Neil Druckmann Says Naughty Dog ‘Will Not Be The Last Of Us Studio Forever,’ Multiple Single-Player Projects In Development

Naughty Dog won’t be The Last of Us studio forever, according to studio president Neil Druckmann. He told the LA Times this in a new interview, which is where he also said the studio has multiple single-player projects in development, as reported by VideoGamesChronicle.

“I promise you, we will not be The Last of Us studio forever,” Druckmann said, revealing the team is working on “multiple single-player projects.” He continued, adding, “We create experiences that are steeped in story and characters, especially relationships. The stories have some sort of philosophical core that everything is going to revolve around and feed into.”

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Elsewhere in the interview, Druckmann told the LA Times he came up with the idea about a man and a girl crossing a post-apocalyptic landscape – the premise of The Last of Us – in college. He planned to get that story out of his head one day as a graphic novel. Even after getting hired at Naughty Dog, he didn’t bring up this story to the studio, he said. Instead, he was given the task of recreating Jak and Daxter into “something more reality-based,” the publication writes. 

However, he and those on the task were struggling. They went to Naughty Dog’s then-president Evan Wells, who retired last year, and asked, “Do we need to do Jak and Daxter?” Wells said no, and Druckmann told the LA Times, “Immediately the lightbulb went off” as the time for what would become The Last of Us had arrived. And the rest is history. 

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This interview follows Druckmann’s recent interview with PlayStation, which resulted in a public rebuttal of the interview by Druckmann, with him claiming he was heavily misquoted, and PlayStation eventually pulling the interview down completely. 

Word of multiple single-player projects in development at Naughty Dog follows news from last December where Naughty Dog canceled its Last of Us live-service multiplayer project after years of development. 

For more, read Game Informer’s review of The Last of Us and then read our review of The Last of Us Part II. After that, read our impressions of Naughty Dog’s remake of The Last of Us. 

[Source: LA Times via VideoGamesChronicle]

What do you hope Naughty Dog’s next game is? Let us know in the comments below!

8 CSS & JavaScript Snippets for Awesome Reveal Effects

Not everything on a website has to be displayed straightforwardly. Sometimes, it’s prudent to hide an element. We can then reveal it automatically or via user interaction.

That’s what makes reveal effects so compelling. They can serve dual purposes. The first is to keep our layouts nice and tidy. The second is to add a bit of flair to the user experience (UX).

And there are many intriguing options for web designers. Using CSS and JavaScript offers a path to creating high-end effects. They not only look great, though. There are ways to build features that are performant and accessible as well.

Want to explore some possibilities? Check out our collection of fantastic reveal effects. They run the gamut in terms of use cases and technology.

Scratch Card CSS Reveal by Nicolas Jesenberger

This reveal effect mimics a real-world experience – using a scratch card. Use your finger or pointing device to “scratch” off the silver foil. You’ll find a little surprise underneath. It’s both clever and well-executed.

See the Pen Scratch Card by Nicolas Jesenberger

Magic Wand Reveal by Kalis Network

Here’s a snippet that takes web magic to the next level. Move the magic wand from left to right to reveal the image gallery underneath. There’s also a subtle effect for nearby images. They’re blurry and displayed with a lower opacity.

See the Pen Magic Reveal by Kalis Network

Circular Reveal Animation by Liza Shermayster

You don’t need to go overboard with reveal effects. This simple presentation reveals more of the image upon hover. And it also adds a classy text animation. It would work well on a portfolio or About Us page.

See the Pen circular reveal animation by Liza Shermayster

Text Reveal Animation by Owlypixel

How about a reveal effect that happens automatically? This animated headline is beautiful and sure to get a user’s attention. It’s also powered by CSS. That means there are no messy scripts to slow down your page load times. The JavaScript used in the snippet refreshes the demo.

See the Pen Text Reveal Animation by Owlypixel

Ink Transition Reveal by Ryan Yu

These scroll-based animations are incredible. The artwork appears to be drawn on your screen as you scroll. The effect creates a mood to enhance the UX. It’s a case of special effects fitting the content to a tee.

See the Pen Ink transition effect with PNG sprite by Ryan Yu (@iamryanyu)

Movie Poster Interaction Reveal by Ethan

Card UIs are a popular design element these days. But there’s only so much content they can hold. This snippet offers a solid workaround. Hover over a card to reveal further content. The layout remains neat while adding a bit of interactivity.

See the Pen Movie Poster Interaction by Ethan

Page Reveal Effect by Kevin Levron

Yes, you can use reveal effects for an entire page! And this tool can help you create the perfect fit for your project. Choose from several animation types and other options to build a beautiful presentation. Plus, it’s just plain fun to experiment with.

