StrainAvatar: Strain-Driven Head Avatars with Explicit Facial Skin Deformation

Head avatars reconstructed from multi-view video achieve photorealistic rendering, yet expression-dependent wrinkles are typically synthesized in appearance or modeled as directly predicted displacements, leaving their geometric cause unmodeled. We introduce StrainAvatar, a head avatar that reconstructs wrinkle-scale facial deformation as explicit geometry from multi-view RGB alone. Our key idea is to factor wrinkle formation into a shared mechanical cause and an identity-specific geometric response. Facial compression, analytically characterized by the strain of the tracked deformation, provides an identity-agnostic signal that generalizes across expressions, while a subject-specific network learns only how the skin responds to this signal. We realize this factorization by predicting per-triangle Jacobian correctives from the strain field and integrating them into a coherent corrected mesh through a Poisson solve. Because a zero corrective exactly recovers the tracked mesh, large-scale facial motion remains deterministic and learning is restricted to wrinkle-scale deformation. We further transport Gaussian primitives using the deformation gradient of the corrected mesh, making appearance explicitly inherit the recovered surface deformation. Experiments show rendering quality comparable to recent head avatars, improved surface-normal accuracy, and consistent reductions in mesh error against independent multi-view stereo depth, qualitatively recovering expression-dependent wrinkle structure as explicit, animatable geometry.