Google has added a 5000 character limit to Google Translate, which is very surprising. The popular service now lets users translate texts of only 5000 characters in one go, and the rest in multiple tries.
A small counter is now visible at the bottom right corner of the text box, which now counts and reveals the number of characters as you type them. It shows that the maximum number of characters now allowed in the service is just 5000. The box of translation limit in Google search has also been set as 2,800 characters. It remains unclear why Google has set the new limits.
Google translate has recently completed 10 years of its release. Last month the internet giant had announced that the service will be getting Neural Machine technique (ML/AI) which will make it even more powerful. The service currently supports eight languages including German, Spanish, English, French, Portuguese, Chinese, Japanese, Korean, and Turkish.
How To Fix Text exceeds 3900 character limit?
The new limits will make it very tedious for people who wish to translate longer documents via Google translate. The above limitation has not been set for web pages that will have a bigger number of characters. However, Google Translate’s API has the same limits of 3900 characters.
Google may have its own reasons for limiting text translations, but the whole process of translating documents has become very difficult for users.
How to get past 5000 Character Limit with Chrome Extension
Step #1 – Add Chrome Extension
Step #2 – On Chrome Extension and Setup:
Step #3 – Open your text with Chrome Explorer and use Google Translate Extension:
You can translate web pages or local files without 5000 Limit.
You can open:
Large text translation
Google has developed a large text translation technology for the million+ words in their database.
The Google Translate team is constantly improving the quality of its translations by using machine learning. The system is built on an artificial neural network that takes into account the whole sentence to find the best translation, instead of just considering isolated words.
We see a great interest in the machine learning topic in our article about DeepNude alternatives. Many of our readers have gained new experience in using these apps. And we decided to collect the available information to compare the two most famous ML solutions.
The comparison method is based on the sum of machine learning service parameters and examples. Evaluate our research and save your time. Enjoy!
Comparison Table of DeepNude Online Alternatives
|Speed||20 sec||approx. 15 min|
|Online Photo Crop||Yes||Yes|
|Max Image Resolution||512×512||512×512|
|Cost||$10 per Week||$4 per Day|
|Payment Currency||Bitcoin||PayPal, CreditCard|
DeepNudeTo vs Deepnude Online: Processing examples [18+]
Deepnude Online Render
Deepnude Online Render
Deepnude Online Render
🔥 DeepNudeTo Render
Deepnude Online Render
Deepnude Online Result
🔥 Telegram bot
p.s.: Open source images are used for comparison of this software.
Make your own Test Now!
These guys, whoever they are, are constantly working on their services and improve their ego. Keep an eye on updates so you don’t miss out on new features.
If you like this post – help us:
Introduction: What is a DeepNude app?
The DeepNide app is an AI-powered content generation tool that generates deep personalization for user engagement. It creates personalized pics.
People nowadays are increasingly looking for personalized solutions to their problems. This way they feel like they are not alone and increase their commitment to achieving their goals.
What are the Best Features of DeepNudes Apps?
DeepNudes apps are a suite of different writing tools that were developed by a copywriting agency. They have been developed with the needs of modern-day copywriters in mind and offer an easy way to write content for any niche.
Some of the best features of these apps are:
- The intelligent word suggestion tool is one of the most innovative features of this app. It suggests words based on what you have typed so far and can predict what you will type next
- It has an integrated dictionary with over 100,000 words which makes it easy for writers to find synonyms or find a word that they want
- This app also has a plagiarism checker that helps writers keep their copy original and avoid being flagged as plagiarists
TOP 10 Deep nude Examples
- DeepNudeTo – This Tool has 10 free uncensored photos with watermarks. Paid Plans start from $10 in Bitcoins.
- Deepsukebe.io – is the best app in 2020 but has boring captcha.
- Deepnude Online website app (aka Nudify) – Undress any photo using the power of AI algorithms (no download software), try it out for free! Without Watermarks.
- SukebeZone+ – Multiple photos and galery but have only Paid plans for $12.99 per 50 photos.
- Deepnude.info new project. Doesn’t work Now.
- DnGG – HQ but without Paid Plan you get only Blurred Photo. Doesn’t work Now.
- DeepNude Telegram Bots – has an easy user interface for quick work! Check it All!
- Deepnude.com – Official Website. Doesn’t work Now.
- FakeNudes.com – They manually create Fake Nudes of Girls You Know for $45 per photo!
- Deepnudenow.com – quick website but has too many ads.
