Neural Texture Extraction and Distribution for Controllable Person Image Synthesis (2204.06160v1)

Abstract: We deal with the controllable person image synthesis task which aims to re-render a human from a reference image with explicit control over body pose and appearance. Observing that person images are highly structured, we propose to generate desired images by extracting and distributing semantic entities of reference images. To achieve this goal, a neural texture extraction and distribution operation based on double attention is described. This operation first extracts semantic neural textures from reference feature maps. Then, it distributes the extracted neural textures according to the spatial distributions learned from target poses. Our model is trained to predict human images in arbitrary poses, which encourages it to extract disentangled and expressive neural textures representing the appearance of different semantic entities. The disentangled representation further enables explicit appearance control. Neural textures of different reference images can be fused to control the appearance of the interested areas. Experimental comparisons show the superiority of the proposed model. Code is available at https://github.com/RenYurui/Neural-Texture-Extraction-Distribution.

• The paper introduces the NTED operation, a double attention-based method that extracts and distributes neural textures for controllable person image synthesis.
• It achieves state-of-the-art performance on the DeepFashion dataset, demonstrating improved SSIM, LPIPS, and FID metrics.
• The approach allows flexible manipulation of semantic features like facial details and clothing, ensuring realistic and fine-grained image synthesis.

An Analysis of "Neural Texture Extraction and Distribution for Controllable Person Image Synthesis"

In the paper titled "Neural Texture Extraction and Distribution for Controllable Person Image Synthesis," the authors introduce a novel approach for generating person images with explicit control over pose and appearance. This research addresses a significant challenge in the area of computer vision: synthesizing realistic human images based on reference images with specified poses and appearances. The method leverages neural texture extraction and distribution, employing a double attention mechanism to efficiently manage this task.

The core innovation of this paper is the proposed Neural Texture Extraction and Distribution (NTED) operation, which effectively manipulates the reference image by extracting and distributing semantic entities such as facial features, clothing, and other attributes. This operation is designed to accommodate transformations while preserving or reconstructing the desired textures. The NTED approach tackles the limitations of convolutional networks, which often struggle with spatial transformations and long-range dependencies, as well as the inefficiencies of flow-based methods, which can induce artifacts during complex deformations or occlusions.

Key Methodological Insights

1. Neural Texture Extraction via Double Attention: The NTED operation first extracts semantic neural textures from reference feature maps using attention mechanisms that learn to focus on different regions of the input images. The textures are then selectively distributed according to spatial configurations derived from target poses, addressing the inefficiency in correlation computation seen in traditional attention mechanisms.
2. Disentangled and Expressive Representations: The NTED approach is adept at producing disentangled representations of various semantic entities. This allows for explicit control over appearance attributes like clothing, enabling fine manipulation of specific image regions without impacting unrelated areas.
3. Hierarchical Modeling in Person Image Synthesis: The paper integrates the NTED operation within a generative model framework, which synthesizes images through a hierarchical process involving multi-scale neural feature deformations. This ensures localized as well as holistic realism in the generated images.

Experimental Evaluation

The authors demonstrate the effectiveness of their approach on the DeepFashion dataset, achieving state-of-the-art results in terms of both image quality and objective metrics such as Structural Similarity Index Measure (SSIM), Learned Perceptual Image Patch Similarity (LPIPS), and Fréchet Inception Distance (FID). These metrics validate the proposed model's ability to produce visually coherent images that convincingly emulate real-world photographs.

Implications and Future Research Directions

The NTED framework offers a promising stride towards creating more controllable and authentic human image synthesis systems, especially beneficial in industries involving virtual reality, e-commerce, and digital communication. By achieving a remarkable balance of quality and computational efficiency, this work sets the stage for further exploration in several directions:

• Scalability and Complexity Reduction: While the proposed method improves on traditional approaches, further reducing computational demands would enhance real-time application potential.
• Broader Semantic Understanding: Extending the model to more diverse categories of semantic entities could broaden its applicability in other domains, such as animation or interactive gaming.
• Model Robustness and Generalization: Addressing failure cases, particularly in less-represented scenarios, remains vital for operational deployment. Future research can focus on enhancing generalization to accommodate a wider variety of poses and garment styles.

In summary, the paper presents a noteworthy advancement in controllable person image synthesis, with its NTED operation offering a sophisticated balance between flexibility and realism. While the work primarily explores synthesis from fashion imagery, its foundational principles could extend to broader contexts, signaling a substantial opportunity for future advancements in AI-driven visual content generation.