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: We develop a new method for 3D human body reconstruction from a monocular image

Keyphrases: 3d human body reconstruction images

Dataset description:
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.
Deep Fashion3D: A novel benchmark and dataset for the evaluation of image-based garment reconstruction systems. Deep Fashion3D contains 2078 models reconstructed from real garments, which covers 10 different categories and 563 garment instances.
AIST++: AIST++ is a 3D dance dataset which contains 3D motion reconstructed from real dancers paired with music. The AIST++ Dance Motion Dataset is constructed from the AIST Dance Video DB.
COCO: The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.
ExPose: Curates a dataset of SMPL-X fits on in-the-wild images.

Options:- A: MPII  B: Deep Fashion3D  C: AIST++  D: COCO  E: ExPose
["MPII", "COCO"]