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VFX artists show that Hollywood can use AI to create, not exploit



Hollywood may be embroiled in ongoing labor disputes that involve AI, but the technology infiltrated film and TV long, long ago. At SIGGRAPH in LA, algorithmic and generative tools were on display in countless talks and announcements. We may not know where the likes of GPT-4 and Stable Diffusion fit in yet, but the creative side of production is ready to embrace them — if it can be done in a way that augments rather than replaces artists.

SIGGRAPH isn’t a film and TV production conference, but one about computer graphics and visual effects (for 50 years now!), and the topics naturally have overlapped more and more in recent years.

This year, the elephant in the room was the strike, and few presentations or talks got into it; however, at afterparties and networking events it was more or less the first thing anyone brought up. Even so, SIGGRAPH is very much a conference about bringing together technical and creative minds, and the vibe I got was “it sucks, but in the meantime we can continue to improve our craft.”

The fears around AI in production are, not to say illusory, but certainly a bit misleading. Generative AI like image and text models have improved greatly, leading to worries that they will replace writers and artists. And certainly studio executives have floated harmful — and unrealistic — hopes of partly replacing writers and actors using AI tools. But AI has been present in film and TV for quite a while, performing important and artist-driven tasks.

I saw this on display in numerous panels, technical paper presentations and interviews. Of course a history of AI in VFX would be interesting, but for the present here are some ways AI in its various forms was being shown at the cutting edge of effects and production work.

Pixar’s artists put ML and simulations to work

One early example came in a pair of Pixar presentations about animation techniques used in their latest film, Elemental. The characters in this movie are more abstract than others, and the prospect of making a person who is made of fire, water or air is no easy one. Imagine wrangling the fractal complexity of these substances into a body that can act and express itself clearly while still looking “real.”

As animators and effects coordinators explained one after another, procedural generation was core to the process, simulating and parameterizing the flames or waves or vapors that made up dozens of characters. Hand sculpting and animating every little wisp of flame or cloud that wafts off a character was never an option — this would be extremely tedious, labor-intensive and technical rather than creative work.

But as the presentations made clear, although they relied heavily on sims and sophisticated material shaders to create the desired effects, the artistic team and process were deeply intertwined with the engineering side. (They also collaborated with researchers at ETH Zurich for the purpose.)

One example was the overall look of one of the main characters, Ember, who is made of flame. It wasn’t enough to simulate flames or tweak the colors or adjust the many dials to affect the outcome. Ultimately the flames needed to reflect the look the artist wanted, not just the way flames appear in real life. To that end they employed “volumetric neural style transfer” or NST; style transfer is a machine learning technique most will have experienced by, say, having a selfie changed to the style of Edvard Munch or the like.

In this case the team took the raw voxels of the “pyro simulation,” or generated flames, and passed it through a style transfer network trained on an artist’s expression of what they wanted the character’s flames to look like: more stylized, less simulated. The resulting voxels have the natural, unpredictable look of a simulation but also the unmistakable cast of the artist’s choice.

Simplified example of NST in action adding style to Ember’s flames. Image Credits: Pixar

Of course the animators are sensitive to the idea that they just generated the film using AI, which is not the case.

“If anyone ever tells you that Pixar used AI to make Elemental, that’s wrong,” said Pixar’s Paul Kanyuk pointedly during the presentation. “We used volumetric NST to shape her silhouette edges.”

(To be clear, NST is a machine learning technique we would identify as falling under the AI umbrella, but the point Kanyuk was making is that it was used as a tool to achieve an artistic outcome — nothing was simply “made with AI.”)

Later, other members of the animation and design teams explained how they used procedural, generative or style transfer tools to do things like recolor a landscape to fit an artist’s palette or mood board, or fill in city blocks with unique buildings mutated from “hero” hand-drawn ones. The clear theme was that AI and AI-adjacent tools were there to serve the purposes of the artists, speeding up tedious manual processes and providing a better match with the desired look.

AI accelerating dialogue

Images from Nimona, which DNEG animated. Image Credits: DNEG

I heard a similar note from Martine Bertrand, senior AI researcher at DNEG, the VFX and post-production outfit that most recently animated the excellent and visually stunning Nimona. He explained that many existing effects and production pipelines are incredibly labor-intensive, in particular look development and environment design. (DNEG also did a presentation, “Where Proceduralism Meets Performance” that touches on these topics.)

