Shadowing Practice: Unreal Engine 5.8 - Experimental MCP Server Support Walkthrough | State of Unreal 2026 - Learn English Speaking with Video
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197 משפטים
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Thanks, Simon.
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Now, at times, making games can be hard,
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expensive, and slow, where the gap between what you can imagine and what you can make is held back by technical overhead.
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I don't know about you, but spending hours in a node graph trying to untangle
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and understand what's going on just to change one thing is not my idea of creativity.
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There has to be a better way to do this.
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So that's what we've been working on, tools that enable you to build faster and iterate more.
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Now, Unreal has been built on a simple idea, your source, your pipeline, your workflow.
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Give developers complete control over the tools and get out of the way. That philosophy hasn't changed.
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We're just applying it to large language models.
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Now in 5.8, we've implemented an MCP server that acts as a bridge between Unreal and any model that you want.
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For this presentation, we're going to be using Cloud Code.
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But you can use Gemini, your own custom model, whatever you want.
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Enabling the Unreal MCP is as simple as turning on the plugin, doing a bit of config, and connecting to the model of your choice.
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And everything you're about to see today runs on that setup.
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Now, these are still early days, and models will still do some really unpredictable things.
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However, with this approach, we maintain the ability to go in and edit as you would any Unreal scene.
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OK, let's see this in action.
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On the left, we have Unreal Engine.
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On the right is a terminal running Cloud Code.
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You can see every message going back and forth.
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Cloud Code is talking to Unreal through the MCP server, reading the scene, and taking actions directly inside the engine.
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So let's start with something small, a modern living room.
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We start by giving the model some context, what we want to build and whereabouts in the level.
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And to make sure everyone can follow along, We're showing a summarized version of each prompt as we go.
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But if you want the full details, you can still see that in the terminal window.
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We're going to speed things up from here for presentation purposes, but everything you're seeing is real work happening inside the engine.
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So you can ask for the basics, a sofa, a rug, and a coffee table.
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Then fill it in with chairs, a lamp, and a side table.
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Semantic Search, which is part of the 5.8 release, pulls assets from your library that fit that description.
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And because this is unreal, we can also manually add a chair, describe it as a reading nook, and the model will furnish the room accordingly.
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Every step here is a creative decision that you're making.
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Models are good at broad iterations.
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You're good at knowing exactly what you want.
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Those same principles work whether you're furnishing a room or whether you want to scale up and build an entire city.
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So let's take that same approach and build a city.
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What characteristics each one has.
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Highways cut through on your direction.
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And from those, the road network starts to fill in.
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Place them in the level.
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Ask for a forest around the edge, and it can fill that in.
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At every step, you're making the calls.
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What do you end up with?
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It's not a rendered image.
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It's real geometry, real procedural content generation graphs that you can open, edit, and modify.
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And this works for a whole team, too.
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Other developers can work on their own sessions, building assets and dropping them straight into the city.
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The road network adjusts, the blocks update, and the city absorbs it.
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Different people, different sessions, and it all works together.
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So how does all of this actually work?
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With the MCP server as the bridge between Unreal and your model, it gives your model the ability to read your scene,
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understand your assets, and make changes directly in the engine.
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And because it's open, you're not locked into any particular model or workflow.
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Now, while large language models are great at language and reasoning, they don't naturally think spatially.
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Unreal does.
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To help connect the two, we're giving you over 80 foundational procedural content generation building blocks,
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a library of examples, and skills that encode specific Unreal workflows.
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Now, this is experimental, and it's going to keep getting better.
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This is where you come in.
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Tell us what's working.
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Tell us what's not.
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That's how we're going to make this better.
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Now that the city is built, we want to light it.
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Getting lighting right on Unreal is a skill that means touching dozens of parameters that all need to work together.
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Light balance, cloud materials, sky atmosphere, post-process.
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That's a lot to know.
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But by creating a dedicated lighting skill, You just describe what you want, and the model orchestrate it is.
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Under the hood, it's adjusting all those settings in concert.
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Then we can ask for overcast skies.
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And this is a good example of where the model gets things wrong.
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It misread the sky and tweaked the cloud material the wrong way.
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You can see exactly what happened, correct it in the next prompt, and you're right back to where you want to be.
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Because models also have real-world knowledge, You can just say Bogota at 9.30 in the morning.
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And it will go and figure out the sun position, direction, temperature, and atmosphere that actually matches that.
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And if you want to match something specific, you can take an image, so here we used one in Vancouver, and ask it to replicate that look.
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It uses screenshots to assess and iterate until it gets there.
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And everything is still editable.
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Grab a road and move it, the city adjusts around it.
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Ask it to make a building taller or more prominent in the scene, and it does that.
