HeadArtist: Text-conditioned 3D Head Generation with Self Score
  Distillation
Hongyu Liu Xuan Wang Ziyu Wan Yujun Shen Yibing Song Jing Liao Qifeng Chen
This work presents HeadArtist for 3D head generation from text descriptions.
With a landmark-guided ControlNet serving as the generative prior, we come up
with an efficient pipeline that optimizes a parameterized 3D head model under
the supervision of the prior distillation itself. We call such a process self
score distillation (SSD). In detail, given a sampled camera pose, we first
render an image and its corresponding landmarks from the head model, and add
some particular level of noise onto the image. The noisy image, landmarks, and
text condition are then fed into the frozen ControlNet twice for noise
prediction. Two different classifier-free guidance (CFG) weights are applied
during these two predictions, and the prediction difference offers a direction
on how the rendered image can better match the text of interest. Experimental
results suggest that our approach delivers high-quality 3D head sculptures with
adequate geometry and photorealistic appearance, significantly outperforming
state-ofthe-art methods. We also show that the same pipeline well supports
editing the generated heads, including both geometry deformation and appearance
change.