Introduction: This article will introduce related research work on key technologies of digital human modeling and animation from a graphical perspective, such as face modeling and hair editing. , virtual clothing, etc., mainly including the following parts:
Published an oral report paper on fat and thin adjustment of video portraits at ACM Multimedia2021. It mainly adjusts the fat and thin faces of people in videos so that it is not visually obvious. The natural effect of trimming.
Double Chin Removal is a paper published in Siggraph 2021. Removing a double chin is difficult in face editing because it involves both texture and geometry. If shown, the first row is the original image, and the double chin can be gradually disappeared by adjusting the parameters (second row).
Removing hair from portraits is Remove the hair of the person in the given photo. You can edit hair, such as changing a set of hair for a character. If you keep the original hair, it will interfere with the synthesis result. In the three-dimensional reconstruction of digital people, if the original hair is retained, it will cause interference to the texture. Our method can obtain 3D reconstruction results without hair texture interference.
This is a new item in the metaverse Fashion, provide a photo, synthesize virtual clothes onto a person's body, and you can wear new clothes as you like.
In sustainable development, there are a lot of problems in the fashion industry. Virtual clothing offers a great solution.
For example, the left side is real clothes and the right side is virtual clothes. It can be seen that virtual clothes and real clothes are very similar.
The clothing model and animation of the digital human Xi Jiajia in the 2022 Baidu World Conference are provided by us.
#The picture above is of the digital person in the movie, and the work of virtual plastic surgery.
#What we want to study is how to construct a high-precision three-dimensional face reconstruction method. One method is to collect user photos and use MVS to reconstruct the three-dimensional model, but this method has poor effect on eyelash processing. Because there is geometric information in the eyelashes, it will cause interference to the reconstruction and make the eye area inaccurate.
Related There is a lot of research work, such as facial geometry and hair reconstruction, eyelid and eyeball reconstruction, etc., but there is no feasible method to accurately edit eyelashes.
① Cutout method based on three-part graph
To edit the eyelashes, you can use cutout to cut out the eyelashes. Cutout is actually solving an ill-conditioned equation, as shown in the figure below. This is an example of natural cutout based on a three-part diagram. , can get good results. However, this method has a disadvantage. It requires the input of a three-part graph, and it is very difficult to construct a three-part graph.
② Cutout data set
There has been a lot of work on cutout data sets in recent years. For example, a data set from CVPR2009 is shown below.
③ Blue screen cutout
Blue screen cutout is used a lot in movie special effects , usually using a green screen or blue screen, and then calculating the value of the foreground mask through some triangulation methods.
① Introduction to data set baseline method
What we want to solve is the cutout of eyelashes. The input on the left is a photo containing eyelashes, and the value of the mask is calculated through the matting network EyelashNet.
② Research motivation
There is geometric texture in the eyelash area. When the three-dimensional reconstruction is parameterized, It will cause great interference to the result and the effect will be very bad. If it is done by an artist, it will be very time-consuming and labor-intensive. Therefore, a method is needed to automatically pull out the eyelashes.
③Main Challenge
If the eyelashes are removed manually, it is very time-consuming and labor-intensive. Using Gabor filtering method, the effect is still not good. Image matting methods can also be used, but data set construction is very difficult. If you use blue screen cutout, the eyelashes grow on the eyelids, so that the background image such as eyelids and eyelids cannot be separated and replaced. In addition, people will blink, making it difficult to keep still when collecting eyelashes. Then you want to collect multiple strictly aligned And it is very difficult to apply eyelashes of different colors.
④ Eyelash data collection
We paint the eyelashes Apply fluorescent agent and turn on the UVA flash, you can see the fluorescent effect, and then get the segmentation results of eyelashes. But this is not enough and further processing is needed.
⑤ Eyelash mask calculation
We use the data set obtained in the previous step as input, using The matting network is used to predict the real matting results. But if we only use the original data set, the effect is not very good, and we do not have ground truth. We designed the virtual synthesis method Render EyelashNet to preheat, and then used the experimental results to predict an estimated result. Combined with manual work, we filtered out those bad results and finally obtained a data set with an initial mask. Then you can use this data set to train and get a refined result. The refined result is put into the data set and then trained. After iteration, a better data set is finally obtained.
