I have an entity that was created using Mixamo, and it has an animation. after the animation completes the mesh of the robot is not where the entity is positioned. I want to do something like when the animation finishes, I set the root entity's transform to the mesh's transform. There are no transformations applied to any of the children of this root of the model, which means that the transformations are applied to the skeleton due the the playing of animations. Is there a way where I can apply the final position of the root of the skeleton to the root entity to make sure to position the entity where the animation has ended just before the next animation plays?

At a recent community meeting we were wondering how Apple creates this soft-edge effect around the occlusion cutouts. We see this effect on keyboard cutouts, iPhone cutouts, and in progressive spaces. An example: Notice the soft edged around the occlusion cutout for the keyboard One of our members created some Shader Graph materials to explore soft edges. These work by sending data into the opacity channel of the PreviewSurface node. Unfortunately, the Occlusion Surface nodes lack any sort of input. If you know how to blend these concepts with RealityKit Occlusion, please let us know!

I've been experimenting with the Muse pen and understand that it can be accessed by my app through a SpatialTrackingSession, but is there any current or planned support for devices like this as for general UI input like game controllers are? For example, using the button as a tap analogue for SwiftUI views.

Environment Versions ・macOS15.6.1 ・visionOS26.0.1 ・Xcode16.1 or 26.0.1 ・unity6000.2.9f1 ・Apple.core3.2.0 ・Apple.PHASE1.2.7 ・polyspatial2.4.2 With the above environment, after installing Apple.PHASE into unity and building to a visionOS device, Audio is available and distance attention works, but Early Reflection and Late Reverb produce no audible change even when checked and their parameters are adjusted. What is required to make Early Reflection and Late Reverb take effect on a visionOS device build? action taken ・created a SoundEvent. ・in composer, created a Sampler and a SpatialMixer; attached an AudioClip to the Sampler; enabled Direct Path, Early Reflection, and Late Reverb on the SpatialMixer. ・attached a PHASE Source to the object to be played, attached the created SoundEvent to it, and set non-zero values for Early Reflection and Late Reverb. ・attached a PHASE Listener to the mainCamera and set the ReverbPreset to a value other than None. ・in project settings > Audio, set Spatializer plugin to PHASE Spatializer. ・from there, build for visionOS.

Hi. I am mixing content destined for Vision Pro. Locked to video. I have the AAX installer and the ASAF video player demonstrated in the quicktimes is nit included in the install package for pro tools. Would it be possible to post a link ?

