无需重训练扩散模型，用轻量掩码实现逼真虚拟试穿。
DiffusionTrend: A Minimalist Approach to Virtual Fashion Try-On
- 用轻量CNN生成精确服装掩码，指导扩散模型生成细节。
- 不需重训练和复杂输入，仍可生成视觉效果出色的试穿图。
- 适合想快速部署试穿系统的工业界与研究者参考。
我们提出DiffusionTrend用于虚拟时尚试穿，无需重新训练扩散模型。该方法利用先进扩散模型中富含先验信息的潜在空间，捕捉衣物细节，并在扩散去噪过程中，通过轻量级紧凑的CNN生成的精确服装掩码，将这些细节无缝融入图像生成。尽管初始指标表现一般，但该探索性方法具有显著优势：（1）避免在大规模数据集上对扩散模型进行资源密集型重训练；（2）无需复杂且用户不友好的模型输入；（3）提供极具视觉吸引力的试穿体验，凸显了无训练扩散模型的潜力。这一初步尝试为虚拟试穿技术中未训练扩散模型的应用开辟了新方向，具有重要的产业与学术价值。
原文摘要 · Abstract (English)
We introduce DiffusionTrend for virtual fashion try-on, which forgoes the need for retraining diffusion models. Using advanced diffusion models, DiffusionTrend harnesses latent information rich in prior information to capture the nuances of garment details. Throughout the diffusion denoising process, these details are seamlessly integrated into the model image generation, expertly directed by a precise garment mask crafted by a lightweight and compact CNN. Although our DiffusionTrend model initially demonstrates suboptimal metric performance, our exploratory approach offers some important advantages: (1) It circumvents resource-intensive retraining of diffusion models on large datasets. (2) It eliminates the necessity for various complex and user-unfriendly model inputs. (3) It delivers a visually compelling try-on experience, underscoring the potential of training-free diffusion model. This initial foray into the application of untrained diffusion models in virtual try-on technology potentially paves the way for further exploration and refinement in this industrially and academically valuable field.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。