PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization (1905.05172v3)

Abstract: We introduce Pixel-aligned Implicit Function (PIFu), a highly effective implicit representation that locally aligns pixels of 2D images with the global context of their corresponding 3D object. Using PIFu, we propose an end-to-end deep learning method for digitizing highly detailed clothed humans that can infer both 3D surface and texture from a single image, and optionally, multiple input images. Highly intricate shapes, such as hairstyles, clothing, as well as their variations and deformations can be digitized in a unified way. Compared to existing representations used for 3D deep learning, PIFu can produce high-resolution surfaces including largely unseen regions such as the back of a person. In particular, it is memory efficient unlike the voxel representation, can handle arbitrary topology, and the resulting surface is spatially aligned with the input image. Furthermore, while previous techniques are designed to process either a single image or multiple views, PIFu extends naturally to arbitrary number of views. We demonstrate high-resolution and robust reconstructions on real world images from the DeepFashion dataset, which contains a variety of challenging clothing types. Our method achieves state-of-the-art performance on a public benchmark and outperforms the prior work for clothed human digitization from a single image.

• The paper introduces PIFu, a novel method that integrates pixel-aligned 2D features with implicit 3D representations for high-resolution digitization.
• It leverages CNN-based feature extraction and implicit occupancy prediction to capture intricate geometry and textures from a single RGB image.
• Experimental results show PIFu outperforms prior approaches, effectively handling complex clothing and occluded regions with superior fidelity.

PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization

The paper "Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization" introduces an innovative approach denoted as Pixel-aligned Implicit Function (PIFu), which reconstructs high-fidelity 3D textured surfaces of clothed humans from a single RGB image. This work, carried out by researchers from multiple esteemed institutions including the University of Southern California and the University of California, Berkeley, represents a significant advancement in the field of 3D human digitization.

Methodology

The crux of the proposed method lies in the pixel-aligned implicit function, which seamlessly integrates 2D image features with implicit 3D surface representations. This integration enables precise recovery of fine geometric details and textures directly from the input image.

Key components of the PIFu framework include:

1. Feature Extraction: Convolutional Neural Networks (CNNs) extract pixel-aligned local image features.
2. Implicit Function: These features are then fed into an implicit function that learns the association between 2D pixel-aligned features and the 3D occupancy field.
3. Occupancy Prediction: The model predicts whether a 3D point, specified in a continuous manner, lies on the surface of the object.

The approach leverages both global and local image cues to accurately predict detailed surface geometry and texture, even in regions that are largely occluded in the input image.

Results

The experimental evaluation shows that PIFu significantly outperforms previous state-of-the-art methods in terms of both geometric details and texture fidelity. The method was tested on complex clothing scenarios such as wrinkled skirts, detailed high-heels, and intricate hairstyles. Crucially, PIFu's inherent ability to handle intricate textures without explicit 3D supervision is a noteworthy achievement. Additionally, the method can be extended to multi-view inputs, further enhancing the reconstruction completeness and accuracy.

Quantitatively, the results demonstrate high-resolution digitization with robust performance across various human poses and clothing styles. Detailed comparisons with existing mesh-based and voxel-based approaches indicate superior performance in capturing fine details and maintaining higher fidelity to the input image.

Implications and Future Directions

This paper contributes to the theoretical understanding of implicit function representations in computer vision, particularly their application in 3D human digitization from 2D inputs. Practically, PIFu opens new avenues for applications in virtual reality, gaming, and digital fashion, where high-quality 3D human models are critical.

Future directions may include:

1. Scalability: Enhancing the model to process larger datasets more efficiently.
2. Real-world Deployment: Overcoming practical challenges in deploying these models in real-time systems.
3. Generalizability: Extending the method to general object categories beyond human digitization.
4. Augmented Reality: Integrating with augmented reality platforms to provide immersive user experiences.

Given the versatility and high accuracy of PIFu, it sets a strong foundation for subsequent research in high-resolution 3D reconstruction, and it is likely that we will see continued iterations and improvements based on this framework.

In summary, this paper makes a substantial contribution to the field of computer vision by presenting a robust and high-resolution method for clothed human digitization, with broad implications for both theoretical advancements and practical applications. The successful integration of pixel-aligned features with implicit functions stands out as an exemplary approach to solving complex 3D reconstruction problems.