“People don’t realize that there’s an enormous amount of time wasted in the creation process,” Bertrand told me. Working with a director to find the right look for a shot can take weeks per attempt, during which infrequent or bad communication often leads to those weeks of work being scrapped. It’s incredibly frustrating, he continued, and AI is a great way to accelerate this and other processes that are nowhere near final products, but simply exploratory and general.

Artists using AI to multiply their efforts “enables dialogue between creators and directors,” he said. Alien jungle, sure — but like this? Or like this? A mysterious cave, like this? Or like this? For a creator-led, visually complex story like Nimona, getting fast feedback is especially important. Wasting a week rendering a look that the director rejects a week later is a serious production delay.

In fact new levels of collaboration and interactivity are being achieved in early creative work like pre-visualization, as one talk by Sokrispy CEO Sam Wickert explained. His company was tasked with doing pre-vis for the outbreak scene at the very start of HBO’s “The Last of Us” — a complex “oner” in a car with countless extras, camera movements and effects.

While the use of AI was limited in that more grounded scene, it’s easy to see how improved voice synthesis, procedural environment generation and other tools could and did contribute to this increasingly tech-forward process.

Final shot, mocap data, mask and 3D environment generated by Wonder Studio. Image Credits: Wonder Studio

Wonder Dynamics, which was cited in several keynotes and presentations, offers another example of use of machine learning processes in production — entirely under the artists’ control. Advanced scene and object recognition models parse normal footage and instantly replace human actors with 3D models, a process that once took weeks or months.

But as they told me a few months ago, the tasks they automate are not the creative ones — it’s grueling rote (sometimes roto) labor that involves almost no creative decisions. “This doesn’t disrupt what they’re doing; it automates 80-90% of the objective VFX work and leaves them with the subjective work,” co-founder Nikola Todorovic said then. I caught up with him and his co-founder, actor Tye Sheridan at SIGGRAPH, and they were enjoying being the toast of the town: it was clear that the industry was moving in the direction they had started off in years ago. (Incidentally, come see Sheridan on the AI stage at TechCrunch Disrupt in September.)

That said, the warnings of writers and actors striking are in no way being dismissed by the VFX community. They echo them, in fact, and their concerns are similar — if not quite as existential. For an actor, one’s likeness or performance (or for a writer, one’s imagination and voice) is one’s livelihood, and the threat of it being appropriated and automated entirely is a terrifying one.

For artists elsewhere in the production process, the threat of automation is also real, and also more of a people problem than a technology one. Many people I spoke to agreed that bad decisions by uninformed leaders are the real problem.

“AI looks so smart that you may defer your decision-making process to the machine,” said Bertrand. “And when humans defer their responsibilities to machines, that’s where it gets scary.”

If AI can be harnessed to enhance or streamline the creative process, such as by reducing time spent on repetitive tasks or enabling creators with smaller teams or budgets to match their better-resourced peers, it could be transformative. But if the creative process is seconded to AI, a path some executives seem keen to explore, then despite the technology already pervading Hollywood, the strikes will just be getting started.

Disclaimer – This is just shared content from above mentioned source for knowledge sharing.


Procurement is painful, so Pivot wants to simplify it



Earlier this year, a big French tech company started requiring an email to the CEO for every purchase above €1,000. That’s because they didn’t have the right tool to manage procurement.

Meet Pivot, a new French startup that wants to overhaul spend management solutions. Pivot wants to work with young companies that are growing fast and feel like they need a procurement solution. Instead of picking a legacy business spend management system from an ERP vendor, Pivot wants to be the first (and last) procurement system for these companies.

At the helm of the startup, you will find three experienced co-founders. Romain Libeau was one of the first employees at Swile and more recently acted as the Chief Product Officer for the French unicorn. Marc-Antoine Lacroix has spent several years working for Qonto as the Chief Technology Officer and then Chief Product Officer. Estelle Giuly has been a workflow engineers for several enterprise companies and for

“I worked a lot on operations at Swile, and especially on all the internal tools. I actually saw a sequencing where first we tried to get as many customers as possible, so first we focused on all the tools for our go-to-market strategy and sales — basically Salesforce. Then, you have a lot of customers, and you want to keep them happy. So we structured our customer service, our customer success team,” Romain Libeau told me.

“And then you get to the last brick, which is how well you manage all your financial flows,” he added. And that’s where Pivot comes in.

When companies hire a head of procurement, that person usually starts by listing all the requirements and issues a call for tenders. Usually, they get to choose between Oracle NetSuite’s procurement component or maybe Coupa. It then takes several months to integrate the product in the company and procurement teams feel like they are only using 10% of the feature set.