① Collection equipment
We built a collection system, including 16 cameras, 365 nm UV flash, fill light system, etc. Please see the screenshots for specific parameters.
We invited many students from Zhejiang University to paint the eyelashes with fluorescent agent. The person had to remain still, and then used a laser to position the eyes.
Comparison of the results of turning on and off the UV flash:
Ideally, there is no offset between the two input comparison images, but human eyelids can easily move and there will be deviations. , we use FlowNet2 to obtain an optical flow field, use the optical flow field results to offset the fluorescent eyelashes, and then obtain a strictly aligned picture, thus obtaining the segmentation result.
① GCA network
The publication we mainly use in the inference stage GCA Network at AAAI 2020.
The input of the GCA network is an RGB image and a three-point image, and the output is an eyelash mask. Our previous segmentation The result can be used as the initial three-part diagram result, thereby solving the problem of difficulty in manually constructing the three-part diagram of eyelashes.
② Mask inference network
Here are three The image is divided into the eyelash mask image and the original RGB image as input. Through progressive training, combined with RenderEyelashNet, the preheating network is trained to obtain a mask result. This result is then added to the input as a training set and manually filtered. Obtain a visually correct eyelash cutout dataset, so that there is both virtual and real data. Use this data set for training and inference, and finally get the predicted version of the eyelash mask. Then put it into the training set and iterate again, usually you can achieve the desired result in two times.
③ Manual selection
Even the most advanced hardware and software equipment cannot guarantee the accuracy of eyelash collection To ensure the accuracy of the training data, we remove some bad results through manual selection.
④ Baseline network
After training the baseline network , input a picture for testing, and get better results. For an unknown image, we don't know what its three-dimensional image is. If we directly input a grayscale image, we can still get good eyelash prediction results.
We capture eyelash data for 12 eye expressions and 15 views.
In order to verify our Method, during testing, we used not only the data we collected ourselves, but also some image data on the Internet.
After two progressive iterations, the results we obtained are already very good and close to the true value.
③ Comparison of methods
We compared with the best methods at present, regardless of Both visually and quantitatively, our method is significantly better than previous methods.
④ Ablation experiment
We also did ablation Experiments to validate various parts of our approach are indispensable.
⑤ Result display
We used some photos on the Internet for verification. These photos are There is no Ground Truth. But for these photos, our method can still calculate better eyelash cutout results.
⑥ Application
We cooperate with Tencent NEXT Studio to use this method for high precision Three-dimensional face reconstruction, the eyelash area has been highly realistic.
Another application is the beautification editing of eyelashes. Once you have eyelashes, you can change their color or make them longer. However, if this method is used in places where people wear glasses and the light intensity is obvious, the results will be biased.
We proposed EyelashNet, which is the first A high-quality eyelash cutout data set, including 5400 high-quality captured eyelash cutout data and 5272 virtual eyelash cutout data.
# We propose a specially designed fluorescent labeling system to capture high-quality eyelash images and masks.
# Our method achieves state-of-the-art performance on eyelash cutouts.
This work is to simulate loose clothes. We cooperated with the University of Maryland and Tencent NEXT Studio, and related papers were published on Siggraph2022. This work proposes a real-time prediction method for loose clothing based on deep learning, which can handle large-scale movements well and supports variable simulation parameters.
One of the core technologies of this work is virtual skeleton, which is a set of controls using rigid transformation and linear hybrid simulation methods. Simulated skeleton for clothing deformation. Using virtual bones, we can efficiently simulate the complex deformations of loose clothing, and these bones can be used as input to guide the generation of clothing details.
There are generally two ways to make clothes move. Class methods, one is a physical method, which is computationally expensive; the other method is data-driven, which learns and drives from real data. This method is relatively fast and has good performance. .
In recent years, there have been more and more methods of machine learning and deep learning, but these methods are either predictive Clothing deformation under static conditions, or predicting dynamic deformation of tight-fitting clothing. But in fact, many clothes such as skirts are loose. Although some methods can predict the deformation of loose clothes, they are not very good at predicting the deformation of large movements. Moreover, none of the current methods support variable parameters.
Our research mainly has two points Contribution, the first one is to use deep learning method to predict the complex deformation of loose clothes. We disassemble the clothing deformation into two parts-the low-frequency part and the high-frequency part. Use virtual bones to represent the deformation of the low-frequency part and use it to infer the high-frequency part; the second contribution is to use the body's movements combined with physical simulation parameters as input, and use this method to handle the heterogeneity of the two inputs.
