  Diffusion models have achieved remarkable success in generating realistic
images but suffer from generating accurate human hands, such as incorrect
finger counts or irregular shapes. This difficulty arises from the complex task
of learning the physical structure and pose of hands from training images,
which involves extensive deformations and occlusions. For correct hand
generation, our paper introduces a lightweight post-processing solution called
$\textbf{HandRefiner}$. HandRefiner employs a conditional inpainting approach
to rectify malformed hands while leaving other parts of the image untouched. We
leverage the hand mesh reconstruction model that consistently adheres to the
correct number of fingers and hand shape, while also being capable of fitting
the desired hand pose in the generated image. Given a generated failed image
due to malformed hands, we utilize ControlNet modules to re-inject such correct
hand information. Additionally, we uncover a phase transition phenomenon within
ControlNet as we vary the control strength. It enables us to take advantage of
more readily available synthetic data without suffering from the domain gap
between realistic and synthetic hands. Experiments demonstrate that HandRefiner
can significantly improve the generation quality quantitatively and
qualitatively. The code is available at
https://github.com/wenquanlu/HandRefiner .