Pivot isn’t the only startup trying to improve procurement. In the U.S., Zip and Levelpath have both raised tens of millions of dollars. “There are some regional features, European features when it comes to compliance and the payment ecosystem,” Libeau said.

But the fact that some American startups are thriving also proves that there is a real market opportunity. That’s why Pivot has already raised a $5.3 million pre-seed round (€5 million) from several VC firms (Visionaries, Emblem, Cocoa, Anamcara and Financière Saint James) as well as entrepreneurs and investors such as Loïc Soubeyrand (founder of Swile), Steve Anavi (co-founder of Qonto), Hanno Renner (co-founder of Personio), Oliver Samwer (co-founder of Rocket Internet), Pierre Laprée, Alexis Hartmann and Alexandre Berriche.

And things have been advancing at a very rapid pace. After this funding round in April, the company started developing the product over the summer and launched it in September with a first client — Voodoo.

“We’re rolling out gradually, because, as I always tell our team, more haste, less speed. But we’re going to end the year with around ten customers. So we’ve got the deals, but we don’t want to rush into anything,” Libeau said.

A PO workflow for humans

If you work for a big company and you often fill out purchase orders, you know that it’s a painful process. There are too many fields, you’re not sure what you’re supposed to write in each field and you would rather find a workaround to avoid purchase orders.

Pivot is well aware of that and has designed a tool that makes the PO workflow less painful. Admins can set up workflows from Pivot’s interface directly — no coding skills required. For instance, a very large purchase with a software vendor might trigger a security review, an IT review, a legal review, etc. That’s why Pivot is betting on third-party integrations and an interface that works for everyone.

Pivot integrates directly with your existing tech stack. It fetches the company’s org chart for the approval workflow from the HR system, it retrieves budgets from Pigment, Anaplan, etc. It then communicates with your communication tools, such as Slack, Microsoft Teams and Jira.

And, of course, Pivot integrates with ERP software (NetSuite, SAP…) so that vendors, cost centers, compliance rules and more are instantly propagated once a purchase order is validated.

Too many companies waste time in approvals and endless workflows. Pivot wants to add a layer of spend management without slowing down business teams. And the timing seems right as many companies are reviewing how they spend money.

Image Credits: Pivot

Disclaimer – This is just shared content from above mentioned source for knowledge sharing.

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Why we’re seeing so many seed-stage deals in fintech



Welcome back to The Interchange, where we take a look at the hottest fintech news of the previous week. If you want to receive The Interchange directly in your inbox every Sunday, head here to sign up! It was a relatively quiet week in fintech startup land, so we took the time to scrutinize where we’re seeing the most funding deals.

Seed deals everywhere

Across the board in all industries, except perhaps AI, we’ve seen a big drop in later-stage funding deals and no shortage of seed-stage rounds.

When it comes to fintech, I can tell you at least anecdotally that the vast majority of pitches that hit my inbox are for seed rounds. It is very rare these days to get pitched for Series B or later, or even for Series A rounds.

Venture banker Samir Kaji, co-founder and CEO of Allocate, points out that the private markets often take their cues from the public markets and as such, it’s no surprise that we’re seeing far fewer later-stage deals and a plethora of seed rounds. The Fintech Index — which tracks the performance of emerging, publicly traded financial technology companies — was down a staggering 72% in 2022, according to F-Prime Capital’s State of Fintech 2022 report.

“Seed is typically the least affected because those companies are just too early to really feel like you have to worry about where the public markets are,” he told me in a phone interview last week. “We’re so far divorced from the time period where these companies are going to be large enough where the public market sentiment is going to really matter.”

Allocate, which recently just closed on $10 million in capital, is currently an investor in about 60 funds. But Kaji is seeing the tide beginning to turn.

“The investment pace in 2022 was just so slow, and the beginning of 2023 was incredibly slow as well, but we’re starting to see things pick up as people are now starting to see that the bid ask on deals at the Series A and later are starting to narrow,” Kaji added. “And I think entrepreneurs have started to capitulate to this new environment. This always is the case — it’s like an 18- to 24-month lag in the public markets. So I would expect much more later-stage activity again in the next 18 to 24 months.”

I asked our friends at PitchBook what they’re seeing, and unsurprisingly, in the second quarter, there were more seed deals forged in the retail fintech space (135) compared to any other stage. When it came to the enterprise fintech space, early-stage deals accounted for most of the deal activity (239) with seed-stage coming in a close second (221), according to PitchBook.

