Thirty days grown seedlings of eight genotypes of groundnut viz. RHRG-6083, TAG-24, TG-60(LC), Karad-4-11, SB-XI, RHRG-6097, RHRG-6021, and RHRG-6055 were subjected to moisture stress by withholding irrigation for 15 days in pot culture experiment in order to evaluate its effect of on RWC , proline accumulation, soluble proteins, chlorophyll content and activities of superoxide dismutase (SOD), peroxidase (POX), glutathione reductase (GR) and lipid peroxidation (MDA).The levels of proline, soluble protein and the activity of all four antioxidative enzymes were found to be increased with MDA content in all genotypes of groundnut during water stress, however the chlorophyll and RWC content were found to be decreased. This study revealed that under water stress condition SB-XI, TAG-24 and RHRG-6021 showed higher increase of proline content and activities of antioxidative enzymes with lower reduction in RWC and chlorophyll. These three genotypes are stable during stress and seems to be promising for drought tolerance.

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Within a unified framework, we handle human image synthesis, including human motion imitation, appearance transfer, and new view synthesis. It indicates that after the model has been trained, it can do all of these jobs. To estimate the human body structure, existing task-specific techniques mostly employ 2D key-points (position). However, they can only represent location data and have no ability to define the person's unique shape or simulate limb rotations. To untangle the position and form, we suggest using a 3D body mesh recovery module in this study. It may define the customized body form as well as model joint placement and rotation. We present a Liquid Warping GAN technique that propagates source information in both image and feature spaces to the synthesized reference in order to retain source information such as texture, style, colour, and face identity. A denoising convolutional auto-encoder extracts the source characteristics in order to accurately characterize the source identity. In addition, our approach allows for more flexible warping from many sources. A one/few-shot adversarial learning is used to increase the generalization capacity of the unseen source pictures. In particular, it begins by putting a model through a rigorous training process. The model is then fine-tuned in a self-supervised manner by using one/few unseen images to create high-resolution (512x512 and 1024x1024) outputs. In addition, we created the imitation dataset to assess human motion imitation and unique view synthesis. Extensive testing has shown that our approaches work better in retaining facial identification, form consistency, and outfit details. Keywords: Human Image Synthesis, Motion Imitation, Novel View Synthesis, Generative Adversarial Network