VToonify: Controllable High-Resolution Portrait Video Style Transfer (2209.11224v3)

Abstract: Generating high-quality artistic portrait videos is an important and desirable task in computer graphics and vision. Although a series of successful portrait image toonification models built upon the powerful StyleGAN have been proposed, these image-oriented methods have obvious limitations when applied to videos, such as the fixed frame size, the requirement of face alignment, missing non-facial details and temporal inconsistency. In this work, we investigate the challenging controllable high-resolution portrait video style transfer by introducing a novel VToonify framework. Specifically, VToonify leverages the mid- and high-resolution layers of StyleGAN to render high-quality artistic portraits based on the multi-scale content features extracted by an encoder to better preserve the frame details. The resulting fully convolutional architecture accepts non-aligned faces in videos of variable size as input, contributing to complete face regions with natural motions in the output. Our framework is compatible with existing StyleGAN-based image toonification models to extend them to video toonification, and inherits appealing features of these models for flexible style control on color and intensity. This work presents two instantiations of VToonify built upon Toonify and DualStyleGAN for collection-based and exemplar-based portrait video style transfer, respectively. Extensive experimental results demonstrate the effectiveness of our proposed VToonify framework over existing methods in generating high-quality and temporally-coherent artistic portrait videos with flexible style controls.

• The paper introduces VToonify, a framework that overcomes fixed-crop limitations by translating feature layers and noise inputs to generate high-resolution, unaligned portrait video frames.
• The paper leverages a fully convolutional encoder-generator architecture with multi-scale style and content integration to ensure detailed facial textures and flicker-free temporal consistency.
• The paper demonstrates both collection-based and exemplar-based approaches, offering users flexible control over artistic styles in portrait video transfers.

An In-Depth Analysis of VToonify Framework for High-Resolution Portrait Video Style Transfer

The paper "VToonify: Controllable High-Resolution Portrait Video Style Transfer" addresses the limitations of existing image-based style transfer methods when applied to video data. The paper introduces VToonify, a framework designed to perform high-quality artistic style transfers on portrait videos. The primary innovation is in extending the capabilities of StyleGAN, traditionally used for image generation, to handle unaligned and variably sized video frames, maintaining temporal consistency across sequences.

Key Features of VToonify

The VToonify framework stands out due to its novel combination of multi-scale content features and style conditions within the StyleGAN framework. This combination allows for high-resolution video outputs with stylistic flexibility. VToonify leverages the mid- and high-resolution layers of StyleGAN, which are adept at handling detailed facial textures, while the encoder extracts content features from each video frame, ensuring that transient details are preserved.

VToonify is compatible with existing StyleGAN-based models, extending their capabilities from static images to dynamic video content. The framework is implemented through two instantiations:

• Collection-based VToonify, utilizing Toonify, which focuses on applying an average style from a style collection.
• Exemplar-based VToonify, built on DualStyleGAN, enabling more granular control by using an exemplar image for reference, allowing fine-level style adjustments, including color and intensity modifications.

Technical Contributions

1. Translation Equivariance: The authors overcome the fixed-crop limitation of StyleGAN by translating both the feature layers and noise inputs, allowing the generation of unaligned and variably sized portrait frames.
2. Fully Convolutional Architecture: By discarding the fixed-sized input feature of StyleGAN, VToonify introduces a fully convolutional encoder-generator architecture, accommodating video frames of varying dimensions.
3. Temporal Consistency: A flicker suppression loss is introduced, simulating camera motion over a single frame without requiring complex video synthesis or optical flow calculations, effectively eliminating temporal artifacts.
4. Data and Model Distillation: The framework is trained using paired data synthesized through existing StyleGAN variations, distilling both the data and model to enable efficient style transfer in videos.

Implications and Future Directions

Practically, VToonify offers substantial advancements in personalized media by facilitating high-quality video style transfers in creative industries, social media content, and digital art production. The potential to extend this work includes:

• Broader GAN Applications: By distilling StyleGAN-like models to video applications, VToonify opens pathways for similar strategies in other generative tasks.
• Enhanced StyleGAN Inversion: The integration of multi-scale features can inform future research on StyleGAN inversion, potentially improving detail preservation in complex scenes.
• Interactive Media Design: With its flexible style control, VToonify paves the way for more interactive design tools where users dynamically choose style parameters in real-time.

In conclusion, this paper provides a substantive contribution to the domain of artistic video style transfers, leveraging the architectural strengths of StyleGAN for dynamic video content. It demonstrates innovative solutions to key challenges in video style transfer, setting a robust foundation for future advancements in high-resolution and style-flexible video generation.