How China’s synthetic media startup Surreal nabs funding in 3 months

What if we no longer needed cameras to make videos and can instead generate them through a few lines of coding?

Advances in machine learning are turning the idea into a reality. We’ve seen how deepfakes swap faces in family photos and turn one’s selfies into famous video clips. Now entrepreneurs with AI research background are devising tools to let people generate highly realistic photos, voices, and videos using algorithms.

One of the startups building this technology is China-based Surreal. The company is merely three months old but has already secured a seed round of $2-3 million from two prominent investors, Sequoia China and ZhenFund. Surreal received nearly ten investment offers in this round, founder and CEO Xu Zhuo told TechCrunch, as investors jostled to bet on a future shaped by AI-generated content.

Prior to founding Surreal, Xu spent six years at Snap, building its ad recommendation system, machine learning platform, and AI camera technology. The experience convinced Xu that synthetic media would become mainstream because the tool could significantly “lower the cost of content production,” Xu said in an interview from Surreal’s a-dozen-person office in Shenzhen.

Surreal has no intention, however, to replace human creators or artists. In fact, Xu doesn’t think machines can surpass human creativity in the next few decades. This belief is embodied in the company’s Chinese name, Shi Yun, or The Poetry Cloud. It is taken from the title of a novel by science fiction writer Liu Cixin, who tells the story of how technology fails to outdo the ancient Chinese poet Li Bai.

“We have an internal formula: visual storytelling equals creativity plus making,” Xu said, his eyes lit up. “We focus on the making part.”

In a way, machine video generation is like a souped-up video tool, a step up from the video filters we see today and make Douyin (TikTok’s Chinese version) and Kuaishou popular. Short video apps significantly lower the barrier to making a professional-looking video, but they still require a camera.

“The heart of short videos is definitely not the short video form itself. It lies in having better camera technology, which lowers the cost of video creation,” said Xu, who founded Surreal with Wang Liang, a veteran of TikTok parent ByteDance.

Commercializing deepfakery

Some of the world’s biggest tech firms, such as Google, Facebook, Tencent and ByteDance, also have research teams working on GAN. Xu’s strategy is not to directly confront the heavyweights, which are drawn to big-sized contracts. Rather, Surreal is going after small and medium-sized customers.

Surreal’s face swapping software for e-commerce sellers

Surreal’s software is currently only for enterprise customers, who can use it to either change faces in uploaded content or generate an entirely new image or video. Xu calls Surreal a “Google Translate for videos,” for the software can not only swap people’s faces but also translate the languages they speak accordingly and match their lips with voices.

Users are charged per video or picture. In the future, Surreal aims to not just animate faces but also people’s clothes and motions. While Surreal declined to disclose its financial performance, Xu said the company has accumulated around 10 million photo and video orders.

Much of the demand now is from Chinese e-commerce exporters who use Surreal to create Western models for their marketing material. Hiring real foreign models can be costly, and employing Asian models doesn’t prove as effective. By using Surreal “models”, some customers have been able to achieve 100% return on investment (ROI), Xu said. With the multi-million seed financing in its pocket, Surreal plans to find more use cases like online education so it can collect large volumes of data to improve its algorithm.

Uncharted territory

The technology powering Surreal, called generative adversarial networks, is relatively new. Introduced by machine learning researcher Ian Goodfellow in 2014, GANs consist of a “generator” that produces images and a “discriminator” that detects whether the image is fake or real. The pair enters a period of training with adversarial roles, hence the nomenclature, until the generator delivers a satisfactory result.

In the wrong hands, GANs can be exploited for fraud, pornography and other illegal purposes. That’s in part why Surreal starts with enterprise use rather than making it available to individual users.

