Collaborative Neural Rendering using Anime Character Sheets (2207.05378v5)

Abstract: Drawing images of characters with desired poses is an essential but laborious task in anime production. Assisting artists to create is a research hotspot in recent years. In this paper, we present the Collaborative Neural Rendering (CoNR) method, which creates new images for specified poses from a few reference images (AKA Character Sheets). In general, the diverse hairstyles and garments of anime characters defies the employment of universal body models like SMPL, which fits in most nude human shapes. To overcome this, CoNR uses a compact and easy-to-obtain landmark encoding to avoid creating a unified UV mapping in the pipeline. In addition, the performance of CoNR can be significantly improved when referring to multiple reference images, thanks to feature space cross-view warping in a carefully designed neural network. Moreover, we have collected a character sheet dataset containing over 700,000 hand-drawn and synthesized images of diverse poses to facilitate research in this area. Our code and demo are available at https://github.com/megvii-research/IJCAI2023-CoNR.

• The paper presents a novel Collaborative Neural Rendering framework that uses character sheets and Ultra-Dense Pose to generate precise anime poses.
• It formulates the task with a dataset exceeding 700,000 images and employs CNN-based U-Net architectures with cross-view warping for refined outputs.
• The methodology outperforms traditional sparse and parametric models, as shown by notable improvements in LPIPS and visual fidelity metrics.

Collaborative Neural Rendering Using Anime Character Sheets: An Overview

The research paper titled "Collaborative Neural Rendering Using Anime Character Sheets" introduces an innovative method known as Collaborative Neural Rendering (CoNR) to assist in the anime production process by rendering character images in desired poses. The paper addresses significant challenges in the field of non-photorealistic rendering by leveraging character sheets as a basis for generating new poses.

Key Contributions

The paper makes several noteworthy contributions:

1. Task Formulation and Dataset Creation: The paper formalizes the task of rendering anime characters from character sheets—a collection of images depicting a character in various poses. Additionally, the authors introduce a comprehensive dataset consisting of over 700,000 hand-drawn and 3D-synthesized images, which serves as a valuable resource for further research.
2. Ultra-Dense Pose (UDP): To overcome the inadequacies of existing human body models (e.g., SMPL) that fail to capture the complexity of anime characters, the paper proposes the Ultra-Dense Pose (UDP) representation. UDP acts as a detailed encoding of body surfaces, offering superior artistic control over features such as clothing and accessories, which are pivotal for anime.
3. Collaborative Neural Rendering Framework: The CoNR model utilizes a Collaborative Inference Neural Network (CINN) renderer. This architecture capitalizes on the information from multiple reference images and introduces a feature space cross-view warping technique that enhances the rendering quality.

Methodology

The methodology outlined involves a multi-step pipeline. The CoNR model processes images from character sheets and utilizes their UDPs to produce the desired pose and visual output. A significant feature is the use of convolutional neural networks (CNNs) with U-Net architectures that are tailored with warping operations and collaborative inference techniques to refine the output at multiple stages.

Renderer Enhancements

The CoNR employs feature warping and message-passing among network branches, improving the alignment and collaborative processing of reference data. This advancement allows the model to create photo-realistic and pose-accurate images by aligning features across different views.

Results and Analysis

Extensive experiments demonstrate that using multiple reference images enhances the rendering quality significantly. The paper provides quantitative analysis through metrics such as $\mathcal{L}_{photo}$ and LPIPS to evaluate visual fidelity.

• Impact of Reference Images: Increased reference views ($m>1$) during training resulted in a noticeable improvement in visual accuracy and coherence of rendered images.
• Comparative Insights: The proposed CoNR method outperforms traditional frameworks reliant on sparse representations and parametric models, which fail to encapsulate the diverse visual and structural characteristics of anime characters.

Implications and Future Directions

The implications of this research are twofold: practical improvements in the anime production pipeline—leading to more efficient and consistent rendering processes—and theoretical advancements in image synthesis from artistic references. The use of UDP and CINN sets a foundation for future exploration in creative AI, potentially influencing domains beyond animation, such as virtual reality and interactive media.

Future work may delve into dynamic modeling of characters, addressing UDP consistency issues across various body shapes, and expanding the dataset to encompass a wider range of anime styles and character archetypes. Bridging these gaps could propel the utility of CoNR in professional and amateur settings alike, enhancing the versatility and efficiency of animation workflows.

In conclusion, this paper not only proposes a novel approach to a critical bottleneck in anime production but also opens avenues for continued innovation in neural rendering and digital artistry.