I'm placing sphere at finger tip and updating its position as hand move. Finger joint tracking functions correctly, but I’ve observed noticeable latency in hand tracking updates whenever a UITextView becomes active. This lag happens intermittently during app usage, lasting about 5–10 seconds, after which the latency disappears and the sphere starts following the finger joints immediately. When I open the immersive space for the first time, the profiler shows a large performance spike upto 328%. After that, it stabilizes and runs smoothly. Note: I don’t observe any lag when CPU usage spikes to 300% (upon immersive view load) yet the lag still occurs even when CPU usage remains below 100%. I’m using the following code for hand tracking: private func processHandTrackingUpdates() async { for await update in handTracking.anchorUpdates { let handAnchor = update.anchor if handAnchor.isTracked { switch handAnchor.chirality { case .left: leftHandAnchor = handAnchor updateHandJoints(for: handAnchor, with: leftHandJointEntities) case .right: rightHandAnchor = handAnchor updateHandJoints(for: handAnchor, with: rightHandJointEntities) } } else { switch handAnchor.chirality { case .left: leftHandAnchor = nil hideAllJoints(in: leftHandJointEntities) case .right: rightHandAnchor = nil hideAllJoints(in: rightHandJointEntities) } } await MainActor.run { handTrackingData.processNewHandAnchors( leftHand: self.leftHandAnchor, rightHand: self.rightHandAnchor ) } } } And here’s the function I’m using to update the joint positions: private func updateHandJoints( for handAnchor: HandAnchor, with jointEntities: [HandSkeleton.JointName: Entity] ) { guard handAnchor.isTracked else { hideAllJoints(in: jointEntities) return } // Check if the little finger tip and intermediate base are both tracked. if let tipJoint = handAnchor.handSkeleton?.joint(.littleFingerTip), let intermediateBaseJoint = handAnchor.handSkeleton?.joint(.littleFingerIntermediateTip), tipJoint.isTracked, intermediateBaseJoint.isTracked, let pinkySphere = jointEntities[.littleFingerTip] { // Convert joint transforms to world space. let tipTransform = handAnchor.originFromAnchorTransform * tipJoint.anchorFromJointTransform let intermediateBaseTransform = handAnchor.originFromAnchorTransform * intermediateBaseJoint.anchorFromJointTransform // Extract positions from the transforms. let tipPosition = SIMD3<Float>(tipTransform.columns.3.x, tipTransform.columns.3.y, tipTransform.columns.3.z) let intermediateBasePosition = SIMD3<Float>(intermediateBaseTransform.columns.3.x, intermediateBaseTransform.columns.3.y, intermediateBaseTransform.columns.3.z) // Calculate the midpoint. let midpointPosition = (tipPosition + intermediateBasePosition) / 2.0 // Position the sphere at the midpoint and make it visible. pinkySphere.isEnabled = true pinkySphere.transform.translation = midpointPosition } else { // If either joint is not tracked, hide the sphere. jointEntities[.littleFingerTip]?.isEnabled = false } // Update the positions of all other hand joint spheres. for (jointName, entity) in jointEntities { if jointName == .littleFingerTip { // Already handled the pinky above. continue } guard let joint = handAnchor.handSkeleton?.joint(jointName), joint.isTracked else { entity.isEnabled = false continue } entity.isEnabled = true let jointTransform = handAnchor.originFromAnchorTransform * joint.anchorFromJointTransform entity.transform.translation = SIMD3<Float>(jointTransform.columns.3.x, jointTransform.columns.3.y, jointTransform.columns.3.z) } } I’ve attached both a profiler trace and a video recording from Vision Pro that clearly demonstrate the issue. Profiler: https://drive.google.com/file/d/1fDWyGj_fgxud2ngkGH_IVmuH_kO-z0XZ Vision Pro Recordings: https://drive.google.com/file/d/17qo3U9ivwYBsbaSm26fjaOokkJApbkz- https://drive.google.com/file/d/1LxTxgudMvWDhOqKVuhc3QaHfY_1x8iA0 Has anyone else experienced this behavior? My thought is that there might be some background calculations happening at the OS level causing this latency. Any guidance would be greatly appreciated. Thanks!

I am running a Spatial Rendering App template demo， it shows “No People Found ” “There is no one nearby to share with”. How can I stream videos rendered by Mac to my vision pro I am using macOS 26.0, visionOS 26, Xcode 26

Spatial photo in RealityView has a default corner radius. I made a parallel effect with spatial photos in ScrollView(like Spatial Gallery), but the corner radius disappeared on left and right spatial photos. I've tried .clipShape and .mask modifiers, but they did't work. How to clip or mask spatial photo with corner radius effect?

Hello Community, I’m currently working with the sample code “CapturingDepthUsingTheLiDARCamera” and using it to capture the depth map of an image taken with the iPhone 14 Pro. From this depth map, I generate a point cloud using the intrinsic camera parameters. I've noticed that objects not facing the camera directly appear distorted in the resulting point cloud. For example: An object with surfaces that are perpendicular to each other appears with a sharper angle in the point cloud — around 60° instead of 90°. My question is: Is this due to the general accuracy limitations of the LiDAR sensor? Or could it be related to the sample code? To obtain the depth map, I’m using: AVCapturePhoto.depthData.converting(toDepthDataType: kCVPixelFormatType_DepthFloat32) Thanks in advance for your help!

