Body Surface Potential Map (BSPM) is an augmented version of 12-lead Electrocardiogram (ECG) with an increased number of electrodes that provides high density spatial information of the cardiac potential on the torso surface for source localization of cardiac abnormalities. A total reconstruction of BSPM is a challenging task. In this paper, we propose a novel Generative Adversarial Network (GAN) architecture to reconstruct 65-lead BSP from standard 12-lead ECG. We present Time-Series GAN (TSGAN), a specially designed modified pix2pix GAN for an accurate reconstruction of time-series BSP data. Further, we propose certain regularization terms in the generator loss function to preserve the key morphological properties of the generated waveform which is a major contribution of this work. The proposed architecture outperforms a Variational Autoencoder (VAE) and a baseline GAN on publicly available dataset in reconstructing 65-lead BSP with morphological preservation.