Companies like Surreal are also posing new legal challenges. Who owns the machine-generated images and videos? To avoid violating copyright, Surreal requires that the client has the right to the content they upload for moderation. To track and prevent misuse, Surreal adds an encrypted and invisible watermark to each piece of the content it generates, to which it claims ownership. There’s an odd chance that the “person” Surreal produces would match someone in real life, so the company runs an algorithm that crosschecks all the faces it creates with photos it finds online.

“I don’t think ethics is something that Surreal itself can address, but we are willing to explore the issue,” said Xu. “Fundamentally, I think [synthetic media] provides a disruptive infrastructure. It increases productivity, and on a macro level, it’s inexorable, because productivity is the key determinant of issues like this.”

AWS reorganizes DeepRacer League to encourage more newbies

AWS launched the DeepRacer League in 2018 as a fun way to teach developers machine learning, and it’s been building on the idea ever since. Today, it announced the latest league season with two divisions: Open and Pro.

As Marcia Villalba wrote in a blog post announcing the new league, “AWS DeepRacer is an autonomous 1/18th scale race car designed to test [reinforcement learning] models by racing virtually in the AWS DeepRacer console or physically on a track at AWS and customer events. AWS DeepRacer is for developers of all skill levels, even if you don’t have any ML experience. When learning RL using AWS DeepRacer, you can take part in the AWS DeepRacer League where you get experience with machine learning in a fun and competitive environment.”

While the company started these as in-person races with physical cars, the pandemic has forced them to make it a virtual event over the last year, but the new format seemed to be blocking out newcomers. Because the goal is to teach people about machine learning, getting new people involved is crucial to the company.

That’s why it created the Open League, which as the name suggests is open to anyone. You can test your skills and if you’re good enough, finishing in the top 10%, you can compete in the Pro division. Everyone competes for prizes, as well, such as vehicle customizations.

The top 16 in the Pro League each month race for a chance to go to the finals at AWS re:Invent in 2021, an event that may or may not be virtual, depending on where we are in the pandemic recovery.

Brandwatch is acquired by Cision for $450M, creating a PR, marketing and social listening giant

Online consumer intelligence and social media listening platform Brandwatch has been acquired by Cision, best known for its media monitoring and media contact database services, for $450 million, in a combined cash and shares deal. TechCrunch understands Brandwatch’s key executive team will be staying on. The move combines two large players to offer a broad range of services from PR to marketing and online customer engagement. The deal is expected to close in the second quarter of 2021.

Cision has a media contact database of approximately 1 million journalists and media outlets and claims to have over 75,000 customers. Brandwatch applies AI and machine learning the practice known as ‘social listening’.

Along the way, Brandwatch raised a total of around $65 million. It was Series A-funded by Nauta Capital, followed by Highland Europe and then Partech.

IN a statement, Giles Palmer, founder, and CEO of Brandwatch said: “We have always built Brandwatch with ambition… Now is the time to take the next step – joining a company of significant scale to create a business and a suite of products that can have an important global impact.”

Abel Clark, CEO of Cision said: “The continued digital shift and widespread adoption of social media is rapidly and fundamentally changing how brands and organizations engage with their customers. This is driving the imperative that PR, marketing, social, and customer care teams fully incorporate the unique insights now available into consumer-led strategies. Together, Cision and Brandwatch will help our clients to more deeply understand, connect and engage with their customers at scale across every channel.”

Brandwatch has been on an almost case-study of a journey from fundraising to acquisition to a merger, but less characteristically for a well-funded tech company, it did much of it from its home-town of Brighton, on the southern coast of England.

The financing journey began for Giles Palmer, with Angel funding in 2006. In 2010 Brandwatch raised $1.5m from Durrants, a marketing and PR firm, and Nauta Capital. In 2014 it raised $22 million in funding in a Series B round led by Highland Capital. That was followed by a $33M Series C financing led by Partech Ventures in 2015.

With the war chest, it went on to acquire BuzzSumo in 2017, a content marketing and influencer identification platform, for an undisclosed sum. And in 2019 Brandwatch merged with a similar business, Crimson Hexagon, creating a business with around $100 million in ARR. It also acquired the London-based SaaS research platform Qriously.

