## MagicPose: Realistic Human Poses and Facial Expressions Retargeting with Identity-aware Diffusion
Di Chang, Yichun Shi, Quankai Gao, Hongyi Xu, Jessica Fu, Guoxian Song, Qing Yan, Yizhe Zhu, Xiao Yang, Mohammad Soleymani
Keywords: 
ICML/2024/Proceedings/33301 - MagicPose: Realistic Human Poses and Facial Expressions Retargeting with Identity-aware Diffusion.pdf
Project URL: https://boese0601.github.io/magicdance/

### Implementation
_Given the documentation given by the authors on the method, how much time investment would it be to re-implement the method from scratch?_

[3]

The code base is very large, and especially the code for the actual method is hard to navigate. Also it contains almost no usable documentation.

### Data
_Given the data description in the documentation, how much effort take to either: Find the same dataset the authors used, or similar datasets and defend the comparability, or acquire one from scratch?_

[3]

(1/1)

The data is available, however, no desciption is provided.

### Configuration 
_Given the (hyper)parameters, including semantic parameters, of the method: How much effort would it take to acquire the algorithm configurations used for their results, and compare against their budgetary constraints?_

[7]

Hyperparemeters are mentioned in the paper, but I find it unclear which relates to their experiments.

### Experimental Procedure
_Given the experimental set-up of the work, how difficult is it to set up a new experiment, similar to those presented in the original work, with the same procedure?_

[2]

One study is a user study, and no measure of distribution is provided.

### Expertise
_How much effort would it take to acquire the expertise required to reproduce the work independently relying on the available documentation?_

[8]

The method requires significant knowledge of previous methods. 
