtual Try-on aims to seamlessly transfer a reference garment onto a target person in a video while preserving both visual fidelity and temporal coherence. Existing methods typically rely on inpainting masks to define the try-on area, enabling accurate garment transfer for simple scenes (e.g., in-shop videos). However, these mask-based approaches struggle with complex real-world scenarios, as overly large and inconsistent masks often destroy spatial-temporal information, leading to distorted results. Mask-free methods alleviate this issue but face challenges in accurately determining the try-on area, especially for videos with dynamic body movements. To address these limitations, we propose PEMF-VTO, a novel Point-Enhanced Mask-Free Video Virtual Try-On framework that leverages sparse point alignments to explicitly guide garment transfer. Our key innovation is the introduction of point-enhanced guidance, which provides flexible and reliable control over both spatial-level garment transfer and temporal-level video coherence. Specifically, we design a Point-Enhanced Transformer (PET) with two core components: Point-Enhanced Spatial Attention (PSA), which uses frame-cloth point alignments to precisely guide garment transfer, and Point-Enhanced Temporal Attention (PTA), which leverages frame-frame point correspondences to enhance temporal coherence and ensure smooth transitions across frames. Extensive experiments demonstrate that our PEMF-VTO outperforms state-of-the-art methods, generating more natural, coherent, and visually appealing try-on videos, particularly for challenging in-the-wild scenarios. The link to our paper's homepage is https://pemf-vto.github.io/.