Brandwatch was recently named a leader in Forrester’s guide for buyers of social listening solutions.

MyHeritage now lets you animate old family photos using deepfakery

AI-enabled synthetic media is being used as a tool for manipulating real emotions and capturing user data by genealogy service, MyHeritage, which has just launched a new feature — called ‘deep nostalgia‘ — that lets users upload a photo of a person (or several people) to see individual faces animated by algorithm.

The Black Mirror-style pull of seeing long lost relatives — or famous people from another era — brought to a synthetic approximation of life, eyes swivelling, faces tilting as if they’re wondering why they’re stuck inside this useless digital photo frame, has led to an inexorable stream of social shares since it was unveiled yesterday at a family history conference… 

MyHeritage’s AI-powered viral marketing playbook with this deepfakery isn’t a complicated one: They’re going straight for tugging on your heart strings to grab data which can be used to drive sign ups for their other (paid) services. (Selling DNA tests is their main business.)

It’s free to animate a photo using the ‘deep nostalgia’ tech on MyHeritage’s site but you don’t get to see the result until you hand over at least an email (along with the photos you want animating, ofc) — and agree to its T&Cs and privacy policy. Both of which have attracted a number of concerns, over the years.

Last year, for example, the Norwegian Consumer Council reported MyHeritage to the national consumer protection and data authorities after a legal assessment of the T&Cs found the contract it asks customers to sign to be “incomprehensible”.

In 2018 MyHeritage also suffered a major data breach — and data from that breach was later found for sale on the dark web, among a wider cache of hacked account info pertaining to several other services.

The company — which, as we reported earlier this week, is being acquired by a US private equity firm for ~$600M — is doubtless relying on the deep pull of nostalgia to smooth over any individual misgivings about handing over data and agreeing to its terms.

The face animation technology itself is impressive enough — if you set aside the ethics of encouraging people to drag their long lost relatives into the uncanny valley to help MyHeritage cross-sell DNA testing (with all the massive privacy considerations around putting that kind of data in the hands of a commercial entity).

Looking at the inquisitive face of my great grandmother I do have to wonder what she would have made of all this?

The facial animation feature is powered by Israeli company D-ID, a TechCrunch Disrupt battlefield alum — which started out building tech to digital de-identify faces with an eye on protecting image and video from being identifiable by facial recognition algorithms.

It released a demo video of the photo-animating technology last year. The tech uses a driver video to animate the photo — mapping the facial features of the photo onto that base driver to create a ‘live portrait’, as D-ID calls it.

“The Live Portrait solution brings still photos to life. The photo is mapped and then animated by a driver video, causing the subject to move its head and facial features, mimicking the motions of the driver video,” D-ID said in a press release. “This technology can be implemented by historical organizations, museums, and educational programs to animate well-known figures.”

It’s offering live portraits as part of a wider ‘AI Face’ platform which will offer third parties access to other deep learning, computer vision and image processing technologies. D-ID bills the platform as a ‘one-stop shop’ for syntheized video creation.

Other tools include a ‘face anonymization’ feature which replaces one person’s face on video with another’s (such as for documentary film makers to protect a whistleblower’s identity); and a ‘talking heads’ feature that can be used for lip syncing or to replace the need to pay actors to appear in content such as marketing videos as it can turn an audio track into a video of a person appearing to speak those words.

The age of synthesized media is going to be a weird one, that’s for sure.

 

Docyt raises $1.5M for its ML-based accounting automation platform

Accounting isn’t a topic that most people can get excited about — probably not even most accountants. But if you’re running any kind of business, there’s just no way around it. Santa Clara-based Docyt wants to make the life of small and medium business owners (and their accounting firms) a bit easier by using machine learning to handle a lot of the routine tasks around collecting financial data, digitizing receipts, categorization and — maybe most importantly — reconciliation.

