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MaPPA: Multimodal Controllable Person Image Generation With Pose and Appearance Guidance
Summary
This study introduces MaPPA, a multimodal framework for person image generation, enabling flexible control using text, pose, and appearance guidance. MaPPA offers enhanced controllability and detail preservation for realistic virtual try-on and content creation applications.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Machine Learning
Background:
- Current person image generation models face limitations due to rigid modality requirements, restricting flexibility and controllability.
- Existing pipelines often rely on fixed input patterns (e.g., pose-plus-appearance or text-only), hindering practical applications requiring mixed guidance.
Purpose of the Study:
- To propose MaPPA (Multimodal Controllable Person Image Generation with Pose and Appearance Guidance), a unified framework for flexible and controllable person image synthesis.
- To enable generation conditioned on arbitrary combinations of text, appearance images, and pose keypoints within a single pipeline.
Main Methods:
- Developed a unified multimodal framework leveraging transformer-based latent diffusion models.
- Introduced dedicated control blocks for appearance and pose guidance, integrated with transformer base blocks.
- Implemented a cumulative classifier-free guidance strategy for independent adjustment of appearance and pose guidance strengths.
- Proposed a texture enhancement decoding (TED) strategy to improve detail preservation in generated images.
Main Results:
- MaPPA achieves competitive quantitative scores and superior perceptual quality compared to existing methods.
- The framework demonstrates enhanced controllability, allowing fine-grained adjustment of guidance modalities.
- The texture enhancement decoding strategy effectively alleviates texture distortion and preserves high-frequency details.
Conclusions:
- MaPPA offers a flexible and practical solution for multimodal person image generation, overcoming limitations of previous approaches.
- The unified pipeline supports combinations of text, pose, and appearance modalities, demonstrating significant practical value.
- The proposed methods enhance controllability and image quality, making it suitable for diverse applications like virtual try-on and digital content creation.
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