[Enhance] Update more models trained on Human-Art Dataset

[juxuan27]

Motivation

This PR is to add more models trained on Human-Art dataset into MMPose.

Modification

The modifications are listed as follows:

• Add configs of hrenet-w32/48 vitpose-h/l rtmpose-t trained on Human-Art in the folder "configs" as well as their markdown documents

BC-breaking (Optional)

It does not.

Use cases (Optional)

It is the same with MSCOCO. E.G. Users can simply convert configs/body_2d_keypoint/topdown_heatmap/coco/td-hm_hrnet-w48_8xb32-210e_coco-256x192.py to configs/body_2d_keypoint/topdown_heatmap/humanart/td-hm_hrnet-w48_8xb32-210e_humanart-256x192.py for training and testing on Human-Art.

Checklist

Before PR:

• I have read and followed the workflow indicated in the CONTRIBUTING.md to create this PR.
• Pre-commit or linting tools indicated in CONTRIBUTING.md are used to fix the potential lint issues.
• Bug fixes are covered by unit tests, the case that causes the bug should be added in the unit tests.
• New functionalities are covered by complete unit tests. If not, please add more unit tests to ensure correctness.
• The documentation has been modified accordingly, including docstring or example tutorials.

After PR:

• CLA has been signed and all committers have signed the CLA in this PR.

[codecov]

Codecov Report

Patch coverage has no change and project coverage change: +0.03 🎉

Comparison is base (a0ca076) 80.73% compared to head (6cb0029) 80.76%.

❗ Current head 6cb0029 differs from pull request most recent head 7053ae1. Consider uploading reports for the commit 7053ae1 to get more accurate results

Additional details and impacted files

@@             Coverage Diff             @@
##           dev-1.x    #2487      +/-   ##
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+ Coverage    80.73%   80.76%   +0.03%     
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  Files          251      251              
  Lines        15216    15216              
  Branches      2636     2636              
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+ Hits         12284    12289       +5     
+ Misses        2267     2262       -5     
  Partials       665      665              

Flag	Coverage Δ
unittests	80.76% <ø> (+0.03%)	⬆️

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see 1 file with indirect coverage changes

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