Check Comparison Table here.
Conclusion: Should I Use a DeepNude App To Generate Fakes or Create My Own Fakes?
I am sure you are very excited about the next part of this article, where I am going to share my thoughts on two of the most popularly asked questions of all time. Should I use a deepnude app to generate fakes or create my own fake?
Before we get into these two questions, let’s talk about what is a deep nude. DeepNude is an application that uses facial recognition that allows you to take any picture on your phone and turn it into a nude photo. This can be done without your consent, which is why people are asking whether they should use it to generate fakes or create their own fake.
Python, as one of the more easy-to-learn and -use languages, is ideal for beginners and experienced coders alike. The language comes with an extensive library that supports common commands and tasks. Its interactive qualities allow programmers to test code as they go, reducing the amount of time wasted on creating and testing long sections of code.
GoLang is a top-tier programming language. What makes Go really shine is its efficiency; it is capable of executing several processes concurrently. Though it uses a similar syntax to C, Go is a standout language that provides top-notch memory safety and management features. Additionally, the language’s structural typing capabilities allow for a great deal of functionality and dynamism.
Low/ No-code platforms: A lot of elements can be simply dragged and dropped from the library. They can be used by different people who need AI in their work but don’t want to dive deep into programming and computer science. In practice, the border between no-code and low-code platforms is pretty thin. Both still, usually leave some space for customization.
R is a strong contender, just missed this poll by a slight margin.
If you’ve succumbed to the hype around machine learning, you’ve likely heard hundreds of ML evangelists claim that data-driven decision-making is inevitable for companies that want to thrive in the near future. And a number of questions will arise as you consider how to employ the technology in your business. Can it significantly aid in reducing costs or increasing revenue? How can you estimate return on investment? Can you leverage the existing data to yield game-changing insights? Should you even try to get on that train right now?
What’s so special about machine learning
The concept of machine learning was conceived about 50 years ago with the idea of making computers learn as humans do. As the field evolved, it gave us a means to find useful patterns in large amounts of data.
The way to address this is to apply an algorithm which would differ from the diligent but narrow “if-then” programs that we’re used to dealing with. Machine learning isn’t limited to narrow-task execution. An engineer doesn’t have to compose a set of rules for the program to follow. Instead, a machine can devise its own model of finding the patterns after being “fed” a set of training examples. Dealing with a “black box” of that sort–where a human is only concerned about inputs and outputs–brings an almost unlimited variety of application opportunities, from recognizing cats in pictures to tracking body functions that yield individual treatment programs.
The reason machine learning is only now topping the list of tech buzzwords is that just recently we’ve achieved computational power enough to process big data: huge and unstructured data sets with possibly thousands of variables instead of small and well-filtered ones. Much talked-about AlphaGo, which has recently beaten a human grandmaster at the ancient game of Go, is just one of the examples.
Defining how machine learning is going to be the gamechanger for your business isn’t as trivial a task of simply putting the data into the black box and waiting for a magical insights sheet to roll into your printer tray. While you can utilize the approach to get insights about one or a handful of operations in a company, tangible changes happen only if the adoption is backed by a strategy. The strategy should be introduced and guided at the C-suite level, and a number talent acquisitions should be made to support this strategy adoption.
Step 1. Articulate the problem
There are generally two types of companies that engage in machine learning: those that build applications with a trained ML model inside as their core business proposition and those that apply ML to enhance existing business workflows. In the latter case, articulating the problem will be the initial challenge. Reducing the cost or increasing revenue should be narrowed down to the point when it becomes solvable by acquiring the right data.
For instance, if you want to reduce the churn rate, data might help you detect users with a high “fly risk” by analyzing their activities on a website, a SaaS application, or even social media. Although you can rely on conventional metrics and make assumptions, the algorithm may unravel hidden dependencies between the data in users’ profiles and the likelihood to leave.
Here’s another example. While it’s relatively easy to estimate performance scores in a sphere of production, can you understand, for instance, how salespeople perform? Technically, they send emails, set calls, and participate in conferences, all of which somehow result in revenue or the lack thereof. People.ai is a startup that tries to address the problem by making a machine-learning algorithm to track all the sales data, including emails, calls, and conferences, to come up with the most productive sales scenarios.
The bottom line here is to define the problem where standard business logic and the set of rules aren’t sufficient to solve it. Use machine learning when decisions heavily rely on the subjective opinion of an analyst or a decision-maker.