Will we start seeing more later-stage deals in 2024? I sure hope so. Will we see any fintechs actually go public? That’s probably less likely. But you can be sure we’ll be on the lookout.

Slope continues its climb

It’s always great to see startups rise through the ranks, especially at a time when fintech hasn’t been doing so well. One of the companies I have had the pleasure of following is Slope. The company, founded by Lawrence Murata and Alice Deng, developed a business-to-business payments platform for enterprise companies.

When covering the company’s initial $8 million seed round in 2021, I learned that Slope’s origins came from Murata watching his wholesaler family struggle with an easier way to manage payments. He and Deng built the company so that moving to a digital order-to-cash workflow was seamless.

Last year, Slope raised another $24 million in Series A funding, and this week banked $30 million in a venture round led by Union Square Ventures, which co-led the Series A. It also included participation from OpenAI’s Sam Altman and a list of other heavy VC hitters. Read more. — Christine

co-founders Lawrence Lin Murata and Alice Deng, B2B payments

Slope co-founders Lawrence Lin Murata and Alice Deng. Image Credits: Slope

Weekly News

TechCrunch Opinion: Fintech actually has a value system: Here’s how we can reclaim it

Introducing the a16z Global Payments Hub

Other items we are reading:

Apple is ordered to face Apple Pay antitrust lawsuit

Greenlight celebrates launch of web-based financial literacy library

Funding and M&A

As seen on TechCrunch

Pan-African contrarian investor P1 Ventures reaches $25M first close for its second fund

QED and Partech back South African payment orchestration platform Revio in $5.2M seed

Crediverso takes on legal after $3.5M capital infusion

Series, which aims to replace ERP systems, lands $25M

Seen elsewhere

Luge Capital: $71M first close of second fund completed

Colektia completes purchase of non-performing loans for $72M

Mexico’s albo receives $40m in Series C funds, striving for neobank profitability

Grow Credit Inc., a top 30 fintech app, secures $10m funding with USAA as lead investor in Series A round

StretchDollar raises $1.6M in pre-seed funding

WealthTech Vega exits stealth with over $8M funding

Farther closes Series B funding round to gain $131M valuation — This new round comes a little over a year after the wealth tech firm raised a Series A on a $50 million valuation. Check out TechCrunch’s earlier coverage of Farther.

Image Credits: Bryce Durbin

Disclaimer – This is just shared content from above mentioned source for knowledge sharing.

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How to raise a Series A in today’s market



If you’re an early-stage founder, the crazy days of 2021 are a distant memory. Money is tight, and the process of getting more is as unsettled as ever.

The past few tumultuous years have tossed out the milestones that defined previous Series A benchmarks. But that doesn’t mean the game is lost. At this year’s TechCrunch Disrupt, three investors shared their perspectives on what’s changed, what’s working today, and what advice they’re giving founders who are looking to raise a Series A.

“As companies mature to seed and Series A, a year and a half ago, if you were at a million or even approaching a million in revenue, a Series A would come together in a snap. That has changed really quickly,” Maren Bannon, co-founder and managing partner at January Ventures, told the audience. “Now it’s probably more like 2 [million] to 3 million in revenue where those rounds come together in a snap.”

For founders, the moving goalposts can be incredibly frustrating — especially since the reasons for it are beyond their control. After a remarkable 13-year bull run, uncertainty crept into the market last year, dampening investor appetite for risk. Rising interest rates compounded the problem.

As a result, Series A investors have pulled back dramatically. “What we’ve noticed in the statistics is that the Series A deployment is down 60% over the last year and a half. The amount deployed per Series A is down 25% from $10 million to $7.5 million. And the number of deals getting done is much fewer,” said James Currier, general partner at NFX.

“The bulk of seed stage companies were [successfully] raising off of story, not traction,” Loren Straub, general partner at Bowery Capital, said of market conditions two years ago. “I think there’s been a real shift in focus towards traction, momentum, legitimate product-market fit.”

“A lot of the Series A investors are understandably looking for a higher bar,” she added.

A market crowded with venture capitalists hasn’t helped, either, Currier said. Back in the ’90s, there were about 150 general partners in the U.S., he said. Today, there are more than 31,000 listed on Signal, a network of investors his firm runs.

Disclaimer – This is just shared content from above mentioned source for knowledge sharing.

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