The company today announced that it has raised a $1.5 million seed-extension round led by First Rays Venture Partners with participation from Morado Ventures and a group of angel investors. Docyt (pronounced “docket”) had previously raised a $2.2 million seed round from Morado Ventures, AME Cloud Ventures, Westwave Capital, Xplorer Capital, Tuesday and angel investors. The company plans to use the new investment to accelerate its customer growth.

At first glance, it may seem like Docyt competes with the likes of QuickBooks, which is pretty much the de facto standard for small business accounting. But Docyt co-founder and CTO Sugam Pandey tells me that he thinks of the service as a partner to the likes of QuickBooks.

Image Credits: Docyt

“Docyt is a product for the small business owners who find accounting very complex, who are very experienced on how to run and grow their business, but not really an expert in accounting. At the same time, businesses who are graduating out of QuickBooks — small business owners sometimes become midsized enterprises as well — [ … ] they start growing out of their accounting systems like QuickBooks and looking for more sophisticated systems like NetSuite and Sage. And Docyt fits in in that space as well, extending the life of QuickBooks for such business owners so they don’t have to change their systems.”

In its earliest days, Docyt was a secure document sharing platform with a focus on mobile. Some of this is still in the company’s DNA, with its focus on being able to pull in financial documents and then reconciling that with a business’ bank transactions. While other systems may put the emphasis on transaction data, Docyt’s emphasis is on documents. That means you can forward an emailed receipt to the service, for example, and it can automatically attach this to a financial transaction from your credit card or bank statement (the service uses Plaid to pull in this data).

Image Credits: Docyt

For new transactions, you sometimes have to train the system by entering some of this information by hand, but over time, Docyt should be able to do most of this automatically and then sync your data with QuickBooks.

“Docyt is the first company to apply AI across the entire accounting stack,” said Amit Sridharan, founding general partner at First Rays Venture Partners. “Docyt software’s AI-powered data extraction, auto categorization and auto reconciliation is unparalleled. It’s an enterprise-level, powerful solution that’s affordable and accessible to small and medium businesses.”

Berlin’s MorphAIs hopes its AI algorithms will put its early-stage VC fund ahead of the pack

MorphAIs is a new VC out of Berlin, aiming to leverage AI algorithms to boost its investment decisions in early-stage startups. But there’s a catch: it hasn’t raised a fund yet.

The firm was founded by Eva-Valérie Gfrerer who was previously head of Growth Marketing at FinTech startup OptioPay and her background is in Behavioural Science and Advanced Information Systems.

Gfrerer says she started MorphAIs to be a tech company, using AI to assess venture investments and then selling that as a service. But after a while, she realized the platform could be applied an in-house fund, hence the drive to now raise a fund.

MorphAIs has already received financing from some serial entrepreneurs, including: Max Laemmle, CEO & Founder Fraugster, previously Better Payment and SumUp; Marc-Alexander Christ, Co-Founder SumUp, previously Groupon (CityDeal) and JP Morgan Chase; Charles Fraenkl, CEO SmartFrog, previously CEO at Gigaset and AOL; Andreas Winiarski, Chairman & Founder awesome capital Group.

She says: “It’s been decades since there has been any meaningful innovation in the processes by which venture capital is allocated. We have built technology to re-invent those processes and push the industry towards more accurate allocation of capital and a less-biased and more inclusive start-up ecosystem.”

She points out that over 80% of early-stage VC funds don’t deliver the minimum expected return rate to their investors. This is true, but admittedly, the VC industry is almost built to throw a lot of money away, in the hope that it will pick the winner that makes up for all the losses.

She now plans to aim for a pre-seed/seed fund, backed by a team consisting of machine learning scientists, mathematicians, and behavioral scientists, and claims that MorphAIs is modeling consistent 16x return rates, after running real-time predictions based on market data.