Hi, I'm currently implementing 180° / 360° property for immersive video in my app. I was able to implement 360° easily by just giving VideoMaterial to flipped sphere. However, I'm bit stuck at 180°. I want to implement by setting VideoMaterial to hemisphere mesh. But since RealityKit doesn't provide default function such like MeshResource.generateHemisphere yet, I just want to apply VideoMaterial half front visible, and half back transparent. I thought this would make my sphere looks like hemisphere. But I can't find my way to implement this method.. I would appreciate any advice / idea / information that might help.

Hello all, I saw this interesting VisionOS app: https://apps.apple.com/us/app/splitscreen-multi-display/id6478007837 I was wondering if there was any documentation on the Swift APIs that were used to create this app.

传输后的直播流分辨率显著下降，画面细节丢失、清晰度不足，导致 3D 家具商品的纹理、尺寸等关键信息无法精准展示，影响用户对商品的判断。 期望 优化流传输过程中的分辨率压缩策略，减少传输过程中的画质损耗，提升 Mac 端接收的直播流清晰度，匹配 3D 商品展示的高精度需求。

Hello, I'm developing a visionOS application for Apple Vision Pro that aims to scan unknown physical objects, capture their 3D data (such as meshes or point clouds), and export them as 3D models. Ideally, I'd also like to visualize these reconstructions in real-time within the headset. This functionality is similar to what's available in Reality Composer on iPad and iPhone, but I'm seeking to implement it natively on Vision Pro. I've reviewed the visionOS documentation but haven't found clear guidance on accessing LiDAR depth data or performing scene reconstruction. Specifically, I'm interested in: 1.Accessing LiDAR or depth data from Vision Pro's sensors. 2.Utilizing ARKit's scene reconstruction capabilities on visionOS. 3.Exporting captured 3D data as models (e.g., USDZ or OBJ formats). Are there APIs or frameworks in visionOS that support these features?

Currently I am using mixed style immersive view to place both my WindowView(plain style) and ImmersiveView content together. The issue is that the rendering depth testing may always let the virtual content block my normal WindowView. Is it possible to manually set windowedVIew always displays in the front of my virtual view in mixed style immersion? (I know modelSortGroup but it doesn't quite fits here) Or if I can dynamically change the .progressive value when the immersive space is open (set the value to zero means .mixed itself right?)

Hi Apple Developer Community, I'm developing an eye-tracking application using ARKit's ARFaceTrackingConfiguration and ARFaceAnchor.blendShapes for gaze detection using Xcode. I'm experiencing several calibration and accuracy issues and would appreciate insights from the community. Current Implementation Using ARFaceAnchor.blendShapes (.eyeLookUpLeft, .eyeLookDownLeft, .eyeLookInLeft, .eyeLookOutLeft, etc.) Implementing custom sensitivity curves and smoothing algorithms Applying baseline correction and coordinate mapping Using quadratic regression for calibration point mapping Issues I'm Facing 1. Calibration Mismatch Red dot position doesn't align with where I'm actually looking Significant offset between intended gaze point and actual cursor position Calibration seems to drift or become inaccurate over time 2. Extreme Eye Movement Requirements Need to make exaggerated eye movements to reach screen edges/corners Natural eye movements don't translate to proportional cursor movement Difficulty reaching certain screen regions even with calibration 3. Sensitivity and Stability Issues Cursor jitters or jumps around when looking at center Too much sensitivity to micro-movements Inconsistent behavior between calibration and normal operation 4. I also noticed that tracking on calibration screen as well as tracking on reading screen works better as expected when head movement is there, but I do not want much head movement. I want tracking with normal eye movement while reading an Ebook. Primary Question: Word-Level Eye Tracking Feasibility Is word-level eye tracking (tracking gaze as users read through individual words in an ebook) technically feasible with current iPhone/iPad hardware? I understand that Apple's built-in eye tracking is primarily an accessibility feature for UI navigation. However, I'm wondering if the TrueDepth camera and ARKit's eye tracking capabilities are sufficient for: Tracking natural reading patterns (left-to-right, line-by-line progression) Detecting which specific words a user is looking at Maintaining accuracy for sustained reading sessions (15-30 minutes) Working reliably across different users and lighting conditions Questions for the Community Hardware Limitations: Are iPhone/iPad TrueDepth cameras capable of the precision needed for word-level tracking, or is this beyond current hardware capabilities? Calibration Best Practices: What calibration strategies have worked best for accurate gaze mapping? How many calibration points are typically needed? Reading-Specific Challenges: Are there particular challenges when tracking reading behavior vs. general gaze tracking? Alternative Approaches: Are there better approaches than ARKit blend shapes for this use case? Current Setup Devices: iPhone 14 Pro iOS Version: iOS 18.3 ARKit Version: Latest available Any insights, experiences, or technical guidance would be greatly appreciated. I'm particularly interested in hearing from developers who have worked on similar eye tracking applications or have experience with the limitations and capabilities of ARKit's eye tracking features. Thank you for your time and expertise!

