The popularity of today's social media and virtual social platforms, as well as the rapid development of augmented reality and virtual reality technologies, have made personalized 3D avatar models an important way for users to express themselves and engage in virtual social interactions, such as Microsoft's release of animated models that can be created based on user photos. The background of the development of virtual human online generation technology can be traced back to advances in the fields of computer graphics, artificial intelligence and virtual reality: Computer graphics: Computer graphics is a discipline that studies how computers are used to generate, process and display images. With the rapid development of computer hardware and software, graphics technology has made great strides in the fields of gaming, animation, film and television production. Among them, the modeling and rendering of virtual characters is an important research direction in the field of graphics. Artificial Intelligence: the rapid development of artificial intelligence technology provides strong support for online generation of virtual characters. AI techniques such as deep learning and generative adversarial networks (GANs) enable computers to learn from large amounts of data and generate realistic avatars. By analyzing and modeling data from real-world characters, these techniques can generate avatars with realistic appearance and behavioral characteristics. Virtual Reality: Virtual reality technology is a computer-generated simulated environment that allows users to interact with the virtual world in real time. Virtual human online generation technology plays an important role in virtual reality applications by generating realistic avatars that enhance the user's immersion and interactive experience in the virtual environment. This technology has a wide range of applications in gaming, virtual socialization, virtual training and healthcare. Personalization demand: With the popularity of social media and virtual social platforms, there is a growing demand for personalized expression and participation in virtual social interactions. Virtual human online generation technology can meet the user's demand for customized and personalized avatars, enabling users to realistically display their own image and characteristics in a virtual environment. The background of avatar online generation technology stems from advances in the fields of computer graphics, artificial intelligence and virtual reality, as well as the demand for personalized avatars. By combining advanced graphics and artificial intelligence technologies, online avatar generation technology is able to generate realistic and personalized avatars that provide users with a richer and more immersive virtual experience.

1.2 Related Work

Understand the significance of SMPL for human body reconstruction, get the parametric understanding based on SMPL, so as to get the skinned effect of the human body, in order to model better adaptability and 3D reconstruction based on photos, I need to pre-process the dataset first, apply the photo rendering tool stable diffusion to render the photos, and the tools in the motion wonder3D, Cross- domain diffusion model and Geometry-aware fusion to realize the 3D animation reconstruction of the character, and then based on the skeleton driving principle in easymocap to realize the driving of mesh.

1.3 Objectives

Search/generation of a parametric avatar mesh model based on/ extended from SMPL.

With capabilities of body shape / face feature reparameterization based on video capture.

3D avatar generation with geometrical feature alignment and restyling based on the aforementioned parametric avatar mesh model; Using only a short video clip to map texture over the avatar mesh.

1.4 Purpose

The purpose of the project is to provide a comprehensive solution for creating and manipulating virtual avatars with realistic appearance and animations. By incorporating advanced technologies such as parametric modeling, video-based reparameterization, texture mapping, and real-time visualization, the project aims to deliver a seamless and immersive user experience in the realm of virtual avatars, with potential applications in gaming, virtual communication, and other virtual reality contexts.

2. Method

2.1 Problem Statement

Our first objective is Search/generation of a parametric avatar mesh model based on/ extended from SMPL. With capabilities of body shape / face feature reparameterization based on video capture. First of all, it is necessary to extract the key frames of the photos from the video, and the characters in the extracted key frames are the real-life photos, but the final realized result needs to be when the animation effect, it is necessary to realize this effect efficiently, due to the lack of arithmetic power because of equipment reasons, the dataset tends to be very small, it is necessary to reproduce the three-dimensional characters based on the photos of a single one, it is necessary to generate a model of the photos of the multiple perspectives, and it is also necessary to realize a realistic and high-quality model based on the generative model.

2.2 Proposed Methods

Pipeline

2.2.1 Data Preparation

The dataset was collected from the web, and since I relied more on diffusion models and 3D fusion models in the method I proposed for reproducing 3D animation meshes in order to improve the generalizability, I chose the full-body red carpet photos of three European and American celebrities for this dataset, and in the preprocessing aspect, the backgrounds were removed, and only the characters themselves were retained, and the quality of the image was improved.

