Image-based virtual try-on is an increasingly important task for online
shopping. It aims to synthesize images of a specific person wearing a specified
garment. Diffusion model-based approaches have recently become popular, as they
are excellent at image synthesis tasks. However, these approaches usually
employ additional image encoders and rely on the cross-attention mechanism for
texture transfer from the garment to the person image, which affects the
try-on's efficiency and fidelity. To address these issues, we propose an
Texture-Preserving Diffusion (TPD) model for virtual try-on, which enhances the
fidelity of the results and introduces no additional image encoders.
Accordingly, we make contributions from two aspects. First, we propose to
concatenate the masked person and reference garment images along the spatial
dimension and utilize the resulting image as the input for the diffusion
model's denoising UNet. This enables the original self-attention layers
contained in the diffusion model to achieve efficient and accurate texture
transfer. Second, we propose a novel diffusion-based method that predicts a
precise inpainting mask based on the person and reference garment images,
further enhancing the reliability of the try-on results. In addition, we
integrate mask prediction and image synthesis into a single compact model. The
experimental results show that our approach can be applied to various try-on
tasks, e.g., garment-to-person and person-to-person try-ons, and significantly
outperforms state-of-the-art methods on popular VITON, VITON-HD databases.

在虚拟试衣领域，我们提出了一种基于纹理保持扩散（TPD）模型的新方法，通过利用空间维度上人体图片和参考服装图片的连接输入扩散模型的去噪 UNet，实现了高效准确的纹理迁移，并预测精确的修补掩膜，将掩膜预测和图像合成整合于单一紧凑模型中，显著优于流行的 VITON、VITON-HD 数据库上的最先进方法。

高保真虚拟试穿的纹理保持扩散模型

Texture-Preserving Diffusion Models for High-Fidelity Virtual Try-On

Diffusion model-based approaches have shown promise in data-driven planning,
but there are no safety guarantees, thus making it hard to be applied for
safety-critical applications. To address these challenges, we propose a new
method, called SafeDiffuser, to ensure diffusion probabilistic models satisfy
specifications by using a class of control barrier functions. The key idea of
our approach is to embed the proposed finite-time diffusion invariance into the
denoising diffusion procedure, which enables trustworthy diffusion data
generation. Moreover, we demonstrate that our finite-time diffusion invariance
method through generative models not only maintains generalization performance
but also creates robustness in safe data generation. We test our method on a
series of safe planning tasks, including maze path generation, legged robot
locomotion, and 3D space manipulation, with results showing the advantages of
robustness and guarantees over vanilla diffusion models.

我们提出一种新的方法 SafeDiffuser，使用控制屏障函数来确保扩散概率模型满足规范，并将有限时间扩散不变性嵌入去噪扩散过程中，从而实现可信扩散数据生成，并证明我们的方法不仅具有一般化性能，而且还在安全数据生成中创建了鲁棒性。