Points-to-3D: Bridging the Gap between Sparse Points and
  Shape-Controllable Text-to-3D Generation
Chaohui Yu Qiang Zhou Jingliang Li Zhe Zhang Zhibin Wang Fan Wang
Text-to-3D generation has recently garnered significant attention, fueled by
2D diffusion models trained on billions of image-text pairs. Existing methods
primarily rely on score distillation to leverage the 2D diffusion priors to
supervise the generation of 3D models, e.g., NeRF. However, score distillation
is prone to suffer the view inconsistency problem, and implicit NeRF modeling
can also lead to an arbitrary shape, thus leading to less realistic and
uncontrollable 3D generation. In this work, we propose a flexible framework of
Points-to-3D to bridge the gap between sparse yet freely available 3D points
and realistic shape-controllable 3D generation by distilling the knowledge from
both 2D and 3D diffusion models. The core idea of Points-to-3D is to introduce
controllable sparse 3D points to guide the text-to-3D generation. Specifically,
we use the sparse point cloud generated from the 3D diffusion model, Point-E,
as the geometric prior, conditioned on a single reference image. To better
utilize the sparse 3D points, we propose an efficient point cloud guidance loss
to adaptively drive the NeRF's geometry to align with the shape of the sparse
3D points. In addition to controlling the geometry, we propose to optimize the
NeRF for a more view-consistent appearance. To be specific, we perform score
distillation to the publicly available 2D image diffusion model ControlNet,
conditioned on text as well as depth map of the learned compact geometry.
Qualitative and quantitative comparisons demonstrate that Points-to-3D improves
view consistency and achieves good shape controllability for text-to-3D
generation. Points-to-3D provides users with a new way to improve and control
text-to-3D generation.