Coarse-to-Fine Latent Diffusion for Pose-Guided Person Image Synthesis: A Novel Approach

Core Concepts

The author proposes a novel Coarse-to-Fine Latent Diffusion method for Pose-Guided Person Image Synthesis, aiming to overcome overfitting by decoupling fine-grained appearance and pose information controls. This approach enhances semantic understanding and texture details in generated images.

Abstract

The paper introduces the Coarse-to-Fine Latent Diffusion (CFLD) method for Pose-Guided Person Image Synthesis, focusing on overcoming overfitting issues by separating appearance and pose controls. By utilizing a Perception-Refined Decoder and Hybrid-Granularity Attention module, the method achieves state-of-the-art results both quantitatively and qualitatively on the DeepFashion benchmark.
The proposed method breaks conventional training paradigms by leveraging image-based prompts instead of textual cues, enhancing semantic understanding of person images. By refining learnable queries progressively, a coarse-grained prompt is generated to guide image synthesis effectively. The Hybrid-Granularity Attention module encodes multi-scale appearance features to control texture details, resulting in realistic image generation.
Extensive experiments demonstrate that CFLD outperforms existing methods in terms of image quality metrics such as FID, LPIPS, SSIM, and PSNR. User studies confirm the superiority of CFLD in generating realistic images with better alignment and texture preservation compared to state-of-the-art approaches.

Stats

Both quantita-
tive and qualitative experimental results on the DeepFash-
ion benchmark demonstrate the superiority of our method
over the state of the arts for PGPIS.
Code is available at
https://github.com/YanzuoLu/CFLD.
Our main contributions can be summarized as follows,
• We present a novel training paradigm in the absence of
image-caption pairs to overcome the limitations when
applying text-to-image diffusion to PGPIS.
• We formulate a new hybrid-granularity attention module
to bias the coarse-grained prompt with fine-grained ap-
pearance features.
• We conduct extensive experiments on the DeepFash-
ion [20] benchmark and achieve the state-of-the-art per-
formance both quantitatively and qualitatively.

Quotes

"Our main contributions can be summarized as follows."
"Both quantitative and qualitative experimental results on the DeepFashion benchmark demonstrate the superiority of our method."

Key Insights Distilled From

Coarse-to-Fine Latent Diffusion for Pose-Guided Person Image Synthesis

by Yanzuo Lu,Ma... at arxiv.org 02-29-2024

https://arxiv.org/pdf/2402.18078.pdf

Deeper Inquiries

How can Coarse-to-Fine Latent Diffusion be applied to other image synthesis tasks beyond PGPIS?

Coarse-to-Fine Latent Diffusion (CFLD) can be applied to various image synthesis tasks beyond Pose-Guided Person Image Synthesis (PGPIS) by adapting the methodology to different domains and requirements. Here are some ways CFLD could be utilized in other image synthesis tasks:

Style Transfer: CFLD's ability to generate realistic texture details while aligning with target poses makes it suitable for style transfer tasks. By leveraging the coarse-grained prompts and fine-grained appearance biasing, CFLD can smoothly transition between different styles in images.

Image Editing: The decoupling of controls for fine-grained appearance and pose information allows for precise editing capabilities in images. This feature could be beneficial in applications where specific attributes need to be adjusted or modified without affecting overall image quality.

Texture Generation: CFLD's hybrid-granularity attention module enables the generation of intricate textures, making it ideal for tasks that require detailed texture synthesis such as fabric patterns, natural landscapes, or artistic designs.

Interpolation: The ability of CFLD to interpolate between different styles or features gradually could be valuable in creating smooth transitions between images or generating variations within a dataset.

Conditional Image Generation: By conditioning on specific inputs or constraints, CFLD can generate images based on predefined criteria such as textual descriptions, attribute labels, or semantic cues.

Overall, the flexibility and adaptability of Coarse-to-Fine Latent Diffusion make it a versatile approach that can enhance various image synthesis tasks across different domains.

What are potential drawbacks or limitations of decoupling fine-grained appearance and pose information controls?

While decoupling fine-grained appearance and pose information controls offers several advantages in terms of generalization performance and overfitting prevention, there are also potential drawbacks and limitations associated with this approach:

Loss of Fine Details: Decoupling these controls may lead to a loss of fine details in generated images since the model focuses more on aligning poses rather than capturing intricate textures or subtle nuances present in source images.

Complexity Increase: Managing separate control mechanisms for appearance and pose information adds complexity to the model architecture and training process, which could result in longer training times and increased computational resources required.

Semantic Misalignment: There is a risk that separating these controls too rigidly may cause semantic misalignments between appearances and poses in generated images, leading to inconsistencies or unrealistic outputs.

Limited Flexibility: Decoupling controls might limit the flexibility of the model when handling diverse datasets with varying levels of detail requirements for both appearance and pose adjustments.

Dependency on Training Data Distribution: The effectiveness of decoupled controls heavily relies on having representative training data that covers a wide range of appearances and poses; otherwise, the model may struggle with novel scenarios not encountered during training.

How might advances in semantic understanding impact future developments in image synthesis technology?

Advances in semantic understanding have significant implications for future developments in image synthesis technology by enhancing realism, interpretability, controllability, diversity, efficiency among others:
1-Realism Enhancement: Improved semantic understanding enables models to generate more realistic images by incorporating high-level concepts like object relationships,
scene context etc., resultingin visually coherent outputs.
2-Interpretability: Semantic understanding allows users better insight into how models operate enabling themto understand why certain decisions were made during
the generation process.
3-Controllability: Models equippedwith advancedsemanticunderstandingcan offermorefine-tunedcontrol overgeneratedoutputsallowingusers toprescribeparticularattributesorfeatures they wantto seein synthesizedimages.
4-Efficiency: Semantic-awaremodelsareabletoproducehigh-qualityresultsusinglessdataandcomputationalsourcesbyleveragingprior knowledgeaboutthesemanticstructureoftheinputspace.
5-GeneralizationCapability: Advancedsemanticunderstandinghelpsmodelsgeneralizebetteracrossdiversedatasetsandconditionsbycapturinghigher-orderrelationshipsandreducingoverfittingrisks
6-**Cross-modalGeneration:**Semanticunderstandingfacilitatescross-modalgenerationtasks(suchastext-to-image,image-captioningetc.)byenablingamodeltointerpretandgeneratecontentbasedonsemanticrootsratherthanjustrawpixelvalues
In conclusion,the integrationofadvancedsemanticunderstandingintomodernimagerepresentationandgenerationmodelsisexpectedtofosterinnovationsandsignificantimprovementsinvariousaspectsofimagesynthesis technologiesleadingtoa neweraofrealistic,intuitive,andcontrollableimagecreationcapabilities