ViViD: Video Virtual Try-on using Diffusion Models

TL;DR

ViViD introduces a diffusion model-based framework for video virtual try-on, achieving high-quality, temporally consistent videos by integrating garment and pose encoding with hierarchical temporal modules.

Contribution

The paper presents a novel diffusion model framework for video virtual try-on, including a garment encoder, pose encoder, and temporal modules, along with a new diverse high-resolution dataset.

Findings

Achieves high visual quality in video try-on results.

Ensures temporal and spatial consistency in generated videos.

Outperforms previous methods in visual fidelity and coherence.

Abstract

Video virtual try-on aims to transfer a clothing item onto the video of a target person. Directly applying the technique of image-based try-on to the video domain in a frame-wise manner will cause temporal-inconsistent outcomes while previous video-based try-on solutions can only generate low visual quality and blurring results. In this work, we present ViViD, a novel framework employing powerful diffusion models to tackle the task of video virtual try-on. Specifically, we design the Garment Encoder to extract fine-grained clothing semantic features, guiding the model to capture garment details and inject them into the target video through the proposed attention feature fusion mechanism. To ensure spatial-temporal consistency, we introduce a lightweight Pose Encoder to encode pose signals, enabling the model to learn the interactions between clothing and human posture and insert hierarchical Temporal Modules into the text-to-image stable diffusion model for more coherent and lifelike video synthesis. Furthermore, we collect a new dataset, which is the largest, with the most diverse types of garments and the highest resolution for the task of video virtual try-on to date. Extensive experiments demonstrate that our approach is able to yield satisfactory video try-on results. The dataset, codes, and weights will be publicly available. Project page: https://becauseimbatman0.github.io/ViViD.