Her co-founder is Jan Saputra Müller, CTO and Co-Founder, who co-founded and served as CTO for several machine learning companies, including askby.ai.

There’s one problem: Gfrerer’s approach is not unique. For instance, London-based Inreach Ventures has made a big play of using data to hunt down startups. And every other VC in Europe does something similar, more or less.

Will Gfrerer manage to pull off something spectacular? We shall have to wait and find out.

Canva acquires background removal specialists Kaleido

Kaleido, makers of a drag-and-drop background removal service for images and video, have been acquired by up and coming digital design platform Canva. While the price and terms are not disclosed, it is speculated that this young company may have fetched nearly nine figures.

It’s the right product at the right time, seemingly. In 2019, the Vienna-based Kaleido made remove.bg, a quick, simple, free, and good-enough background removal tool for images. It became a hit among the many people who need to quickly do that kind of work but don’t want to fiddle around in Photoshop.

Then late last year they took the wraps off Unscreen, which did the same thing for video — a similar task conceptually, but far more demanding to actually engineer and deploy. The simplicity and effectiveness of the tool practically begged to be acquired and integrated into a larger framework by the likes of Adobe, but Canva seems to have beaten the others to the punch.

Animated image showing a stack of books on a table in a room, but the table and room get deleted.

Image Credits: Unscreen

The acquisition was announced at the same time as another by Canva: product mockup generator Smartmockups, suggesting a major product expansion by the growing design company.

We completely bootstrapped Kaleido with no investors involved from day one,” said co-founder and CEO of Kaleido, Benjamin Groessing, in a press release. “It has just been two founders and an incredible team. We’ve been profitable from the start — so this acquisition wasn’t essential for our existence. It just made sense on so many levels.”

The company declined to provide any further details on the acquisition beyond that the brand and name are expected to survive — at least Unscreen, which makes perfect sense as a product name even under another company.

German outlets Die Presse and Der Brutkasten cited sources putting the purchase “reiht sich dahinter ein” or in the same rank as the largest Austrian exits (the largest of which was Runtastic at €220M), though still in the two-digit millions — which suggests a price approaching $100M.

The team at kaleido celebrating their acquisition - each member has been digitally added.

Image Credits: Kaleido

Whatever the exact amount, it seems to have made the team very happy. And don’t worry – they put that image together using their own product for each person.

Aquarium scores $2.6M seed to refine machine learning model data

Aquarium, a startup from two former Cruise employees, wants to help companies refine their machine learning model data more easily and move the models into production faster. Today the company announced a $2.6 million seed led by Sequoia with participation from Y Combinator and a bunch of angel investors including Cruise co-founders Kyle Vogt and Dan Kan.

When the two co-founders CEO Peter Gao and head of engineering Quinn Johnson, were at Cruise they learned that finding areas of weakness in the model data was often the problem that prevented it from getting into production. Aquarium aims to solve this issue.

“Aquarium is a machine learning data management system that helps people improve model performance by improving the data that it’s trained on, which is usually the most important part of making the model work in production,” Gao told me.

He says that they are seeing a lot of different models being built across a variety of industries, but teams are getting stuck because iterating on the data set and continually finding relevant data is a hard problem to solve. That’s why Aquarium’s founders decided to focus on this.

“It turns out that most of the improvement to your model, and most of the work that it takes to get it into production is about deciding, ‘Here’s what I need to go and collect next. Here’s what I need to go label. Here’s what I need to go and retrain my model on and analyze it for errors and repeat that iteration cycle,” Gao explained.

The idea is to get a model into production that outperforms humans. One customer Sterblue offers a good example. They provide drone inspection services for wind turbines. Their customers used to send out humans to inspect the turbines for damage, but with a set of drone data, they were able to train a machine learning model to find issues. Using Aquarium, they refined their model and improved accuracy by 13%, while cutting the cost of human reviews in half, Gao said.

The 7 person Aquarium startup team.

