  One image to editable dynamic 3D model and video generation is novel
direction and change in the research area of single image to 3D representation
or 3D reconstruction of image. Gaussian Splatting has demonstrated its
advantages in implicit 3D reconstruction, compared with the original Neural
Radiance Fields. As the rapid development of technologies and principles,
people tried to used the Stable Diffusion models to generate targeted models
with text instructions. However, using the normal implicit machine learning
methods is hard to gain the precise motions and actions control, further more,
it is difficult to generate a long content and semantic continuous 3D video. To
address this issue, we propose the OneTo3D, a method and theory to used one
single image to generate the editable 3D model and generate the targeted
semantic continuous time-unlimited 3D video. We used a normal basic Gaussian
Splatting model to generate the 3D model from a single image, which requires
less volume of video memory and computer calculation ability. Subsequently, we
designed an automatic generation and self-adaptive binding mechanism for the
object armature. Combined with the re-editable motions and actions analyzing
and controlling algorithm we proposed, we can achieve a better performance than
the SOTA projects in the area of building the 3D model precise motions and
actions control, and generating a stable semantic continuous time-unlimited 3D
video with the input text instructions. Here we will analyze the detailed
implementation methods and theories analyses. Relative comparisons and
conclusions will be presented. The project code is open source.