Applied predictive analytics is a broader variety of techniques that anticipate outcomes by leveraging data. While machine learning is one approach to realize predictive analysis, the current landscape of areas where it acts as a strategical reinforcement to business processes is quite broad, from content recommendations to healthcare.
Step 2. Consider the prescription
The most advanced issue of developing predictive analytics strategy is whether you can find the right prescription based on the received knowledge. In other words, what are you going to do with the insights you obtain? Can you automate the decision-making in this case? McKinsey disclosed story of an international bank that was concerned about the number of defaults that some of their clients experienced. By means of machine learning, they managed to detect a group of customers that had suddenly switched from spending money during the day to use their bank cards in the middle of the night. This behavioral pattern closely correlated with the default risk as the bank later discovered that the people from the group were coping with a recent stressful experience. The prescription was to offer financial advice to the people from the risk group and establish new credit limits for these customers. In some cases, coming up with such prescriptions would be much harder or it would involve a course of actions that can’t be automated at all.
Moreover, insights that you will get may inspire the prescription measures that you could never think before unraveling hidden dependencies in your data.
Step 3. Ensure that the quality of your data is good enough
Data science is a broad field of practices aimed at extracting valuable insights from data in any form. And we believe that using data science in decision-making is a better way to avoid bias. However, that may be trickier than you think. Even Google has recently fallen into a trap of showing more prestigious jobs to men in their ads than to women. Obviously, it’s not that Google data scientists are sexist, but rather the data that the algorithm uses are biased because it was collected from our interactions with the web.
Qualify your data and decide the minimum prediction accuracy
Basically, the quality of the data you have or can collect will define whether it may be used to build the algorithm. Data can be noisy; some information can be conflicting, biased, or just missing. To qualify your data set for further model development, you’ll need to involve a technical consultant or a data scientist in the early stages. This will allow for data testing and setting the minimum acceptable prediction accuracy. Here’s something to note: Although business decision-makers look for concrete recommendations, data science can only provide relative figures. So, deciding the minimum degree of confidence acceptable for solving a business problem will be on top of the importance list.
In one of our projects involving fare prediction analysis in booking air tickets, we were challenged to design an algorithm which would forecast flight fares, both short and long term forecasts. Seventy-five percent of prediction accuracy was high enough to support customers with booking recommendations.
Be ready to break down silos, anonymize, and share data
One of the hurdles that our data science team regularly faces is access to data at the stage of project negotiation. While understanding the initial costs are critical for any business that decided to embark on predictive analytics, it’s nearly impossible to estimate the accuracy level and price without seeing actual data. That’s the point when negotiations can be paralyzed by the catch-22 problem. Business executives can’t give away the sensitive customer or business-related information to a technical consultant, while a consultant can’t give definitive answers before seeing data.
We usually offer to provide a subset instead of the whole database and anonymize it beforehand. Even for the companies having a data scientist on board, it’s a common management challenge to share data among different departments. An overregulated information policy or just hoarding of data across departments can really slow down the process. That’s why data science adoption should be introduced and guided on the higher management level.
Good news: Data can be fixed
Even if your data set is messy and unstructured, it’s not necessarily a death sentence for your data science initiative. Today, data scientists are well equipped with a number of practices to apply during the preparation stage to restructure, clean your data set, and further optimize it for efficient modeling.
Source: O’Reilly, The Evolution of Analytics
The bad news here is that a data scientist may require quite a while to complete data cleansing and proceed to the modeling stage. Should you try handling it yourself in advance without having proper expertise? The general answer is no. It’s very likely that your data set will need refactoring anyway.
Step 4. Prepare to bridge the gap between technical and business vision
If you ask data scientists about their favorite and most useful algorithms, you’ll likely hear something about boosted decision trees, artificial neural networks, kernel methods, principal component analysis, etc. Thus, you may hire a brilliant data scientist who’s still going to have a hard time translating complex results into concrete business language. On the other hand, a chief marketing officer (CMO) may lack the technical background to convert figures given as probabilities into monetary terms.
According to a recent SAS paper, many organizations have already recognized the need to introduce a chief analytics officer to their corporate frameworks. The person should have both business and tech expertise to lead the data science initiatives, envision the options to scale the machine learning application and reconcile business and technical vision.
Otherwise, your data scientists should be ready to educate decision-makers on the opportunities and limitations that different ML models present.
