SARS: A novel face and body shape and appearance aware 3D reconstruction system extends morphable models

3D morphable models (3DMMs) reconstruct the structure and appearance of 3D objects from 2D images, particularly human faces and bodies. Conventional 3DMM pipelines combine identity and expression blendshapes with a template mesh and are controlled by parameters such as shape, texture, illumination, and camera pose. However, many 3D human reconstruction methods focus on global facial geometry and make limited use of semantic facial cues such as age, gender, and landmark structure, characterising facial boundaries, curves, dips, and wrinkles. In order to adapt these high-level facial characteristics, this paper presents SARS, a shape- and appearance- aware modular reconstruction system that extracts face and body information from a single image and reconstructs a full body 3D human model. SARS improves the accuracy of semantic surface editing to capture expression-driven facial muscle deformation and appearance changes for different genders, while maintaining identity. SARS has three transformation modules. The first module estimates high-level facial attributes, including age, gender, and facial landmarks. The second module combines 3DMM expression coefficients, displacement maps, signed distance fields, and semantic facial features in a latent space to generate an identity-aware 3D face mesh. The third module uses the Skinned Multi-Person Linear model (SMPL) to extract high-level descriptors of the full body and based on these parameters, generates a 3D body model. A mesh fusion module then integrates the reconstructed face with the body mesh. Experiments on MICC, 3DPW, and EHF indicate that SARS improves facial detail and face-body continuity while maintaining competitive full-body reconstruction accuracy. Our code is available at https://github.com/gulraizk94/SARS_Integration .