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原文摘要

Deformable material surfaces, such as cloth, owe their appearance to fine-scale geometries that restructure under deformation. Even when coarse simulation models efficiently recover this microstructure, explicitly instantiating and rendering it remains expensive. We introduce Deformable Texture Maps (DTM), a representation that compiles deformation-driven microgeometry into a continuous and compact neural texture. Our model maps texture coordinates and local deformations to intrinsic maps describing opacity, surface normals, depth, and material identity. This captures the occlusion and visibility changes generated by deformation. At runtime, these maps provide a common texture-space representation of deformation-driven fine-scale structure that can be evaluated directly on coarse deforming surfaces. Since our representation encodes geometric attributes rather than radiance, lighting and material response remain decoupled from the learned model. This allows us to apply relighting, as well as shading and material editing without retraining. We further introduce a persistent texture-space rendering strategy that caches these intrinsic maps and selectively updates them as deformation changes. This effectively decouples neural evaluation from frame rendering and enables their reuse across frames and conventional rendering effects. We demonstrate our approach on yarn-based textiles, where non-linear material stretch and shear induce changes in yarn geometry, spacing and occlusion. We build our model by, first, sweeping tileable yarn patches through deformation space, then, rendering the detailed geometry into a canonical texture-space, and finally, compressing the resulting maps into a single neural representation. Our resulting model can be evaluated directly on arbitrary coarse garment meshes and integrated with conventional shading pipelines, keeping appearance authorable at render time.