WWDC25: Combine Metal 4 machine learning and graphics Demonstrated a way to combine neural network in the graphics pipeline directly through the shaders, using an example of Texture Compression. However there is no mention of using which ML technique texture is compressed. Can anyone point me to some well known model/s for this particular use case shown in WWDC25.

I’m working on a Vision Pro app using Metal and need to implement multi-pass rendering. Specifically, I want to render intermediate results to a texture, then use that texture in a second pass for post-processing before presenting the final output. What’s the best approach in visionOS? Should I use multiple render passes in a single command buffer or separate command buffers? Any insights on efficiently handling this in RealityKit or Metal? Thanks!

Hi, Apple’s documentation on Order-Independent Transparency (OIT) describes an approach using image blocks, where an array of size 4 is allocated per fragment to store depth and color in a tile shading compute pass. However, when increasing the scene’s depth complexity by adding more overlapping quads, the OIT implementation fails due to the fixed array size. Is there a way to dynamically allocate storage for fragments based on actual depth complexity encountered during rasterization, rather than using a fixed-size array? Specifically, can an adaptive array of fragments be maintained and sorted by depth, where the size grows as needed instead of being limited to 4 entries? Any insights or alternative approaches would be greatly appreciated. Thank you!