See the Pen Page Reveal Effect (CSS/VueJS) by Kevin Levron

Accessible Offcanvas Reveals by Vasileios Mitsaras

Offcanvas elements are a handy place to store extra info. They’re often used to hide mobile navigation so that users can focus on content. This demo uses jQuery to add elements that can be revealed in multiple ways.

See the Pen Accessible Offcanvas by Vasileios Mitsaras

A Revealing Way to Build a UI

Reveal effects can take many forms. They’re suitable for everything from a corporate website to an online game. Their potential is vast and varied.

It’s still important to consider the impact on users, though. The best implementations feel natural and add to the UX. Therefore, it’s best to avoid effects that get in the way of accessing content.

Thankfully, CSS and JavaScript provide plenty of leeway. You can use the combination that works best for your project.

Want to see even more reveal effects? Check out our CodePen collection!

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A technique for more effective multipurpose robots

Let’s say you want to train a robot so it understands how to use tools and can then quickly learn to make repairs around your house with a hammer, wrench, and screwdriver. To do that, you would need an enormous amount of data demonstrating tool use.

Existing robotic datasets vary widely in modality — some include color images while others are composed of tactile imprints, for instance. Data could also be collected in different domains, like simulation or human demos. And each dataset may capture a unique task and environment.

It is difficult to efficiently incorporate data from so many sources in one machine-learning model, so many methods use just one type of data to train a robot. But robots trained this way, with a relatively small amount of task-specific data, are often unable to perform new tasks in unfamiliar environments.

In an effort to train better multipurpose robots, MIT researchers developed a technique to combine multiple sources of data across domains, modalities, and tasks using a type of generative AI known as diffusion models.

They train a separate diffusion model to learn a strategy, or policy, for completing one task using one specific dataset. Then they combine the policies learned by the diffusion models into a general policy that enables a robot to perform multiple tasks in various settings.

In simulations and real-world experiments, this training approach enabled a robot to perform multiple tool-use tasks and adapt to new tasks it did not see during training. The method, known as Policy Composition (PoCo), led to a 20 percent improvement in task performance when compared to baseline techniques.

“Addressing heterogeneity in robotic datasets is like a chicken-egg problem. If we want to use a lot of data to train general robot policies, then we first need deployable robots to get all this data. I think that leveraging all the heterogeneous data available, similar to what researchers have done with ChatGPT, is an important step for the robotics field,” says Lirui Wang, an electrical engineering and computer science (EECS) graduate student and lead author of a paper on PoCo.     

Wang’s coauthors include Jialiang Zhao, a mechanical engineering graduate student; Yilun Du, an EECS graduate student; Edward Adelson, the John and Dorothy Wilson Professor of Vision Science in the Department of Brain and Cognitive Sciences and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL); and senior author Russ Tedrake, the Toyota Professor of EECS, Aeronautics and Astronautics, and Mechanical Engineering, and a member of CSAIL. The research will be presented at the Robotics: Science and Systems Conference.

Combining disparate datasets

A robotic policy is a machine-learning model that takes inputs and uses them to perform an action. One way to think about a policy is as a strategy. In the case of a robotic arm, that strategy might be a trajectory, or a series of poses that move the arm so it picks up a hammer and uses it to pound a nail.

Datasets used to learn robotic policies are typically small and focused on one particular task and environment, like packing items into boxes in a warehouse.

“Every single robotic warehouse is generating terabytes of data, but it only belongs to that specific robot installation working on those packages. It is not ideal if you want to use all of these data to train a general machine,” Wang says.

The MIT researchers developed a technique that can take a series of smaller datasets, like those gathered from many robotic warehouses, learn separate policies from each one, and combine the policies in a way that enables a robot to generalize to many tasks.

They represent each policy using a type of generative AI model known as a diffusion model. Diffusion models, often used for image generation, learn to create new data samples that resemble samples in a training dataset by iteratively refining their output.

But rather than teaching a diffusion model to generate images, the researchers teach it to generate a trajectory for a robot. They do this by adding noise to the trajectories in a training dataset. The diffusion model gradually removes the noise and refines its output into a trajectory.

This technique, known as Diffusion Policy, was previously introduced by researchers at MIT, Columbia University, and the Toyota Research Institute. PoCo builds off this Diffusion Policy work. 

The team trains each diffusion model with a different type of dataset, such as one with human video demonstrations and another gleaned from teleoperation of a robotic arm.

Then the researchers perform a weighted combination of the individual policies learned by all the diffusion models, iteratively refining the output so the combined policy satisfies the objectives of each individual policy.

