Advancing Pose-Guided Image Synthesis with Progressive Conditional Diffusion Models (2310.06313v4)

Abstract: Recent work has showcased the significant potential of diffusion models in pose-guided person image synthesis. However, owing to the inconsistency in pose between the source and target images, synthesizing an image with a distinct pose, relying exclusively on the source image and target pose information, remains a formidable challenge. This paper presents Progressive Conditional Diffusion Models (PCDMs) that incrementally bridge the gap between person images under the target and source poses through three stages. Specifically, in the first stage, we design a simple prior conditional diffusion model that predicts the global features of the target image by mining the global alignment relationship between pose coordinates and image appearance. Then, the second stage establishes a dense correspondence between the source and target images using the global features from the previous stage, and an inpainting conditional diffusion model is proposed to further align and enhance the contextual features, generating a coarse-grained person image. In the third stage, we propose a refining conditional diffusion model to utilize the coarsely generated image from the previous stage as a condition, achieving texture restoration and enhancing fine-detail consistency. The three-stage PCDMs work progressively to generate the final high-quality and high-fidelity synthesized image. Both qualitative and quantitative results demonstrate the consistency and photorealism of our proposed PCDMs under challenging scenarios.The code and model will be available at https://github.com/tencent-ailab/PCDMs.

• The paper presents a three-stage approach that progressively refines global feature alignment, inpainting, and detailed restoration for pose-guided image synthesis.
• It employs CLIP embeddings and a cross-attention mechanism with DINOv2 features, achieving superior performance in SSIM, LPIPS, and FID over existing methods.
• The technique demonstrates practical benefits in applications like person re-identification and paves the way for optimizing efficiency in complex image synthesis tasks.

An Expert Review of "Advancing Pose-Guided Image Synthesis with Progressive Conditional Diffusion Models"

The paper "Advancing Pose-Guided Image Synthesis with Progressive Conditional Diffusion Models" introduces a sophisticated technique for generating pose-guided person images. This method capitalizes on the strengths of Progressive Conditional Diffusion Models (PCDMs) structured in a three-stage process, designed to tackle the intrinsic challenges posed by pose disparities between source and target images in person image synthesis.

Methodology Overview

The PCDMs are delineated into three distinct phases to progressively refine the image synthesis process:

1. Prior Conditional Diffusion Model: This initial stage focuses on predicting the global features of the target image by extracting the global alignment relationships between pose coordinates and image appearance. Utilizing CLIP embeddings allows the model to capture rich image content and style, serving as a foundational step for subsequent synthesis stages.
2. Inpainting Conditional Diffusion Model: In the second stage, the model aims to establish dense correspondences between the source and target images, leading to a coherent transfer of pose and appearance. By aligning inputs at image, pose, and feature levels, this stage addresses the issues commonly observed in previous methodologies, where unaligned image-to-image generation could lead to distorted and unrealistic outcomes.
3. Refining Conditional Diffusion Model: The final stage employs the refinement of coarse-grained images generated from earlier steps, emphasizing texture restoration and fine-detail consistency. By leveraging a cross-attention mechanism and integrating features from DINOv2, this phase significantly enhances image quality and fidelity.

Results and Implications

The proposed methodology demonstrates superior performance across multiple metrics (SSIM, LPIPS, FID) when compared to state-of-the-art methods. Qualitative assessments reveal that PCDMs consistently outperform existing techniques in generating realistic and high-fidelity images, particularly in scenarios involving complex textures and poses.

The authors also conducted user studies to measure the subjective quality of the generated images, reinforcing the objective findings with perceptual evaluations. Notably, the refining conditional diffusion model not only improves images synthesized by PCDMs but also enhances outputs from other existing methods.

The paper suggests practical applications of the synthesized images, notably in the field of person re-identification, where PCDMs show remarkable improvements in performance. The potential to directly boost downstream task efficacy via improved synthetic data quality underscores the broader applicability of the proposed approach beyond mere image synthesis.

Future Directions

While the PCDMs framework significantly advances pose-guided image synthesis, the authors acknowledge inherent trade-offs in computational resource demands and inference times due to multi-stage processing. Future research should aim to optimize these stages for efficiency, perhaps through the development of more streamlined models or innovative training strategies, to enhance the practical deployment of such sophisticated methodologies in resource-constrained environments.

In conclusion, the methodology presented in this paper represents an elegant integration of diffusion models into the domain of pose-guided image synthesis, offering both theoretical insights and practical enhancements in image generation tasks. Further investigation and refinement could expand its applications, paving the way for more versatile and efficient tools in this field, contributing significantly to both academic exploration and industrial applications.