The convergence of virtual reality live streaming and AI-driven avatars has emerged as a significant technological trend. However, current integration attempts remain in the proof-of-concept stage, with the primary challenge of automatic interaction system establishment. To build interactive intelligence anime avatars within VR frameworks, we have developed a multimodal interaction architecture centered on dialogue agents, realizing comprehensive understanding, reasoning, and response. Our approach 1).proposes high granularity explicit-implicit understanding and a dual-center switchable reasoning mechanism to support flexible responses. 2).innovates a dual-source animation mechanism for co-speech face-body visualization and a textual command module for supervising crossmodal animation, and 3).enhances expressiveness through mapping persona, content, voice, and motion to anime style. Experimental results demonstrate the state-of-the-art performance of VRtalk, highlighting its practical significance and future potential.

Abstract This paper presents a novel fully automated method for generating view‐independent abstract wire art from 3D models. The main challenge in creating line art is to strike a balance among abstraction, structural clarity, 3D perception, and consistent aesthetics from different viewpoints. Many existing approaches have been proposed, including extracting wire art from mesh, reconstructing it from pictures, etc. But they all suffer from the fact that the wires are usually very unorganized and cumbersome and usually can only guarantee the observation effect of specific viewpoints. To overcome these problems, we propose a paradigm shift: instead of predicting the line segments directly, we consider the generation of wire art as an optimization‐driven manifold‐fitting problem. Thus we can abstract/generalize the 3D model while retaining the key properties necessary for appealing line art, including structural topology and connectivity, and maintain the three‐dimensionality of the line art with a multi‐perspective view. Experimental results show that our view‐independent method outperforms previous methods in terms of line simplicity, shape fidelity, and visual consistency.

This article reviews published research in the field of computer-aided colorization technology. We argue that within this context, the colorization task can be considered to originate from computer graphics, advance by introducing computer vision, and progress towards the fusion of vision and graphics. Hence, we propose a specific taxonomy and organize the research work chronologically. We extend the existing reconstruction-based colorization evaluation techniques on the basis that aesthetic assessment should be introduced to ensure the computer-coloredimages closely satisfy human visual-related requirements. We then perform an aesthetic assessment using the proposed metric and existing evaluations, comparing the colorization performance of seven representative unconditional colorization models. Finally, we identify unresolved issues and propose fruitful areas for future research and development.

Being essential in animation creation, colorizing anime line drawings is usually a tedious and time-consuming manual task. Reference-based line drawing colorization provides an intuitive way to automatically colorize target line drawings using reference images. The prevailing approaches are based on generative adversarial networks (GANs), yet these methods still cannot generate high-quality results comparable to manually-colored ones. In this article, a new AnimeDiffusion approach is proposed via hybrid diffusions for the automatic colorization of anime face line drawings. This is the first attempt to utilize the diffusion model for reference-based colorization, which demands a high level of control over the image synthesis process. To do so, a hybrid end-to-end training strategy is designed, including phase 1 for training diffusion model with classifier-free guidance and phase 2 for efficiently updating color tone with a target reference colored image. The model learns denoising and structure-capturing ability in phase 1, and in phase 2, the model learns more accurate color information. Utilizing our hybrid training strategy, the network convergence speed is accelerated, and the colorization performance is improved. Our AnimeDiffusion generates colorization results with semantic correspondence and color consistency. In addition, the model has a certain generalization performance for line drawings of different line styles. To train and evaluate colorization methods, an anime face line drawing colorization benchmark dataset, containing 31,696 training data and 579 testing data, is introduced and shared. Extensive experiments and user studies have demonstrated that our proposed AnimeDiffusion outperforms state-of-the-art GAN-based methods and another diffusion-based model, both quantitatively and qualitatively.

Abstract Significant advancements have been made in colorization in recent years, especially with the introduction of deep learning technology. However, challenges remain in accurately colorizing images under certain lighting conditions, such as shadow. Shadows often cause distortions and inaccuracies in object recognition and visual data interpretation, impacting the reliability and effectiveness of colorization techniques. These problems often lead to unsaturated colors in shadowed images and incorrect colorization of shadows as objects. Our research proposes the first shadow-aware image colorization method, addressing two key challenges that previous studies have overlooked: integrating shadow information with general semantic understanding and preserving saturated colors while accurately colorizing shadow areas. To tackle these challenges, we develop a dual-branch shadow-aware colorization network. Additionally, we introduce our shadow-aware block, an innovative mechanism that seamlessly integrates shadow-specific information into the colorization process, distinguishing between shadow and non-shadow areas. This research significantly improves the accuracy and realism of image colorization, particularly in shadow scenarios, thereby enhancing the practical application of colorization in real-world scenarios.

Automatic colorization of anime line drawing has attracted much attention in recent years since it can substantially benefit the animation industry. User-hint based methods are the mainstream approach for line drawing colorization, while reference-based methods offer a more intuitive approach. Nevertheless, although reference-based methods can improve feature aggregation of the reference image and the line drawing, the colorization results are not compelling in terms of color consistency or semantic correspondence. In this paper, we introduce an attention-based model for anime line drawing colorization, in which a channel-wise and spatial-wise Convolutional Attention module is used to improve the ability of the encoder for feature extraction and key area perception, and a Stop-Gradient Attention module with cross-attention and self-attention is used to tackle the cross-domain long-range dependency problem. Extensive experiments show that our method outperforms other SOTA methods, with more accurate line structure and semantic color information.