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 present the first non-linear 3DMMs using mesh

Keyphrases: 3d morphable models 2d images

Dataset description:
CelebA: CelebFaces Attributes dataset contains 202,599 face images of the size 178×218 from 10,177 celebrities, each annotated with 40 binary labels indicating facial attributes like hair color, gender and age.
Florence: The Florence 3D faces dataset consists of:   High-resolution 3D scans of human faces from many subjects. Several video sequences of varying resolution, conditions and zoom level for each subject.
DREAM: DREAM is a multiple-choice Dialogue-based REAding comprehension exaMination dataset. In contrast to existing reading comprehension datasets, DREAM is the first to focus on in-depth multi-turn multi-party dialogue understanding.
SUM: SUM is a new benchmark dataset of semantic urban meshes which covers about 4 km2 in Helsinki (Finland), with six classes: Ground, Vegetation, Building, Water, Vehicle, and Boat. The authors used Helsinki 3D textured meshes as input and annotated them as a benchmark dataset of semantic urban meshes.
Caltech-256: Caltech-256 is an object recognition dataset containing 30,607 real-world images, of different sizes, spanning 257 classes (256 object classes and an additional clutter class). Each class is represented by at least 80 images.

Options:- A: CelebA  B: Florence  C: DREAM  D: SUM  E: Caltech-256
["CelebA", "Florence"]