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But then you can go in and modify any individual asset, making it exactly the way that you want it.
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This isn't the generated image that you're stuck with.
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It's a real scene in Unreal, and you control every part of it.
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Flashing lamps, steam, and a sense of danger as the player gets close.
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That normally means days of back and forth across multiple disciplines.
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So here's what we did instead.
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The warning lamp already has a material.
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Let's say you want to add something to it.
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You ask the model to read the existing graph, build the new logic, and insert it right where it needs to go.
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You can then jump into the material instance editor and tweak the final values by hand.
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Then you take a Niagara template and extend it, adding a single parameter that drives multiple emitters at once, giving you control over the whole effect in one place.
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You build out the HUD through prompting and UMG.
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Status display, danger meter, warning states.
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Then blueprint logic ties it all together, a proximity trigger connected to the material, the Niagara system, and the HUD.
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Walk in, and everything fires at once.
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All right, so let's run it.
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Lamps are off.
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It's quiet.
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She walks toward the site.
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The lamps come on.
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The steam starts rising.
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She steps inside, lamps are flashing, steam's pouring in, and the site is erupting.
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What we've shown here today would take months to build by hand.
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But with the MCP server and Unreal, our artists were able to make all of this in days.
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When you lower the technical friction, you can iterate more and make better games.
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That's what we've built.
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This is really just a starting point.
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Bring your own primitives, your own examples, your own skills built around your pipelines.
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The more your team puts in over time, the more useful it gets for everyone on the project.
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We're shipping the MCP server, the PCG primitive plugin, and the skills we've developed as part of 5.8 today.
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It's open and it's yours to extend.
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Go download it, point your model at it, and see what you can do.
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If you want to go deeper, we've got two chances to catch our tech talk here at Fest.
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Come find us.
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Now I'm gonna hand it over to Pat to talk about how some of what I've shown you
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impacts linear content creation in the engine.
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PAT TUBOCH, Thanks, Michael.
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Hi, everyone.
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My name is Pat Tuobach.
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I've got a background in film and television.
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For media and entertainment, the MCP plugin that Michael just showed you opens up a wide range of new connections to LLMs,
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but also to image and video models like Nano Banana, GPT Image, Grok Imagine, Luma, Seed Dance, and many more.
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Michael was telling you about tools in the present, and I'm going to cover tools for creating linear content
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that are releasing early next year that give you control over these models.
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The standard approach to using an image model is to start by crafting a text prompt.
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In this case, I wrote a novel describing a fictional scene in Epic Motors, a 1950s gas station.
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But in professional situations where you have to hit story points, match exact continuity, and deliver what your creative director wants,
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you would typically communicate visually through sketches and pre-riffs.
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Unreal is great for that.
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So, to get started in Unreal, I scouted the derelict corridor scene from Quixel that's available on Fab,
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which I quickly customized to look like the gas station layout I needed.
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I worked in detail lighting mode since I was just using this for blocking to this stage.
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I dropped in some of the appropriate assets from that map, shelving, a propane tank, a cabinet, and also made proxy shapes for the assets that I didn't have,
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a long counter, more shelving, a cash register, and an ice chest.
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Then I backed up and framed my camera view
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and animated a boom down and a push in that I'd later use for my shot.
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But before I tackled the full video creation, I had to create a single style frame, the visual reference of my scene.
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To do that, I used the model to combine a depth pass plus the prompt novel I'd written before.
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The depth conditions the models to understand the layout and the framing.
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Here's how that works using our new tools in Unreal.
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I asked ColladeCode, connected by our MCP plugin, to use Unreal's movie render graph to create a 4K image plus all the rest.
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Asking for an Unreal render to create the image was a signal to ColladeCode
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that I wanted to give my model my depth pass, but used the creative look described in my prompt.
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I then fired off that request.
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And while it was being generated, I inspected the graph that it made for the task.
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Image models are hungry for helpful data and conditioning, and so I prepared a process that pulls valuable visual data from the Unreal scene and passes it to the image model,
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in this case, Google's Nano Banana Pro.
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Now, let's take a look at this image more closely.
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You can see it matched the composition of my model.
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I wanted another tire on the floor, but I didn't there to mask the tire.
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I selected both the mask and the image, and with a predefined workflow, asked Claude Code to extract it, repair it, and mesh it using Tripo 3.1.
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And then I double-checked that graph, and I supervised the work.
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Now, of course, this is not a real-time process.
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So after several minutes, I got my texture tire mesh.
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I saved it to the content browser, where I then pulled in the tire and positioned it in 3D space.
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And finally, I asked Cloud Code to add the tire to the image.
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Let's take a look at that result.
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So if we compare my UE passes with the tire in it, you can see that it put the new tire where I asked it to, and it made it look like a variation of the generated tire, not an exact amount in 3D.