Greater than the sum of its parts

“One of the benefits of this approach is that we can combine policies to get the best of both worlds. For instance, a policy trained on real-world data might be able to achieve more dexterity, while a policy trained on simulation might be able to achieve more generalization,” Wang says.

Animation of robot arm using a spatula to lift toy pancake
With policy composition, researchers are able to combine datasets from multiple sources so they can teach a robot to effectively use a wide range of tools, like a hammer, screwdriver, or this spatula.

Image: Courtesy of the researchers

Because the policies are trained separately, one could mix and match diffusion policies to achieve better results for a certain task. A user could also add data in a new modality or domain by training an additional Diffusion Policy with that dataset, rather than starting the entire process from scratch.

Animation of robot arm using toy hammer as objects are being placed randomly next around it.
The policy composition technique the researchers developed can be used to effectively teach a robot to use tools even when objects are placed around it to try and distract it from its task, as seen here.

Image: Courtesy of the researchers

The researchers tested PoCo in simulation and on real robotic arms that performed a variety of tools tasks, such as using a hammer to pound a nail and flipping an object with a spatula. PoCo led to a 20 percent improvement in task performance compared to baseline methods.

“The striking thing was that when we finished tuning and visualized it, we can clearly see that the composed trajectory looks much better than either one of them individually,” Wang says.

In the future, the researchers want to apply this technique to long-horizon tasks where a robot would pick up one tool, use it, then switch to another tool. They also want to incorporate larger robotics datasets to improve performance.

“We will need all three kinds of data to succeed for robotics: internet data, simulation data, and real robot data. How to combine them effectively will be the million-dollar question. PoCo is a solid step on the right track,” says Jim Fan, senior research scientist at NVIDIA and leader of the AI Agents Initiative, who was not involved with this work.

This research is funded, in part, by Amazon, the Singapore Defense Science and Technology Agency, the U.S. National Science Foundation, and the Toyota Research Institute.

Suda51 On Working With Swery65, James Gunn, And Finding Peace And Appreciation For Shadows Of The Damned

Goichi “Suda51” Suda is arguably the most consistently unique game designer in the industry. He created Killer7, the No More Heroes series, worked with James Gunn (before he directed Guardians of the Galaxy or was put in charge of DC films) on Lollipop Chainsaw, and lots more. In preparation for his role as the keynote speaker at MomoCon this year, we caught up with him over e-mail to discuss his next game, Hotel Barcelona (a collaboration with Deadly Premonition creator Hidetaka “Swery65” Suehiro), remastering his 2011 game Shadows of the Damned, Batman, and more.

Hotel Barcelona

Game Informer: Hotel Barcelona is inspired by horror films. Are there specific films you cite as references?

Goichi “Suda51” Suda: There are a lot of them. Swery of White Owls is the main creator, so you should probably ask him this question, as I don’t want to spoil anything. It’s mostly based on Swery’s own personal tastes – however, I do have a good amount of input in it as well – so I’d suggest continuing this interview with Swery himself. I’ll be looking forward to reading it!

How do you and Swery split the work on a game like Hotel Barcelona? Do you collaborate on a script, or do you each handle specific characters? Does one of you handle gameplay, the other story?

At first, Swery was like, “Let’s write the whole thing up character by character,” but now I’m extremely busy with Grasshopper projects so I’ve been leaving most of it up to him. We both came up with a lot of ideas, but Swery has been doing most of the work; I do provide some input sometimes, but Swery and White Owls are handling the actual development.

What are your favorite time loops in fiction?

One would be the German show Dark on Netflix. Also, Flower, Sun & Rain is time loop-based, too; that time loop was actually based on an X-Files episode called “Monday” (Season 6/Episode 14) that was really interesting. That was where the idea from Flower, Sun & Rain FSR came from.

Shadows of the Damned: Hella Remastered

Has the Shadows of the Damned: Hella Remastered tempted you to make changes that are closer to what you wanted it to be?

No; this game feels like our own child – it’s something we created, and we love it for what it is. For the remastered version, content-wise, we simply added a New Game+ feature and new costumes we weren’t able to include before. We’d always wanted to do a proper, loyal remaster for SotD. It originally stemmed from Kurayami (which ran in Edge Magazine in the UK), which is something else that I’d actually like to make a game out of at some point. But Damned is Damned, and Kurayami is something else altogether now.
Damned actually came from the sixth draft of Kurayami. If possible, I’d like to do something with all the first through five drafts, respectively, which are all very different.

The fourth draft ended up becoming Black Knight Sword, and Kurayami Dance was I believe the third or fifth draft, I think.