2.2.2 Stable Diffusion

Rendering the database, after rendering the images of the character frames, before training the model. Diffusion model is a new generate model, comparing to the popular generation model GAN. GAN consists of a generator, which is responsible for generating realistic data to "fool" the discriminator, and a discriminator, which is responsible for determining whether a sample is real or "made up". "The training of GAN is actually the two models learning from each other. A generative model is essentially a set of probability distributions. As shown in the figure below, the left side is a training dataset in which all the data are random samples taken independently and identically distributed from some data. On the right is its generative model (probability distribution), in which a distribution Pe is found such that it is closest to Pdata. Then pick new samples on, you can get a steady stream of new data. Diffusion model is From a probability distribution point of view, consider the two-dimensional joint probability distribution P(X, v) in the shape of the Swiss volume in the figure below. The diffusion process q is very intuitive, where the sample points, which are originally centrally ordered, are perturbed by the noise, diffuse outward, and ultimately turn into a completely disordered noise distribution. The diffusion model is actually the inverse of this process P, a noise distribution N (0,1) gradually denoised to map to Pdata, with such a mapping, we sample from the noise distribution, and ultimately we can get a desired image, that is, you can do the generation. And from a single image sample to look at this process, the diffusion process q is to keep adding noise to the image until the image becomes a pure noise, and the inverse diffusion process P is the process of generating an image from pure noise. Comparing to GAN, in general, GAN generates a model that is a black box that is hard to debug, whereas diffusion is very scalable, and on top of that, diffusion is more efficient than GAN.

2.2.3 Cross-domain Diffusion Model

Based on introduction of diffusion model above, we know that diffusion model is a good generation model. Unlike the previous diffusion model which only outputs color image, Wonder3D can output both normal map and color image, and to achieve this goal, the authors propose a cross-domain switcher to control whether the diffusion model produces color image or normal map. To achieve this goal, the authors propose a cross-domain switcher to control whether the diffusion model produces a color image or a normal map, and in order to maintain a good consistency constraint between the normal map and the color image under the same viewpoint, the authors utilize a cross domain attentions mechanism. First, a domain switching layer is used to switch out two different layers of domains to generate a normal and a colorful, both of which are photographs of six viewpoints, and in order to ensure the consistency of these two domains, a cross domain attentions layer is invoked, where the keys and values of the normal image domain and the color image domain are combined and processed by attentional operations. This design ensures that the generation of color images and normal maps are closely related, thus promoting geometric consistency between the two domains.

2.2.4 Geometry-aware Fusion

The use of existing sdf-based reconstruction methods, such as NeuS, proved to be infeasible. These methods are tailored for real captured images and require dense input views. In contrast, we generate views they are relatively sparse, and the resulting normal maps and color images may exhibit subtle inaccuracies in the prediction of certain pixels. Wonder3D proposes geometric fusion algorithms, the principle behind which uses normals, a configuration that ensures that the angle between the normal vector and the observation ray remains no less than 90◦. Deviation from these criteria will mean that the generated normals are inaccurate. In this way, constraints are applied to the generated model. Evaluation results: Wonder3D outperforms previous work in terms of reconstruction results, robust generalization capabilities, and significant efficiency.

2.2.5 Skinned Multi-Person Linear Model

SMPL refers to the parametric 3D model of the human body constructed in the article "SMPL: Skinned Multiple Person Linear Model" in 2015. The human body can be understood as the sum of the base model and the deformations performed on top of the model to obtain the shape parameters by PCA, which are the low-dimensional parameters characterizing the shape. On the basis of the deformation, the shape parameters are obtained through PCA, which are the low-dimensional parameters characterizing the shape; at the same time, the pose of the human body is represented by the kinematic tree, i.e., the rotational relationship between each joint and the parent node in the kinematic tree, which can be represented by a 3D vector, and ultimately, the local rotation vectors of each joint constitute the pose of the smpl model. Parameter (pose).