① Virtual skeleton generation method
First use the simulation method to obtain a ground truth training set, perform Laplacian smoothing processing on these training sets to obtain low-frequency Mesh, and then perform Skin Decomposition processing to obtain Virtual bones and weights.
② Motion network
Get the motion sequence of the virtual skeleton through the body motion sequence, predict low-frequency deformation information through the motion network, and use low-frequency The information predicts high-frequency information, and finally the simulation results are obtained (the rightmost graph).
③ Simulation parameter variables
We want to evaluate different parameter variables, through The RBF network predicts the results of simulation parameters that we have not seen, so that a set of networks can be used to make predictions even if the parameters are different.
① Data preparation
First of all, we need to generate ground truth data. We use the Houdini Vellum Solver to simulate approximately 40,000 frames of animation. We did not use the motion capture results of real people, but the video actions from the Internet. This is because we want to simulate large movements, but real people's movements are smaller.
② Skin Decomposition
Low-frequency deformation sequence We use skin decomposition to obtain virtual bones, and we get The result is a linear hybrid skinned model that includes a Rest Pose and skin weights corresponding to each bone. The translation and rotation of the virtual skeleton at each frame are also obtained. Virtual bones have no hierarchical relationship, there is no relationship between parent bones and child bones, and each bone has its own rotation and translation.
#In addition, virtual bones have no real realistic meaning. Virtual bones are obtained for each specific animation. We use Motion Network to process the input of the body. Each network corresponds to different body simulation parameters. The input is only the rotation of the joints and the translation of the character, and the output is the Mesh inference result corresponding to the physical parameters.
③ Action network
The action network infers the low-frequency and high-frequency parts in sequence.
The low frequency part uses a recurrent neural network GRU converts input body movements into rotations and translations of virtual bones. The advantage of using a recurrent neural network is that it can obtain information from previous frames, which can better capture dynamic effects. Low-frequency deformations can be obtained using linear hybrid skinning of virtual bones.
The action network can be used to predict the high-frequency part. One is GRU to obtain high-frequency features, and the other is GNN to obtain low-frequency part features. The two parts of features are passed through MLP to obtain high-frequency information. The high frequency and low frequency results are added together to get the final result.
In order to process the physical simulation parameter input, we trained many Motion Networks with different actions. The output of the same action corresponds to the parameter simulation results. We use the RBF neural network to add these results. The weighting coefficient depends on the simulation parameters and Corresponds to the distance of the simulated parameters of the network, and uses a multi-layer perceptron to project the parameters into a space before calculating the distance.
In real-time simulation, without changing the simulation parameters, Loose clothing can be simulated very well.
##The simulation results on the left are very close to the ground truth, and the right side deals with variable parameters. #Another question is how to select the number of virtual bones. Our experiment found that for the low-frequency part, too small a number has no good effect, and too much does not help much. 80 is a better result. But for the high-frequency part, the more virtual bones the better, so that details can be better expressed. Loose refers to the distance between the clothes and the human body. The red part means farther, and the blue part means tight. section, we can see that our results (far right) are better. This is a comparison chart between low frequency and high frequency cases and the true value. Our method is closer to the ground truth. . Visually different Comparison of methods, although our effect is slightly different from the ground truth, it is relatively better. Regardless of the high frequency or low frequency parts, they are relatively close. We also conducted quantitative analysis, such as RMSE, STED and other indicators, and the results showed that it was significantly better than previous methods, even for tight-fitting clothes and traditional methods. We conducted an ablation experiment through the RBF network to verify our method. In a very big move In some cases, the legs in the simulation results may pass through the clothes. This is because collision avoidance is added through the energy network. Other skinning methods can also be used in the future to obtain better results. #A1: The virtual bones are calculated. Changing a set of clothes requires regenerating new bones, and the number and transformation are also different. It is calculated in real time during inference. #A2: It is still very convenient. People who have never learned it before can learn it quickly after training. Even if you design an outfit from scratch, you may be able to design a very complicated outfit in one or two hours. 
4. Question and Answer Session
#Q1: How to ensure the stability of virtual bones Generalizability?
Q2: Is it easy to make three-dimensional clothing?
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