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I went browsing on FAB and found one of our junkyard car parts for mega scans.
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I picked the specific fender piece that I wanted
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that had a 1950s look modeled into it to leave less to the image model's imagination.
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I asked Claude Code to add it to my image and make it look like it had been newly repainted.
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And after another image processing step in banana, banana, that was done.
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OK, now that I've produced my style frame, I was ready to go back to our new Unreal tools to generate a video of our scene.
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And here's my shot camera move again.
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If you recall, this is the move that I created at the beginning.
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Over on the right, I ran a sequence render node that represents the depth over the entire range.
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I'm going to use the same depth restyling process with the video that I did on the still image.
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I supply the style frame I just generated to the video model as a first frame reference, and you can see that I added a cat, just hoping for a bit of life.
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And then I set my video generation node to use CDance 2.0, set the resolution, added audio, and then I was off.
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Once that process had run for several minutes, I had a video that looked like this.
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So clearly, this is showing you that I can control the camera, but nobody controls a cat.
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This is a different workflow that we've in parallel for more rapid concepting with diffusion and your viewport side by side.
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What you're seeing here in this video is the UE viewport on the left
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that was streaming to a local FluxKline image model on the right.
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The local model result isn't temporarily coherent.
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It was stamping a new image on every frame and only running a few frames a second, which is why it seems slower over there on the right.
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But let me show you how intuitive it is to use.
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I prompted for sunset lighting at first
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and then decided to see what it would look like if there had just been a fire in this apartment.
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And then I tried something more extreme, like being underwater at the bottom of a murky pond.
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The real benefit you get here is being able to go back to the UE viewport, interact with the assets, and see them reposition through this new diffusion lens.
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Not only that, but you can try more illustrated looks, like a charcoal drawing or even a watercolor painting.
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As models get more capable and hardware improves, it's not hard to project this workflow forward to a time when diffusion is real-time and temporally consistent.
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OK, let's look at some other examples of Unreal Engine scenes restyled by artists in the Engine using a variety of sources,
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some of which are premium environment packs from our friends at KitBash 3D, and some of which are our own internal assets, including the city sample.
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Starting from quality source assets leaves less room for interpretation by the models.
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We think these new UI tools are a force multiplier
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that will give artists more opportunities for creative iteration on their work alongside the real-time animation,
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modeling, texturing, and virtual production tools that you've come to count on in Unreal Engine.
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So this is where we're headed.
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The MCP server that's enabling all this is experimental.
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And what you're seeing here for image and video creation will be available early next year.
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We're on this journey together, and we're aware that for these models to be truly useful, we need to focus on not just lowering technical complexity,
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but also giving you full creative control over what you're making.
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Thank you.
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Thanks, Pat.
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That was awesome.
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אוצר מילים והערות הגייה לשיעור הזה
שיעור הדיבור הזה ברמה C1 מבוסס על הסרטון “Unreal Engine 5.8 - Experimental MCP Server Support Walkthrough”. המילים שחוזרות הכי הרבה: model, unreal, image, scene. בסרטון הזה יש 197 משפטים ו-2656 מילים לתרגול Shadowing. הדיבור נמשך 15:47. הדובר מדבר בקצב טבעי של כ-168 מילים בדקה, קרוב לשיחה יומיומית. רק 81% מהמילים נמצאות בין 3,000 המילים הנפוצות ביותר באנגלית, כך שאוצר המילים מאתגר.
אוצר מילים מרכזי בסרטון הזה
15 המילים המתקדמות ביותר בסרטון, עם ההגייה והמשמעות שלהן:
| מילה | הגייה | משמעות |
|---|---|---|
| lamp שם עצם | /ˈlæ̝mp/ | מְנוֹרָה |
| graph שם עצם | /ɡɹæf/ | גְּרָף |
| banana שם עצם | /bəˈnænə/ | בָּנָנָה |
| adjust פועל | /əˈd͡ʒʌst/ | כוונן, התאים |
| furnish פועל | /ˈfɝnɪʃ/ | רִהֵט |
| shelve פועל | /ʃɛlv/ | גָּנַז |
| editable שם תואר | ניתן לעריכה | |
| fender שם עצם | /ˈfɛnd.ə(ɹ)/ | כנף |
| multiplier שם עצם | /ˈmʌltɪˌplaɪəɹ/ | כּוֹפֵל |
| intuitive שם תואר | /ɪnˈtjuːɪtɪv/ | אינטואיטיבי |
| charcoal שם עצם | /ˈt͡ʃɑɹ.koʊl/ | פֶּחָם עֵץ |
| coherent שם תואר | /koʊˈhɛɹ.ənt/ | עקבי, עקיב |
| unpredictable שם תואר | בלתי צפוי | |
| template שם עצם | /ˈtɛm.plɪt/ | תַּבְנִית, שַׁבְּלוֹנָה |
| illustrate פועל | /ˈɪl.əˌstɹeɪt/ | הִמְחִישׁ |
Phrasal verbs שתשמעו
| מילה | משמעות |
|---|---|
| back up פועל | נָסוֹג |
| figure out פועל | הֵבִין, מצא תשובה |
משפטים שכדאי לחזור עליהם
משפטים קצרים ושלמים מהסרטון שאפשר להשתמש בהם בשיחה יומיומית:
- We're just applying it to large language models.