2.2.6 Skeleton Drive Grid

Because of the large variability of different human body shapes, we still need to estimate the bone points that conform to the mesh after the two hybrid modeling methods mentioned before, so that we can rotate these bone points to form our final desired pose. Therefore, bone point position estimation here refers to estimating the ideal position of the bone points as control points for the silent pose based on the position of the endpoints of the mesh in the silent pose after hybrid molding. The whole process is solved by Eq.1

The whole process can be visualized as shown in Fig 6, where the position of each skeletal point is determined by weighting the endpoints of a number of meshes closest to itself.

Easymocap is an open source platform based on smpl, in which openpose is used to predict skeletal keypoints to enable skeleton-driven mesh, according to Image 7, (a) The method takes the whole image as input to the CNN to jointly predict (b) a confidence map for body part detection and (c) a PAF for part association. (d) The parsing step performs a set of bipartite matches on the associated of body part candidates by performing a set of two-part matches. (e) They are finally assembled into a complete pose for everyone in the image. Skeleton Drive Grid.

Results

3.1 System Settings

Three full-body photographs of people are used as the input dataset. These photos can contain different people and need to cover both full body and facial features. Render using the Stabilized Diffusion Model, a powerful text-to-image generation model that produces high-quality images based on text descriptions. My rendering description is "Render as an animation, preserving the character's body and facial features". This description will direct Stabilized Diffusion to generate an animated image, but still retain the original character features. To further improve the rendering, use ControlNet, a model that can be integrated with Stabilized Diffusion to guide and constrain the Stabilized Diffusion generation process using information such as edges, depth, segmentation, and more from the input image. ControlNet will be used to extract edge, segmentation, and other information from the input three person photos and pass this information as additional input to the stabilization diffusion model. This will help the Stabilized Diffusion model better preserve the body and facial features of the original characters when creating animated images. First, three photos of the character are fed into the ControlNet model to extract edges, segmentation and other information. Then, these ControlNet outputs are fed into the Stabilized Diffusion Model along with the text description "Rendered for animation, preserving the character's body and facial features." The Stabilized Diffusion Model will use the information provided by ControlNet to produce an image that retains the original character features but in an animated style. The final result is three rendered images.

3D Mesh Reproduction

3D mesh reproduction, using a single image as input, outputs multiple corresponding normal maps and color images through a cross-domain diffusion model, and then uses geometric reconstruction methods to recover texture and geometry from the normal maps and color images. The technique route show as Figure11.

Running the above rendered dataset into Cross-domain diffusion model will generate 6-angle photos by generative model and categorized into two types of viewpoint photos: normal and color.

3.2 Presentation and Interpretation of Results

The model employs a geometric information-based normal fusion algorithm to extract high-quality 3D surfaces from 2D representations of multiple viewpoints. This normal fusion algorithm is an iterative optimization process, and the number of iterations can be selected. Specifically, the algorithm first extracts normal maps from the input multi-view 2D images, and then fuses these normal maps to a uniform 3D surface through an iterative optimization process. In each iteration, the algorithm updates the normals and vertex positions of the 3D surface based on the current 3D surface and the input normal maps to minimize the differences between the normal maps and the 3D surface. The number of iterations is a hyper-parameter that can be set, usually between 10 and 50 attempts are made. The higher the number of iterations, the higher the quality of the fused 3D surface, but with a corresponding increase in computation time. Through this iterative optimization process based on geometric information, Wonder3D is able to generate high-fidelity 3D textured mesh models from a single input image, and the number of iterations can be adjusted according to the demand to balance quality and efficiency. The iterative process is shown in Figure 13.

Finally, a more efficient reproduction is obtained by driving the mesh model through the skeleton.

