目录
github地址：
依赖项：
xformers安装：
解决方法，安装xformers
smpl_data下载：
推理步骤：
SMPLDataset
香港科技大学提出了一种叫PSHuman的新框架。这个方法利用了一个多视角扩散模型的“先验知识”来更好地重建人物。如果直接用多视角扩散技术去处理单视角图像，结果往往会出现很大的几何扭曲，尤其是在生成的面部细节上。为了解决这一问题，设计了一种跨尺度扩散方法，这种方法能同时兼顾整体身体形状和局部面部特征的细节，生成的结果既细致又真实，没有几何上的畸变。
github地址：
GitHub - pengHTYX/PSHuman
模型下载地址：
https://huggingface.co/pengHTYX/PSHuman_Unclip_768_6views
依赖项：
pip install rembgpip install yacspip install pymeshlabpip install kaolin==0.17.0 -f https://nvidia-kaolin.s3.us-east-2.amazonaws.com/torch-2.5.1_cu121.html
pip install icecream
pip install torch_scatterxformers安装：
不更新会报错：
NotImplementedError: No operator found for `memory_efficient_attention_forward` with inputs:
解决方法，安装xformers
pip3 install -U xformers --index-url https://download.pytorch.org/whl/cu121
安装完了后，torch会自动更新为2.5.1
nvdiffrast安装：
https://github.com/NVlabs/nvdiffrast
下载下来，cd nvdiffrast
pip install .
'https://github.com/danielgatis/rembg/releases/download/v0.0.0/u2net.onnx' to file '/root/.u2net/u2net.onnx'.
omegaconf.errors.ConfigKeyError: Key ' ' not in 'TestConfig'
临时解决方法：
/mnt/pfs/users/lbg/envs/py310wj/lib/python3.10/site-packages/omegaconf/dictconfig.py
def _validate_get(self, key: Any, value: Any = None) -> None:is_typed = self._is_typed()if key==" ":return调用：
CUDA_VISIBLE_DEVICES=0 python inference.py --config configs/inference-768-6view.yaml \pretrained_model_name_or_path='/xxx/models/models--pengHTYX--PSHuman_Unclip_768_6views' \validation_dataset.crop_size=740 \with_smpl=false \validation_dataset.root_dir=examples \seed=600 \num_views=7 \save_mode='rgb'smpl_data下载：
huggingface-cli download --resume-download lilpotat/pytorch3d --local-dir lilpotat--pytorch3d
拷贝到目录：
smpl_related/smpl_data
推理步骤：
- Given a human image, we use Clipdrop or
rembgto remove the background. For the latter, we provide a simple scrip.
python utils/remove_bg.py --path $DATA_PATH$
Then, put the RGBA images in the $DATA_PATH$.
- By running inference.py, the textured mesh and rendered video will be saved in
out.
CUDA_VISIBLE_DEVICES=$GPU python inference.py --config configs/inference-768-6view.yaml \pretrained_model_name_or_path='pengHTYX/PSHuman_Unclip_768_6views' \validation_dataset.crop_size=740 \with_smpl=false \validation_dataset.root_dir=$DATA_PATH$ \seed=600 \num_views=7 \save_mode='rgb' SMPLDataset
from econdataset import SMPLDatasetimport os
import sys
import torch
os.chdir(os.path.dirname(os.path.abspath(__file__)))current_dir = os.path.dirname(os.path.abspath(__file__))paths = [os.path.abspath(__file__).split('scripts')[0]]
print('current_dir',current_dir)
paths.append(os.path.abspath(os.path.join(current_dir, './')))
paths.append(os.path.abspath(os.path.join(current_dir, '../')))for path in paths:sys.path.insert(0, path)os.environ['PYTHONPATH'] = (os.environ.get('PYTHONPATH', '') + ':' + path).strip(':')class Params:passvalidation_dataset=Params()
validation_dataset.root_dir='examples'# dataset_param = {'image_dir': validation_dataset.root_dir, 'seg_dir': None, 'colab': False, 'has_det': True, 'hps_type': 'pixie'}
dataset_param = {'image_dir': validation_dataset.root_dir, 'seg_dir': None, 'colab': False, 'has_det': True, 'hps_type': 'pixie'}
econdata = SMPLDataset(dataset_param, device='cuda')