- You're good at knowing exactly what you want.
- At every step, you're making the calls.
- What do you end up with?
- That's how we're going to make this better.
דקדוק בסרטון הזה
המבנים שהדובר משתמש בהם הכי הרבה, עם המילים המדויקות מהסרטון:
| מבנה | בסרטון |
|---|---|
| הווה מושלם (Present perfect) have/has + past participle — פעולה שקרתה בעבר ועדיין יש לה חשיבות או השפעה עכשיו | has been built · hasn't changed · we've implemented |
| סביל (Passive voice) be + past participle — הדגש הוא על מה שקורה, לא על מי שעושה את הפעולה | is held · has been built · is built |
| משפטי זיקה (Relative clauses) who / which + clause — מידע נוסף על אדם או דבר | Search, which is · second, which is |
הגייה ששווה לשים לב אליה
הדובר משתמש ב-46 קיצורים וצורות מקוצרות, כמו you're, we're, we've. אמרו אותם בצורה הקצרה, כפי שאתם שומעים אותם.
- הצלילים של “sh” ו-“zh”: diffusion /dɪˈfju.ʒən/, furnish /ˈfɝnɪʃ/, iteration /ˌɪt.əˈɹeɪ.ʃən/, shelve /ʃɛlv/, mesh /mɛʃ/
- מילים ארוכות — שימו לב להטעמה: iteration /ˌɪt.əˈɹeɪ.ʃən/, procedural /pɹəˈsiːd͡ʒ(ə)ɹəl/, parameter /pəˈɹæm.ə.tɚ/, foundational /faʊnˈdeɪ.ʃə.nəl/, intuitive /ɪnˈtjuːɪtɪv/
צלילים שדוברי עברית מתקשים בהם:
- /w/ — שפתיים מעוגלות, לא /v/: workflow /ˈwɝkfloʊ/, whereabouts /ˌ(h)wɛəɹəˈbaʊts/, underwater /ʌndɚˈwɔtəɹ/
- /æ/ — פתוח יותר מה-“אֶ” העברית, לא כמו “a” ב-“cup”: lamp /ˈlæ̝mp/, graph /ɡɹæf/, banana /bəˈnænə/, parameter /pəˈɹæm.ə.tɚ/, animate /ˈæn.ə.mət/
- /r/ — הלשון לא נוגעת בשום מקום, לא ה-“ר” הגרונית: unreal /ʌnˈɹi.əl/, prompt /pɹɑmpt/, graph /ɡɹæf/, render /ˈɹɛn.dɚ/, iterate /ˈɪtəɹeɪt/
איך לתרגל עם הסרטון הזה
- הקשיבו לכל הסרטון פעם אחת בלי לדבר, ורשמו את המילים שאתם לא מכירים.
- התחילו במהירות 0.75×, תרגלו Shadowing משפט אחר משפט, וחזרו למהירות רגילה כשזה מרגיש קל.
- הקליטו את עצמכם והשוו למקור, תוך תשומת לב למילים כמו lamp, graph, banana.
מהי טכניקת השאדואינג?
שאדואינג היא טכניקת למידת שפה מבוססת מדע שפותחה במקור להכשרת מתורגמנים מקצועיים ופופולרית על ידי הפוליגלוט ד"ר אלכסנדר ארגואלס. השיטה פשוטה אך עוצמתית: אתה מקשיב לאודיו באנגלית ילידית וחוזר עליו בקול רם מיד — כמו צל העוקב אחרי הדובר עם עיכוב של 1–2 שניות בלבד. בניגוד להאזנה פסיבית או תרגילי דקדוק, שאדואינג מכריח את המוח והשרירים בפה לעבד ולשחזר דפוסי דיבור אמיתיים בו זמנית. מחקרים מראים שזה משפר משמעותית את דיוק ההגייה, האינטונציה, הקצב, הדיבור המחובר, הבנת השמיעה ושטף הדיבור — מה שהופך אותו לאחת השיטות היעילות ביותר להכנה לIELTS Speaking ולתקשורת באנגלית בעולם האמיתי.