4. Discussion

4.1 Reproducibility of Methods, Code, Data, and Results

The purpose of this report is to provide an overview of the reproducibility of methods, code, data, and results for modeling and animating the appearance of virtualized bodies in an augmented reality (AR) environment. The focus of the report is on the environment in which the models are run, which includes various dependencies such as Torch, torchvision, diffusers, xformers, transformers, and so on. Among them the image rendering model: stable diffusion, the 3D fusion model: geometry-aware fusion, and the Mesh skeleton driven model are trained reproducibly. The report provides a detailed list of code and dependencies required to run the virtualized body appearance modeling and animation models. These dependencies include Torch (1.13.1), torchvision (0.14.1), diffusers[torch] (0.19.3), xformers (0.0.16), transformers (>=4.25.1), bitsandbytes (0.35.4), decord (0.6.0), pytorch-lightning (<2), omegaconf (2.2.3), nerfacc (0.3.3), trimesh (3.9.8), pyhocon (0.3.57), icecream (2.1.0), PyMCubes (0.1.2), accelerate, modelcards, einops, ftfy, piq, matplotlib, opencv-python, imageio, imageio-ffmpeg, scipy, pyransac3d, torch_efficient_distloss, tensorboard, rembg, rembg, rembg, rembg, rembg, rembg, rembg, rembg, rembg. tensorboard, rembg, segment_anything, and gradio (3.50.2). For data processing, a background-free, clear full-body photo of the person is required, and the preprocessing needs to fulfill the png format.

4.2 Difficulties and Limitations

In order to retain better results, I used 10,000 iterations. Although wonder3D implements the algorithm to reproduce with only one photo, the time to run 10,000 iterations is still very long, and there are hard requirements on the machine's arithmetic and storage, so the adaptability of this model is a bit poorer. In addition to that, due to the rendering effect of STABLE Diffusion, the rendered In addition, due to the rendering effect of stable diffusion, the rendered photos are often very different from the real-life photos, and many of wonder3D's strengths lie in the re-realization of real-life type of photos, and for the overly abstract rendered animated photos, the reproduction of the facial details will be a little bit worse.

4.3 Potential Improvement and Future Work

Further improvement of geometry-awareness: Wonder3D has proposed a geometry-aware normal fusion algorithm to extract high-quality 3D surfaces from multi-view 2D representations. In the future, we can continue to explore more accurate geometry-aware methods, such as utilizing Voronoi diagrams to describe local geometric structures, introducing edge domain adaptive stereo matching algorithms, etc., to further improve the geometric accuracy of 3D reconstruction. Enhancing cross-modal consistency: Wonder3D adopts a multi-view cross-domain attention mechanism to facilitate information exchange between different views and modalities (normal map, color map). In the future, it can be further investigated how to enhance the cross-modal consistency to ensure that the generated 3D geometries and textures are more harmonized. Improve computational efficiency: Wonder3D has been able to generate high-quality 3D texture mesh from a single image more efficiently than previous methods. In the future, the algorithm can be optimized to further improve the computational efficiency and make it more practical for real-world applications. Expanding to more complex scenes: Currently, Wonder3D mainly focuses on 3D reconstruction of single objects, but in the future, we can explore how to expand it to more complex scenes, such as indoor scenes, urban scenes, etc., in order to satisfy a wider range of application needs. Combining with multi-sensor fusion: We can consider combining Wonder3D with ADAS multi-sensor fusion technology to improve the accuracy and robustness of 3D reconstruction by utilizing multi-sensor data.

5. Conclusion

So far, for the reconstruction of 3D character models, people have made and realized a lot of progress, from SMPL to easymocap to stable diffusion, and finally wonder3D, this is my step by step realization of animation character reproduction step by step, the algorithms behind each model contain this era for the advancement of the understanding of artificial intelligence, animation model reproduction is not a simple thing, in our current era, the most mainstream animation modeling, is the animation modeler modified little by little, which makes me believe that the real realization of real-time human animation model reproduction is not a simple thing. The reproduction of animation models is not a simple thing, in our current era, the most mainstream animation modeling, is the animation modeler bit by bit to modify out, just rely on the computer to let this process to achieve this is undoubtedly very exciting, based on the results I have achieved, the effect of the reproduction of the human body is insufficient, which there are still a lot of places that can be improved, I hope that through my future learning, I can realize the progress! I also believe that in the near future, human beings will be able to realize real-time camera-based human animation model reproduction.

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