Step 5. Explore the options to hire the right talent
One of the most popular courses at Stanford is machine learning. And back in 2012, the Harvard Business Review regarded a data scientist to be “the sexiest job of 21st century.” Yet there have been a lot of talk about the shortage of data-science talent over the past few years. McKinsey theorizes that by 2017 the demand for this expertise will be 60 percent higher than supply. No matter whether this prediction is true, the profession is extremely hyped. If you operate from New York City or Silicon Valley, the starting salary for a data scientist will be about $200,000, as stated by Bloomberg.
What makes data scientists so scarce and valuable is the blistering change in the technological landscape that outstrips educational capacities. Moreover, being a data scientist requires a rare skillset combination at the junction of math, statistics, programming, databases, and domain expertise.
So, here is the challenge. What are the options?
Hire a data scientist and be ready to engage
If you aren’t operating in a metro area such as New York City or Silicon Valley, the median salary will be about $104,000, which is nearly double the average salary for a regular programmer. Not only do experienced data scientists have higher price tags, they will demand creative work to stay engaged, which often conflicts with the siloed department structures of many organizations.
To leverage the talent that you already have, you’ll inevitably need a data scientist to take a leadership role. This also can be addressed by building or acquiring a machine learning platform with a friendly interface that would be approachable to a wider range of specialists within your organization. That way, you’ll be able to scale from one or a handful of people to a larger group of experts. Have a look at our data science team structures guide to get a better idea of roles distribution.
Find a vendor team
Outsourcing several operations to external experts became a common practice a long time ago. But unlike generic programming that so many vendors can do, data science and machine learning outsourcing haven’t yet overcome the threshold of trust. The biggest challenge of outsourcing machine learning tasks is to align corporate limitations of sharing data with external expert assistance. Depending on the type of data you have, you may need to anonymize it in a way that it doesn’t reveal sensitive details, like customer contacts, their location, etc. You should also keep in mind that an anonymized data set doesn’t allow an analyst to enrich it by using external sources or applying his/her own understanding of a problem to build a more efficient model.
Build relationships with educational institutions
In the US, there are about a dozen Ph.D. data science programs available at universities and nearly the same number of computer science programs that are actively emphasizing data science. Another popular way to fill the skills gap is boot camps where attendees take 12-month or so courses. This option seems very promising for companies that aren’t ready to invest into hiring experienced experts, though you should always consider additional internal training to accumulate essential domain expertise.
Step 6. Models become dated, be ready to iterate
Most of the models are developed on a static subset of data, and they capture the conditions of the time frame when the data were collected. Once you have a model or a number of them deployed, they become dated over time and provide less accurate predictions. Depending on how actively the trends in your business environment change, you should more or less frequently replace models or retrain them. There are two basic approaches to that:
Challenger testing. When the existing model is assumed to become less accurate, a new challenger model is introduced and tested against the deployed model. The old model is removed once the new one outperforms it. Then the process is repeated.
Online updates. The parameters of a model are changed under the continuous flow of new data.
So, if you want to retain your predictive analytics on the same level of accuracy, having occasional or short-term data science services is not an option.
Step 7. Decide whether you need a custom-built algorithm
Building custom data models, their deployment, and further iterative development may be a serious financial and management burden for small and midsize businesses. Using algorithms that are shipped off the shelf is a viable option if you’re looking for common prediction tasks. Large product developers like Hitachi are already preparing blueprints and even solutions to support the industries they’re focusing on. Having an out-of-the-box algorithm doesn’t necessarily mean you won’t have to customize it to align with business objectives, but it might greatly reduce the financial difficulties.
Salesforce, for instance, is offering artificial intelligence instruments that can communicate with their existing cloud solutions. The previously mentioned people.ai service along with Azure Machine Learning, Google Prediction API, and IBM Watson Analytics can be integrated with the most popular CRMs like Salesforce, Hubspot, Zoho, and some others. Guesswork offers ecommerce companies better understanding of customers by analyzing various collected data and providing tailored experiences. It integrates with ecommerce sites and can predict which visitors are more prone to conversion or it lets you tailor a newsletter to each customer. Ultimately, you can apply to Algorithmia, a marketplace of pre-built algorithms that communicate with software through REST APIs.
Is it the right time to adopt Machine Learning?
In one of his novels, Hemingway described how a man goes broke “gradually and then suddenly.” The passage aptly reflects the way machine learning progresses. Today, it’s on the top of the hype cycle, and, consistent with Gartner, the mainstream adoption will happen in 2 to 5 years. Early adopters are already actively testing and iterating to reach a high productivity stage.
