Is there any standard way of efficiently showing a MTLTexture on a RealityKit Entity? I can't find anything proper on how to , for example, generate a LowLevelTexture out of a MTLTexture. Closest match was this two year old thread. In the old SceneKit app, we would just do guard let material = someNode.geometry?.materials.first else { return } material.diffuse.contents = mtlTexture Our flow is as follows (for visualizing the currently detected object): Camera-Stream -> CoreML Segmentation -> Send the relevant part of the MLShapedArray-Tensor to a MTLComputeShader that returns a MTLTexture -> Show the resulting texture on a 3D object to the user

Basically, take just the Xcode 26 AR App template, where we put the ContentView as the detail end of a NavigationStack. Opening app, the app uses < 20MB of memory. Tapping on Open AR the memory usage goes up to ~700MB for the AR Scene. Tapping back, the memory stays up at ~700MB. Checking with Debug memory graph I can still see all the RealityKit classes in the memory, like ARView, ARRenderView, ARSessionManager. Here's the sample app to illustrate the issue. PS: To keep memory pressure on the system low, there should be a way of freeing all the memory the AR uses for apps that only occasionally show AR scenes.

In courses like Compose interactive 3D content in Reality Composer Pro Realitykit Engineers recommended working with Reality Composer Pro to create RealityKit packages to embed in our Realitykit Xcode projects. And, comparing the workflow to Unity/Unreal, I can see the reasoning since it is nice to prepare scenes/materials/assets visually. Now when we also want to run a Xcode Cloud CI/CD pipeline this seems to come into conflict: When adding a basic *.usdz to the RealityKitContent.rkassets folder, every build we run on Xcode cloud fails with: Compile Reality Asset RealityKitContent.rkassets ❌realitytool requires Metal for this operation and it is not available in this build environment I have also found this related forum post here but it was specifically about compiling a *.skybox.

So, our app imports CoreML and loads the CoreML models when starting up. Locally all tests succeed consistently. Running Tests on Xcode Cloud, they all fail with (UI and Unit Tests) App (6846) encountered an error (Early unexpected exit, operation never finished bootstrapping - no restart will be attempted. (Underlying Error: Test crashed with signal ill before establishing connection.)) the test instance is busy for 35min and then just aborts, with all tests failing. This sounds to me like the simulator is maybe showing some exception or is stuck? Or is it possible that xcode server runs in a special environment that gets stuck on loading CoreML models?

Most models are only available as glb or fbx, so I usually reexport them into usdz using Blender. When I import them into Reality Composer Pro, Mesh, Textures etc look great, but in the Animation Library subsection all I can see is one default subtree animation. In Blender I can see all available animations and play them individually. The default subtree animation just plays the default idle animation. In fact when I open the nonlinear animation view in Blender and select a different animation as the default animation, the exported usdz shows the newly selected animation as default subtree animation. I can see in the Apple sample apps models can have multiple animations in their Animation Library. I'm using the latest Blender 4.5 and the usdz exporter should be working properly?

So experimenting with the new SpeechTranscriber, if I do: let transcriber = SpeechTranscriber( locale: locale, transcriptionOptions: [], reportingOptions: [.volatileResults], attributeOptions: [.audioTimeRange] ) only the final result has audio time ranges, not the volatile results. Is this a performance consideration? If there is no performance problem, it would be nice to have the option to also get speech time ranges for volatile responses. I'm not presenting the volatile text at all in the UI, I was just trying to keep statistics about the non-speech and the speech noise level, this way I can determine when the noise level falls under the noisefloor for a while. The goal here was to finalize the recording automatically, when the noise level indicate that the user has finished speaking.