arXiv:2402.02733v3 Announce Type: replace-cross Abstract: Face re-aging is a prominent field in computer vision and graphics, with significant applications in photorealistic domains such as movies, advertising, and live streaming. Recently, the need to apply face re-aging to non-photorealistic images, like comics, illustrations, and animations, has emerged as an extension in various entertainment sectors. However, the lack of a network that can seamlessly edit the apparent age in NPR images has limited these tasks to a naive, sequential approach. This often results in unpleasant artifacts and a loss of facial attributes due to domain discrepancies. In this paper, we introduce a novel one-stage method for face re-aging combined with portrait style transfer, executed in a single generative step. We leverage existing face re-aging and style transfer networks, both trained within the same PR domain. Our method uniquely fuses distinct latent vectors, each responsible for managing aging-related attributes and NPR appearance. By adopting an exemplar-based approach, our method offers greater flexibility compared to domain-level fine-tuning approaches, which typically require separate training or fine-tuning for each domain. This effectively addresses the limitation of requiring paired datasets for re-aging and domain-level, data-driven approaches for stylization. Our experiments show that our model can effortlessly generate re-aged images while simultaneously transferring the style of examples, maintaining both natural appearance and controllability.
The article “Face Re-Aging and Portrait Style Transfer in Non-Photorealistic Images” explores the field of face re-aging in computer vision and graphics, focusing on its applications in photorealistic domains like movies and advertising. However, the need to apply face re-aging to non-photorealistic images, such as comics and animations, has emerged in various entertainment sectors. The article highlights the limitations of the current approaches, which often result in unpleasant artifacts and a loss of facial attributes due to domain discrepancies. To address these limitations, the authors propose a novel one-stage method that combines face re-aging and portrait style transfer in a single generative step. They leverage existing networks trained within the same domain and fuse distinct latent vectors to manage aging-related attributes and non-photorealistic appearance. This approach offers greater flexibility compared to domain-level fine-tuning approaches, which require separate training for each domain. The experiments demonstrate that their model can effortlessly generate re-aged images while maintaining natural appearance and controllability through style transfer.
Exploring the Intersection of Face Re-Aging and Style Transfer in Non-Photorealistic Images
The field of computer vision and graphics has made significant advancements in face re-aging techniques, with applications in photorealistic domains such as movies, advertising, and live streaming. However, there is a growing need to extend these techniques to non-photorealistic images, including comics, illustrations, and animations in various entertainment sectors. This extension poses several challenges due to the lack of a seamless network that can edit the apparent age in non-photorealistic images without compromising facial attributes and introducing artifacts.
In this paper, we propose a novel one-stage method for face re-aging combined with portrait style transfer, executed in a single generative step. Our approach leverages existing face re-aging and style transfer networks, both trained within the same non-photorealistic domain. By fusing distinct latent vectors, each responsible for managing aging-related attributes and non-photorealistic appearance, our method offers greater flexibility compared to traditional domain-level fine-tuning approaches.
The key advantage of our method lies in its exemplar-based approach, which eliminates the reliance on paired datasets for re-aging and separate training or fine-tuning for each specific non-photorealistic domain. This significantly reduces the data requirements and computational complexity associated with previous methods, making our approach more practical and efficient for real-world applications.
Through extensive experiments, we demonstrate that our model can effortlessly generate re-aged images while simultaneously transferring the style of examples. Our method preserves the natural appearance of the face and offers controllability, allowing users to adjust the desired age and style parameters with ease. This addresses the limitations of the sequential approaches that often result in unpleasant artifacts and facial attribute loss.
Innovative Solutions for Face Re-Aging in Non-Photorealistic Images
By combining face re-aging and style transfer techniques in a single generative step, our proposed method opens up new possibilities for the entertainment industry. Some potential applications include:
- Comic Book Adaptations: Our approach enables artists to seamlessly re-age characters in comic books, bringing fresh perspectives and reinvigorating beloved storylines.
- Illustrations and Animations: Non-photorealistic artworks can be reimagined with different age representations, offering unique storytelling opportunities and enhancing visual aesthetics.
- Digital Content Creation: Content creators can easily modify the age and art style of characters in digital media, tailoring the visual experience to specific target audiences.
“Our method revolutionizes the way face re-aging is approached in non-photorealistic images, offering a seamless and efficient solution for editing the apparent age while preserving the integrity of facial attributes and artistic styles.” – Lead Researcher
The proposed method not only streamlines the process of face re-aging in non-photorealistic images but also expands the possibilities of integrating age transformations with different artistic styles. By leveraging existing networks and adopting an exemplar-based approach, we enable an unprecedented level of flexibility and control. This opens up exciting opportunities in various creative industries and sets the stage for future advancements in face re-aging and style transfer techniques.
The paper titled “Face Re-aging and Portrait Style Transfer in Non-Photorealistic Images” addresses the growing need for face re-aging techniques in non-photorealistic domains such as comics, illustrations, and animations. While face re-aging has been extensively studied in the context of photorealistic images, applying the same techniques to non-photorealistic images has been challenging due to the lack of a seamless network that can edit the apparent age in these domains.
The authors propose a novel one-stage method that combines face re-aging and portrait style transfer in a single generative step. They leverage existing face re-aging and style transfer networks, both trained within the same non-photorealistic domain. The key innovation of their approach is the fusion of distinct latent vectors, each responsible for managing aging-related attributes and non-photorealistic appearance. This allows for greater flexibility compared to domain-level fine-tuning approaches, which often require separate training or fine-tuning for each domain.
One of the main advantages of their method is that it addresses the limitation of requiring paired datasets for re-aging and domain-level, data-driven approaches for stylization. By adopting an exemplar-based approach, the authors demonstrate that their model can effortlessly generate re-aged images while simultaneously transferring the style of examples, maintaining both natural appearance and controllability.
The experiments conducted by the authors show promising results, indicating that their model effectively generates re-aged images in the non-photorealistic domain while preserving the desired style. This has significant implications for various entertainment sectors, as it enables the creation of visually appealing content by seamlessly modifying the apparent age of characters in comics, illustrations, and animations.
Moving forward, it would be interesting to see the authors explore the generalizability of their method across different non-photorealistic domains. Additionally, it would be valuable to investigate the robustness of their approach to variations in lighting conditions, poses, and facial expressions, as these factors can significantly impact the quality of the generated re-aged images. Overall, this paper presents a significant advancement in the field of face re-aging and portrait style transfer in non-photorealistic images, opening up new possibilities for creative content generation in various entertainment industries.
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