You were publicly unhappy with Shadows of the Damned at launch. How do you feel about it in 2024?

A lot of things happened at the time, but I look back on the whole experience pretty fondly now. While we were making the original, we definitely had a lot of friction with EA – we argued about a lot of aspects and ideas for the game, and both we and EA made a lot of compromises, but those experiences are what brought about Garcia, Paula, Fleming, and their whole story, so I love the end result as a Grasshopper game in general and as our own personal creation as well. I had some great experiences with Mikami making the game; we got called out to Los Angeles by [publisher] EA this one time, and there were like ten people in this hotel suite sitting at this big table. When we walked in, it kinda freaked us out. We got really bitched out, getting asked things like, “Just what the hell are you trying to make?!” That kind of experience is rare – especially in the video game industry – and I feel like if this was the underworld or something I probably would’ve gotten whacked in that room (laughs). So many of the experiences we had back then were super interesting and are really fond and valuable memories now.

Now that I think about it, I believe that hotel thing happened at the Marriott in Los Angeles. Like I said, there were like ten people from EA – some of their top dudes – all lined up, and we had no idea what was going on. We were simply told, “Come to this meeting.” No context or anything, so we were shocked when we got there. It’s actually sort of an awesome memory now. It wasn’t exactly scary, since being the video game industry and all, I knew we weren’t actually going to get killed or anything, but we were definitely surprised.

Anyway, I’m really proud of what we made, and I cherish all the memories I have of those times.

What is the most underrated Suda 51 game?

There would be two: Samurai Champloo: Sidetracked & Blood+ One Night Kiss. Blood+ wasn’t released outside of Japan, but Samurai Champloo was released in Japan and North America. They’re both really great games, and the stories were totally different from their respective original versions. Also, these two games basically spawned the No More Heroes.

If you get the opportunity, be sure to try Samurai Champloo. Blood+ is only available in Japanese, but if you can, please give that one a try, too. I consider Samurai Champloo, Blood+, and No More Heroes to be my “Big 3 Sword Action Games.” Without the first two, I’m not sure that No More Heroes would have ever been made.

What are some of your recent favorite video games that you didn’t work on?

I’ve downloaded a ton of games recently, but I have the bad habit of not actually getting around to playing them [laughs]. I started Helldivers 2 recently, and I’m still deciding on whether or not to really get full-on into it. I’ve also been digging Alan Wake 2, which I haven’t cleared yet but would like to sometime soon.

What are some of your recent favorite movies and TV shows?

TV: 3 Body Problem (both the Tencent version and the Netflix version); movie: The Iron Claw.

You and Swery65 use similar names. You both strive to make unique games. Does it bother you when people confuse the two of you? Or is it flattering?

I’m really good friends with Swery, so it doesn’t bother me at all. I mean, they’re totally different numbers anyway, so I really don’t get annoyed or anything when people get them mixed up.

Hidetaka “Swery65” Suehiro’s Deadly Premonition

What surprised you about working with Swery?

It’s well-known that Swery is the director and CEO of White Owls, but he’s also a really good producer, and he also does project management-related stuff, as well. He’s generally thought of as mainly a game creator, but he’s actually really proficient in all kinds of game development areas; he writes scenarios, he handles things like product management, etc. I think he has this image of just drinking lots of beer and messing around a lot, but he’s actually a super serious, hard worker and a really great producer – and although Swery himself may not want me to say stuff out loud [laughs], he’s really quite professional, which I’ve seen firsthand throughout his career, and I want people to know this.

What do you think he would say is surprising about working with you?

Probably the fact that although we promised we’d both write up the scenarios for Hotel Barcelona, I ended up not doing it and left it up to him [laughs].

Are there other developers you would like to collaborate with in the future?

I spoke with Akihiro Hino, CEO of Level-5 a little while back, and I’d really like to collaborate with him on something. The difference between the two of us is like night and day, so I think it would be really interesting to see what we could come up with.

Lollipop Chainsaw

Would you consider making a licensed DC video game with James Gunn?

I haven’t spoken with him much recently apart from a few greetings or DMs on Twitter here and there, but I think I would totally be down with doing another game with him if the opportunity presented itself. I love Batman, so if it was going to be a DC game, then I’d really like to try doing something with the Black & White series. Also, James is really good at always making sure to get back to me quickly whenever I message him, which I really appreciate, but I know that he’s obviously extremely busy, so I try not to bother him too much.

When will Killer7 get the remake/remaster treatment?

Unfortunately we don’t own the IP for Killer7 – Capcom does – so I don’t really have any say in that.