Download MMD Models of Identity V Characters for Free

Hey guys! I recently learned that It is in fact possible to import MMD models to Maya. I would love to animate some stuff for this community(As I am a 3D character animator by profession), and I have a bunch of ideas... But I don't have any models.

However, The ones i'm REALLY looking for though; is the full truth and inference cast. If anyone can find these models for me(and please make sure there are no harsh rules in place by the creator. No NSFW use and respecting companies copyright is fine. But It'll be a chore to abide by too many rules more than that..).

DOWNLOAD ————— https://t.co/8TqMvEbG4Q

Not sure if this is the right place to be asking this but if anyone knows the download key for this mmd model (Maid Lucky Guy) I'd really appreciate knowing it!! I'm new to mmd and dont really know how to work around things like this and I've been wanting to make idv mmd content for a while now, please help if you can!! ;w;

Compared with other characters whose prototypes can be found in reality, the setting of the priest will make people feel a bit magical, for example, the ability to open the shuttle hole with a key. How to design a character that not only fits the tone of the game, but also matches the skill setting? We try to choose a headgear with G*psy ethnic characteristics, brightly colored and flowing long skirts and waist ornaments, exaggerated earrings, and coquettish makeup. Roman-style strappy sandals, paired with colorful decorative jewelry. These simple elements and objects with regional characteristics make the character finally present a concise and light modeling image.

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As we talk with our customers that are using Microsoft Endpoint Manager to deploy, manage, and secure their client devices, we often get questions regarding co-managing devices and hybrid Azure AD-joined devices. Many customers confuse these two topics. Co-management is a management option, while Azure AD is an identity option. For more information, see Understanding hybrid Azure AD and co-management scenarios. This blog post aims to clarify hybrid Azure AD join and co-management, how they work together, but aren't the same thing.

You can't deploy the Configuration Manager client while provisioning a new computer in Windows Autopilot user-driven mode for hybrid Azure AD join. This limitation is due to the identity change of the device during the Azure AD-join process. Deploy the Configuration Manager client after the Autopilot process. See Client installation methods in Configuration Manager for alternative options for installing the client.

However, due to the influence of the annotation environment and cultural background, the annotators of the FER dataset may produce extreme subjective annotation deviation [12]. In addition, several other interference factors, such as sample quality (e.g., occlusion and sample clarity) and racial bias [13], may also produce some outliers in facial expression datasets as shown in Figure 1. Wang et al. [14] declared that outlier samples in facial expression datasets could be harmful to the model for learning useful facial expression features. Xu et al. [15] also revealed the performance degradation of other cross-dataset tasks, through extensive experiments, caused by learning incorrect outlier samples. Similarly, in dealing with cross-dataset FER, these outlier samples can also inevitably break the modeling of the relationship between the source domain and its corresponding label information. Therefore, the unsupervised domain adaptation (UDA) models could fail to learn the discriminative facial expression features. In this case, it is hard for the UDA models to cope with the cross-database FER tasks, although they successfully eliminate the feature distribution difference between the source and target domains with well-designed strategies. Therefore, it is crucial to consider the outlier samples in dealing with cross-dataset FER tasks.

To deal with this problem, we proposed an effective network termed as sample self-revised network (SSRN) in our conference work of [15]. SSRN is committed to learning a feature extractor that is more robust to both source and target domains. By reducing the influence of outlier samples in the source domain on model training and aligning the target domain with the revised source domain at the feature level, SSRN can obtain the domain-invariant features for both source and target domains. Precisely, the proposed SSRN consists of three essential modules: outlier perception module, outlier perception coefficient (OPC) revision module, and feature transfer module. Given a batch of samples from source and target domains, respectively, the CNN backbone can be used to extract facial features. Then, the outlier perception and the OPC revision modules are used to dynamically perceive outlier samples in the source domain and mitigate the influence of these outliers on the model training. After that, the well-designed feature transfer module enforces the outlier samples to share the same feature distribution with other samples in the source domain so as to revise these outlier samples. In addition, features of source and target domains can also be aligned to learn more robust domain invariant features with the feature transfer module.

In this paper, we strengthen our conference work on SSRN [15], and further propose the enhanced sample self-revised network (ESSRN). Specifically, we modify our original SSRN from two aspects. First, the class imbalance of the FER dataset can sharply degrade the domain adaptation performance [16]. Inspired by [17], we add conditional maximum mean discrepancy (MMD) [18] while dealing with outliers revision and domain adaptation in the feature transfer module. In addition to aligning the marginal distribution considered in previous works, we further align the class-conditional distribution by narrowing the feature distance of each category between the source and the target domains. Second, we added a feature reconstruction layer in the revision process. The feature reconstruction layer performs linear reconstruction of outlier samples at the feature level so that the revised samples can mitigate the inconsistency of modeling the relationship between the source domain and their label information. In summary, besides the original contributions in our preliminary work [15], in this paper, we further improve the outliers revision function and the unsupervised domain adaptation process to learn better domain-invariant features to describe facial expressions. Therefore, we achieve more promising performance in dealing with cross-dataset FER.

where pij and qij represent the ground truth and the prediction of the j-th class of the i-th sample, respectively, ej is a one-hot vector, whose j-th element is one and the others are equal to zero. Wcls represents parameters of the classifier. By employing the weighted cross-entropy loss function, the model can reduce the interference of outlier samples to the model training and pay more attention to the samples with low outlier levels.

where FHs and FLs represent the feature vectors of the high-coefficient and low-coefficient groups in the source domain. The loss function of the feature transfer module is composed of two parts. The first half of (12) reduces the feature distribution distance of high-coefficient group and low-coefficient group samples in the source domain, which revises outlier samples detected by the outlier perception module. The second half of (12) further ensures that the target domain features can be aligned with the revised source domain features so that the model can learn more robust domain invariant features.

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