The Aquarium team. Image: Aquarium

Aquarium currently has 7 employees including the founders, of which three are women. Gao says that they are being diverse by design. He understands the issues of bias inherent in machine learning model creation, and creating a diverse team for this kind of tooling is one way to help mitigate that bias.

The company launched last February and spent part of the year participating in the Y Combinator Summer 2020 cohort. They worked on refining the product throughout 2020, and recently opened it up from beta to generally available.

Aquarium scores $2.6M seed to refine machine learning model data

Aquarium, a startup from two former Cruise employees, wants to help companies refine their machine learning model data more easily and move the models into production faster. Today the company announced a $2.6 million seed led by Sequoia with participation from Y Combinator and a bunch of angel investors including Cruise co-founders Kyle Vogt and Dan Kan.

When the two co-founders CEO Peter Gao and head of engineering Quinn Johnson, were at Cruise they learned that finding areas of weakness in the model data was often the problem that prevented it from getting into production. Aquarium aims to solve this issue.

“Aquarium is a machine learning data management system that helps people improve model performance by improving the data that it’s trained on, which is usually the most important part of making the model work in production,” Gao told me.

He says that they are seeing a lot of different models being built across a variety of industries, but teams are getting stuck because iterating on the data set and continually finding relevant data is a hard problem to solve. That’s why Aquarium’s founders decided to focus on this.

“It turns out that most of the improvement to your model, and most of the work that it takes to get it into production is about deciding, ‘Here’s what I need to go and collect next. Here’s what I need to go label. Here’s what I need to go and retrain my model on and analyze it for errors and repeat that iteration cycle,” Gao explained.

The idea is to get a model into production that outperforms humans. One customer Sterblue offers a good example. They provide drone inspection services for wind turbines. Their customers used to send out humans to inspect the turbines for damage, but with a set of drone data, they were able to train a machine learning model to find issues. Using Aquarium, they refined their model and improved accuracy by 13%, while cutting the cost of human reviews in half, Gao said.

The 7 person Aquarium startup team.

The Aquarium team. Image: Aquarium

Aquarium currently has 7 employees including the founders, of which three are women. Gao says that they are being diverse by design. He understands the issues of bias inherent in machine learning model creation, and creating a diverse team for this kind of tooling is one way to help mitigate that bias.

The company launched last February and spent part of the year participating in the Y Combinator Summer 2020 cohort. They worked on refining the product throughout 2020, and recently opened it up from beta to generally available.

3D model provider CGTrader raises $9.5M Series B led by Evli Growth Partners

3D model provider CGTrader, has raised $9.5M in a Series B funding led by Finnish VC fund Evli Growth Partners, alongside previous investors Karma Ventures and LVV Group. Ex-Rovio CEO Mikael Hed also invested and joins as Board Chairman. We first covered the Vilnius-based company when it raised 200,000 euro from Practica Capital.

Founded in 2011 by 3D designer Marius Kalytis (now COO), CGTrader has become a signifiant 3D content provider – it even claims to be the world’s largest. In its marketplace are 1.1M 3D models and 3.5M 3D designers, service 370,000 businesses including Nike, Microsoft, Made.com, Crate & Barrel, and Staples.

Unlike photos, 3D models can also be used to create both static images as well as AR experiences, so that users can see how a product might fit in their home. The company is also looking to invest in automating 3D modeling, QA, and asset management processes with AI. 

Dalia Lasaite, CEO and co-founder of CGTrader said in a statement: “3D models are not only widely used in professional 3D industries, but have become a more convenient and cost-effective way of generating amazing product visuals for e-commerce as well. With our ARsenal enterprise platform, it is up to ten times cheaper to produce photorealistic 3D visuals that are indistinguishable from photographs.”

CGTrader now plans to consolidate its position and further develop its platform.

The company competes with TurboSquid (which was recently acquired for $75 million by Shutterstock) and Threekit.