ChronoMesh: Two-Stage Age Editing for 3D Head Mesh

Mesh-based head assets are widely used in production, but many recent aging methods target volumetric 3D representations that do not translate cleanly to UV material maps. We propose ChronoMesh, a two-stage framework for age editing of existing 3D head meshes that updates both global head shape and wrinkle-normal details while preserving identity. Because facial aging is inherently multi-scale, jointly optimizing geometry and texture under image-based supervision tends to favor local shading cues, under-expressing macro-scale deformation. Our two-stage design mitigates this issue by separating shape deformation from material editing. In the first stage, we predict an age-conditioned residual in a parametric head model to drive shape deformation toward a target age. During this stage, age-dependent proxy normal maps provide wrinkle cues consistent with the target age. In the second stage, we synthesize age-conditioned edits for UV-space intrinsic maps using a pre-trained StyleGAN with inversion-based latent editing, including wrinkle-normal synthesis. Both stages use image-based supervision through differentiable rendering. Experiments show that our edits preserve identity better than both image-space editors and mesh-reconstruction baselines while moving the rendered appearance toward the target age, with wrinkles represented explicitly in the normal maps rather than baked into diffuse reflectance.