Intuitive, Interactive Beard and Hair Synthesis with Generative Models (2004.06848v1)

Abstract: We present an interactive approach to synthesizing realistic variations in facial hair in images, ranging from subtle edits to existing hair to the addition of complex and challenging hair in images of clean-shaven subjects. To circumvent the tedious and computationally expensive tasks of modeling, rendering and compositing the 3D geometry of the target hairstyle using the traditional graphics pipeline, we employ a neural network pipeline that synthesizes realistic and detailed images of facial hair directly in the target image in under one second. The synthesis is controlled by simple and sparse guide strokes from the user defining the general structural and color properties of the target hairstyle. We qualitatively and quantitatively evaluate our chosen method compared to several alternative approaches. We show compelling interactive editing results with a prototype user interface that allows novice users to progressively refine the generated image to match their desired hairstyle, and demonstrate that our approach also allows for flexible and high-fidelity scalp hair synthesis.

• The paper introduces a two-stage neural network architecture that synthesizes facial hair interactively in under one second.
• It employs user-drawn guide strokes to provide intuitive control over hair structure and color during synthesis.
• Results demonstrate high-fidelity outputs with superior performance over traditional methods using adversarial and perceptual loss functions.

An Evaluation of Interactive Beard and Hair Synthesis Using Generative Models

The paper "Intuitive, Interactive Beard and Hair Synthesis with Generative Models" presents a sophisticated method for synthesizing and editing facial hair in digital images. It offers a practical solution through the application of generative adversarial networks (GANs) for realistic hair synthesis. The authors, leveraging advancements in neural networks, propose a novel two-stage network architecture for this purpose. This research aims to address the challenges in facial hair synthesis, which traditionally involves computationally expensive processes in 3D modeling and rendering.

Overview and Methodology

The researchers introduce a method that allows users to interactively synthesize a variety of facial hair styles by employing a neural network pipeline that operates efficiently, producing results in under one second. This is achieved through a user interface that facilitates the use of "guide strokes," which are essentially user-drawn lines indicating desired hair structure and color. The system synthesizes hair that adheres to these strokes, providing users with the ability to make subtle edits or produce complex styles as needed.

The synthesis process is divided into two distinct stages. The first stage involves the initial synthesis of facial hair, utilizing a network that processes input in the form of segmentation masks and the aforementioned guide strokes. The second stage refines the initial output and integrates the newly synthesized hair back into the input image seamlessly.

Technical Contributions and Dataset

A key aspect of this work is the creation of a substantial synthetic dataset, vital for training the proposed network architecture. This dataset comprises variations in hair styles, colors, and orientations, generated using the Daz 3D modeling platform, along with a smaller set of real images to enhance generalizability.

The approach also incorporates a perceptual loss, leveraging VGG-19 based features, and an adversarial loss, to ensure high-quality synthesis outputs that are perceptually similar to real images. This multi-faceted loss function allows the network to focus on salient visual features that contribute to realistic facial hair synthesis.

Results and Comparative Analysis

The results demonstrate that the proposed method excels in synthesizing diverse hair styles, displaying high fidelity in real-time applications. This is confirmed through qualitative results and experimental evaluations, including a perceptual paper and user feedback. The synthesized outputs were consistently realistic and coherent with user-provided inputs, whether for creating new hair styles on clean-shaven images or editing existing facial hair.

Comparative analysis highlights the advantages over traditional copy-paste methods, which suffer from issues in matching colors, orientations, and lighting. The proposed approach, however, provides users with nuanced control over these aspects. Additionally, the method was compared against Brushables, a texture synthesis tool, where it showed superior performance in synthesizing realistic hair textures directly aligned with user intentions.

Implications and Future Work

The implications of this research are manifold, with potential applications in digital media, law enforcement, personal grooming simulations, and beyond. From a theoretical perspective, it expands the possibilities of interactive image synthesis using generative models, offering insights into user-guided neural model training.

Future iterations of this work could explore extensions to other hair types or transition into non-hair domains such as fabric or terrain synthesis for augmented reality applications. Additionally, expanding the dataset to include a broader range of hairstyles and ethnicities could improve the model's adaptability and realism.

In summary, this paper contributes a robust framework for facial hair synthesis, setting a precedent for future research in interactive image synthesis using generative models, and encouraging ongoing exploration into more generalized applications of this technology.