In a course of a few years, it’s likely that having a data science department will be the definitive point of competition in a wide range of business verticals, as CRM systems became years ago.
Nowadays almost everything is digitally connected, whether it’s a business, a classroom, or a road trip.
People are relentlessly using different technologies to indulge the usage in their daily lives.
Gone those old days when people used to send handwritten letters, or hardly use printed text and a major reason is people use digital text that can be easily edited, shared, or for other meaningful purposes.
Let’s get deeper into it.
The connection between AI and Image to text
Thanks to the OCR technologies that use Artificial intelligence programs that can easily fetch a picture and extract text from it.
Some people might be unfamiliar with the concept of what is OCR?
Optical character recognition is a technology that uses a text detecting device like a digital camera to take pictures and then it uses a software that can extract the data from any visual and convert it for further use.
The accuracy level of Image to text
Nowadays in almost every sector, OCR has gained a lot of respect due to its AI advancement.
It has become not only an image-to-text traditional conversion process but also a human mistakes checker.
Like it’s widely used in the education sector to examine MCQs papers as it saves time, gives accurate results and stores the data very efficiently.
OCR engine’s job is to extract the data from an image that it performs perfectly, but due to its nature, it follows a pattern or in simple words, it follows a structured form of data, or else it cannot give accurate results.
Thanks to the developers who have tested and worked hard to get the best out of OCR technologies, these experts have incorporated two major distinct altogether in OCR engine such as given below:
- Machine learning – With the passage of time the OCR technologies have incorporated machine learning.
Machine learning is technology replicating human ability to different patterns of texts like fonts, gaps in between characters, colors, alignment, writing styles, language on any visual.
Sometimes when the visual quality is not good the OCR technology can miss out on the character, especially when the spacing in between characters is very congested.
In the testing segments, it can be trained to go through similar patterns so that it can detect those errors and correct it, thus improve the accuracy level.
- Intelligent data processing – Incorporating AI technologies like intelligent data processing users can minimize the extraction errors from unstructured text, it helps in identifying relevant sections required for extracting and classifies them before extracting.
Further, it trains the Machine learning modules to extract the only data required on a visual, which thankfully eliminates the need for manually entering data into an application and improve the accuracy level.
Traditional OCR engines alone were not quite as accurate as the latest ones are developed and this is all well designed because of involving AI technologies (Machine learning, intelligent data processing).
This replicates a human brain at a very low level of errors, thus giving the user accurate results.
Benefits of using Image to text converter tools
First of all image to text is basically an OCR that consists of using the latest AI technologies, to give accurate results in the form of digitized text that can be incorporated for multiple purposes. Like Prepostseo, Aconvert, Hipdf.
- It helps in converting any form of visual picture text into an editable text format;
- It helps in extracting required data in an organized format;
- It helps in pdf text recognition to further use it while writing in white papers;
- It helps to extract text from any image available on the internet by simply copy-pasting the URL;
- It helps to get accurate results by eliminating manual errors;
- It helps in scanning barcodes and interpreting it in computer language especially in bulk quantity;
- It saves time and money when students are looking to photocopy assignments and then create in word file;
- It helps in recognizing old historical handwritten documents in a digital format like a word file;
- It helps in highlighting the desirable text and converting it into a new editable format which can be used in a new picture;
- It’s more compact when storing all the data in a device storage disk, as compared to traditional manual documentation record rooms;
- It helps to translate different language’s visual text into their own preferred language, like translating Chinese signboards into English.
Uses of Image to text
As the digital world is continuously evolving, Image to text is getting used in almost every field.
Applications of Usage:
- It can be used in Legal documents, like tax or property documents for extracting the manual written document and convert it into digital format for a longer life span;
- It helps vehicle passing authorities for license plate recognition;
- It helps in public images to detect large texts especially for marketing purposes;
- It’s widely used in enterprises to share documents and edit it in own format, like pdf image to text converting purposes;
- It is used in airports for extracting desired text from passports, e-tickets and etc;
- It can be used in classrooms for saving time and easily understand the handwriting while noting down notes from other classmates;
- It is widely used in shopping stores to scan the barcode of the products and automatically generate invoices while cross-examining the price list of the product;
- It’s widely used in the medical sector as extracting the medicines from prescriptions can be a little challenging, so it can make it easier for the user to understand.
