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The analytical material models commonly used in VR often fall short of reproducing complex real\u2010world appearances. Recently, neural materials have emerged as a promising alternative, offering a compact yet effective representation of real\u2010world materials. Deploying neural materials on low\u2010power mobile VR devices poses significant challenges due to the computational complexity of neural networks and the high display resolution and frame rate requirements of VR devices (commonly 72+ frames per second). We address these challenges by leveraging texture\u2010space shading with spatiotemporal computation amortization, driven by a compact, coarse\u2010to\u2010fine neural material model of extremely low capacity. Thanks to our distillation training scheme, our compact neural materials achieve visual quality comparable to NeuMIP [KMX*21] at a much lower cost. 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