I've encountered an unexpected crash with RoomPlan on iOS 16 devices. The odd part is the code is protected by an available check, since I'm using newer RoomPlan features. Xcode error dyld[40588]: Symbol not found: _$s8RoomPlan08CapturedA0V16USDExportOptionsV5modelAEvgZ I can repro using the Apple sample code. https://developer.apple.com/documentation/roomplan/create-a-3d-model-of-an-interior-room-by-guiding-the-user-through-an-ar-experience Modify RoomCaptureViewController.swift as follows. Remove try finalResults?.export(to: destinationURL, exportOptions: .parametric) Add if #available(iOS 17.0, *) { try finalResults?.export(to: destinationURL, exportOptions: .model) } else { try finalResults?.export(to: destinationURL, exportOptions: .parametric) } I would have expected this code to at least compile and run on older devices. When the app was targeting iOS 15, the available checks worked as expected and the app is able to launch properly.

it looks like one week after accepting as a nearby other AVP device... it expires since we are providing our clients for a timeless app to walk inside archtiecture, it's a shame that not technical staff should connect every week 5 devices to work together is there any roundabout for this issue or straight to the wishlist ? thanks for the support !!

当我进入混合空间时，出现一个模型，但模型后面有一个 windowGroup，无法完全查看。如果我想点击进入 mix 空间，我需要使用代码将 windowGroup 移动到另一个位置，而不是手动移动 ![]（“https://developer.apple.com/forums/content/attachment/0471ead0-4c74-43a7-9ecc-12e67e81cec6” “title=WechatIMG31.jpg;宽度=915;高度=777”）

切换后两者的亮度、色彩饱和度、对比度等画质参数差距较大，导致画面视觉体验割裂，破坏直播连贯性，影响用户观看沉浸感。 期望 "· 对标常规直播单反相机的画质基准，优化 Vision Pro 的画面亮度、色彩还原能力； · 提供设备端或配套软件的画质自定义调节功能（亮度、对比度、色温等），支持直播前手动校准，确保与单反相机画面风格一致。"

Hello, I am trying to build an AVP app for real-time "zero-latency" spatial video streaming. I am trying to figure out, on a high level, the best way to do this. Currently this is my method: Server sends stereo images via a WebRTC service (ie, livekit) The WebRTC stream is converted to a CVPixelBuffer, writes them to file, plays via AVPlayer, and applies a VideoMaterial to a plane entity. However, this is a bit hacky and it seems like this won't be compatible with Apple's spatial experinces. To my understanding, Apple supports HLS streaming for spatial experiences and APMP content. However, HLS (and even Low Latency HLS) introduces a second or more of latency, likely do to the segmentation nature of HLS. Thus, HLS will not work for us. Some other alternatives I've thought of are streaming the live stream video via webrtc from the server to a local computer in the AVP's network, and then using LL-HLS to stream from the local computer to the vision pro. Still, it seems like this would introduce latency on the order of seconds. Is my current approach the best way to implement this? Or could anyone suggest a better way, perhaps something compatible with AVP's spatial experiences