ViViD: Video Virtual Try-on using Diffusion Models

• URL: http://arxiv.org/abs/2405.11794v2
• Date: Tue, 28 May 2024 04:08:09 GMT
• Title: ViViD: Video Virtual Try-on using Diffusion Models
• Authors: Zixun Fang, Wei Zhai, Aimin Su, Hongliang Song, Kai Zhu, Mao Wang, Yu Chen, Zhiheng Liu, Yang Cao, Zheng-Jun Zha,
• Abstract summary: Video virtual try-on aims to transfer a clothing item onto the video of a target person. Previous video-based try-on solutions can only generate low visual quality and blurring results. We present ViViD, a novel framework employing powerful diffusion models to tackle the task of video virtual try-on.
• Score: 46.710863047471264
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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