In 2019, the Ukrainian IT-company Neocortext (current RefaceAI) released the Doublicat mobile app (now Reface), with which the user can replace the face on the gif with his own. Six months later, the application was already changing faces to video, and by August the number of its installations exceeded 20 million.
Initial Release Date: Dec 23, 2019
Content Rating:Rated 12+
According to the analytics service App Annie as of August 15, Reface is among the ten most popular apps on iOS in 15 countries, and on Android – in 19.
How the user interacts with the service?
To insert their face into a GIF or video, the user takes a photo in the application and selects a template, for example, a fragment from a movie. After that, the algorithm changes its appearance in a few seconds. The result can be downloaded immediately or shared on social networks.
How does the technology work and where is it used?
Usually, when creating a deepfake, it takes a lot of time to train a neural network. At the same time, a separate network must be trained for each person.
However, RefaceAI has created a universal Artificial neural network to replace any human face, thanks to which a deepfake is obtained in seconds. The developers have trained the network on millions of images from open libraries (the company does not disclose the name), so it can change faces in both photos and videos.
Having received the user’s photo, the network “translates” it into face embeddings – an anonymized set of numbers. According to it, the machine determines the facial features and transfers them to the template.
Deepfake turns out to be more realistic thanks to machine learning, including a GAN-type neural network – its peculiarity is that it includes two networks that train each other. In the case of Reface, for example, they “adjust” the color of the user’s face to the lighting of the original video or picture.
Startup Success Story
RefaceAI, the company behind Reface, was founded in 2011 by:
- Roman Mogilny – CEO.
- Oles Petriv – Technical Director.
- Yaroslav Boyko – Chief Operating Officer.
Before the face-swapping app, entrepreneurs had been involved in various projects for seven years: developing websites, collaborating with post-production studios for Hollywood films, where machine learning technologies were needed. For example, they converted a video from 2D to 3D format.
In 2018, the company came up with the idea to create an app that would replace faces in photos. At that time, RefaceAI employed six people.
RefaceAI has raised 1 round. This was a Pre-Seed round raised on Dec 5, 2019. Adventures Lab has invested in the startup between $300,000 and $500,000.crunchbase.com & ain.ua
In March 2019, Elon Musk posted photos on Twitter with his face instead of Dwayne Johnson’s. The image featured the Reflect watermark. Due to this publication, application traffic has grown tenfold, entrepreneurs noticed.
By September, the co-founders realized that simply changing faces in photos was not enough. At that time, product manager Ivan Altsibeev joined the team. He will suggest switching to gifs. The idea turned into a Doublicat app. It was presented at Product Hunt in January 2020.
Six months later, the company added face-to-video to the app and renamed Doublicat to Reface. With the new feature, the service has grown in popularity, with Britney Spears, Snoop Dogg and other celebrities sharing their videos.
Reface currently has 20 million installs and continues to grow. How quickly, the company does not specify. Her spokesperson added that 65% of users share content created in the app.
The basic version of Reface is free. The company receives income from advertising and paid subscriptions, where you can turn off the watermark: 199 rubles per week, 299 rubles per month and 1990 rubles per year. The company does not disclose the total revenue from the service.
To replace faces in photos, developers use images with open licenses, and for gifs they partner with sites like Tenor.
In the case of the video, the company adheres to the advice of lawyers:
- Content falls under US copyright fair use and therefore does not require licensing.
- Limits the length of the videos, their quality and the rest of the content.
If the copyright holder wants to exclude their materials from the application, Reface App will remove them.
In an interview Mogilny, Petriv and Boyko explained that the popularity of such applications is usually short-lived, so they use mechanics to retain users.
According to entrepreneurs, Reface will move forward not only the appearance of new content, but also its localization – so that the user can insert his face into a clip with a popular star in the country.
Since 2018, RefaceAI has grown to 40 employees. She is currently conducting closed beta testing of the Reface Studio web platform. With its help, creators of entertainment content will be able to insert faces into any video. In the future, the company plans to replace bodies as well.
As conceived by the founders, the new service will work in the b2b segment as well: it will be useful for creative agencies, filmmakers and computer game developers.
One of the problems that Reface Studio can face is using the service to create fake news and replace the faces of famous people. To prevent possible harm to the public, developers will apply two approaches:
- You cannot use the service anonymously.
- The video created in the “studio” will have an invisible mark that the project was created using Reface Studio.
Top In-App Purchases from AppStore
- Weekly $2.99
- Annual Plus $27.99
- Monthly $4.99
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