You are provided with a research question, keyphrases about the question, a description of candidate datasets and dataset options. Read the description of popular datasets provided below and select the ones that can be used to validate the following research question. Use your knowledge of machine learning datasets to make the best judgement.
Your response should be formatted as a `json` array. For instance, for the query "Semi supervised image classification", a sample response might be: ["CIFAR-10", "CIFAR-100"]. Do not include any extra text in the response other than the answer array.

Query: Given a human image, I want to generate a human image with the target pose and the target

Keyphrases: human image generation

Dataset description:
Simitate: Simitate is a hybrid benchmarking suite targeting the evaluation of approaches for imitation learning. It consists on a dataset containing 1938 sequences where humans perform daily activities in a realistic environment.
MPII: The MPII Human Pose Dataset for single person pose estimation is composed of about 25K images of which 15K are training samples, 3K are validation samples and 7K are testing samples (which labels are withheld by the authors). The images are taken from YouTube videos covering 410 different human activities and the poses are manually annotated with up to 16 body joints.
LAMBADA: The LAMBADA (LAnguage Modeling Broadened to Account for Discourse Aspects) benchmark is an open-ended cloze task which consists of about 10,000 passages from BooksCorpus where a missing target word is predicted in the last sentence of each passage. The missing word is constrained to always be the last word of the last sentence and there are no candidate words to choose from.
DeepFashion: DeepFashion is a dataset containing around 800K diverse fashion images with their rich annotations (46 categories, 1,000 descriptive attributes, bounding boxes and landmark information) ranging from well-posed product images to real-world-like consumer photos.
Market-1501: Market-1501 is a large-scale public benchmark dataset for person re-identification. It contains 1501 identities which are captured by six different cameras, and 32,668 pedestrian image bounding-boxes obtained using the Deformable Part Models pedestrian detector.
BanglaLekhaImageCaptions: This dataset consists of images and annotations in Bengali. The images are human annotated in Bengali by two adult native Bengali speakers.

Options:- A: Simitate  B: MPII  C: LAMBADA  D: DeepFashion  E: Market-1501  F: BanglaLekhaImageCaptions
["DeepFashion", "Market-1501"]