Progressive Conditional Diffusion Models (PCDMs) have been introduced by Tencent AI Lab to address the challenges in pose-guided person image synthesis. PCDMs consist of three stages: predicting global features, establishing dense correspondences, and refining images. The method effectively aligns source and target images at multiple levels, producing high-quality and realistic results. It also demonstrates improved performance in person re-identification tasks. The research has been validated through experiments and user studies. Overall, PCDMs offer a promising solution for pose-guided image synthesis.
Introducing Progressive Conditional Diffusion Models (PCDMs) for Pose-Guided Person Image Synthesis
The field of pose-guided person image synthesis has made significant progress in recent years, offering practical solutions for e-commerce content generation and improving person re-identification. However, challenges arise due to inconsistencies between source and target poses.
To address these challenges, researchers have explored various techniques, including GAN-based, VAE-based, and flow-based approaches. However, these methods have limitations such as unrealistic results, blurred details, misaligned poses, and introduced artifacts.
A recently published paper introduces Progressive Conditional Diffusion Models (PCDMs), which offer a three-stage approach to generate high-quality images:
1) Prior Conditional Diffusion Model:
This stage predicts the global features of the target image by leveraging the alignment relationship between pose coordinates and image appearance. It bridges the gap between the source and target images at the feature level, ensuring better texture and detail consistency.
2) Inpainting Conditional Diffusion Model:
In this stage, the model establishes dense correspondences between the source and target images using the global features obtained in the previous stage. This transforms the unaligned image-to-image generation task into an aligned one, improving the alignment between source and target images for more realistic results.
3) Refining Conditional Diffusion Model:
After generating a preliminary coarse-grained target image, this stage enhances image quality and detail texture. It uses the coarse-grained image as a condition to improve image fidelity and texture consistency, resulting in further texture repair and detail enhancement.
The method has been validated through comprehensive experiments on public datasets, demonstrating competitive performance in quantitative metrics and user studies. It also showcases improved person re-identification performance compared to baseline methods.
In conclusion, PCDMs offer a notable breakthrough in pose-guided person image synthesis. They effectively address alignment and pose consistency issues, producing high-quality, realistic images. Their superior performance, as demonstrated in experiments and user studies, along with their applicability to person re-identification tasks, highlights their practical utility. PCDMs provide a promising solution for various applications in the field of pose-guided image synthesis.
If you’re interested in leveraging AI to evolve your company and stay competitive, consider exploring Tencent AI Lab’s Progressive Conditional Diffusion Models. To learn more about how AI can redefine your way of work, connect with us at hello@itinai.com. Stay updated on the latest AI research news and projects by joining our ML SubReddit, Facebook Community, Discord Channel, and Email Newsletter.
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Evolve Your Company with AI
If you want to evolve your company with AI and stay competitive, consider using Tencent AI Lab’s Progressive Conditional Diffusion Models (PCDMs) for pose-guided person image synthesis. These models offer practical solutions to address alignment and pose consistency issues, producing high-quality, realistic images.
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