Reconstructing 3D human body shapes from a given set of textured partial scans, remains a fundamental task for many computer vision and graphics applications – e.g., body animation, virtual dressing. We propose a new neural network architecture for 3D body shape and high-resolution texture completion – TSCom-Net – that can reconstruct full geometry from mid-level to high-level of partial input scans. We decompose the overall reconstruction task into two stages – first, a joint implicit signed distance function (SDF) learning network (SCom-Net and TCom-Net) that takes a voxelized scan and its occupancy grid as input to reconstruct high fidelity body shape and predict vertex textures. Second, a high resolution texture completion network, that utilizes the predicted coarse vertex textures to inpaint the missing parts of the partial ‘texture atlas’. A thorough experimental evaluation on 3DBodyTex.V2 dataset shows that our method achieves competitive results with respect to the state-of-the-art while generalizing to different types and levels of partial shapes. The proposed method has also ranked second in the SHARP 2022 Challenge1-Track1.