  Score distillation sampling~(SDS) has been widely adopted to overcome the
absence of unseen views in reconstructing 3D objects from a \textbf{single}
image. It leverages pre-trained 2D diffusion models as teacher to guide the
reconstruction of student 3D models. Despite their remarkable success,
SDS-based methods often encounter geometric artifacts and texture saturation.
We find out the crux is the overlooked indiscriminate treatment of diffusion
time-steps during optimization: it unreasonably treats the student-teacher
knowledge distillation to be equal at all time-steps and thus entangles
coarse-grained and fine-grained modeling. Therefore, we propose the Diffusion
Time-step Curriculum one-image-to-3D pipeline (DTC123), which involves both the
teacher and student models collaborating with the time-step curriculum in a
coarse-to-fine manner. Extensive experiments on NeRF4, RealFusion15, GSO and
Level50 benchmark demonstrate that DTC123 can produce multi-view consistent,
high-quality, and diverse 3D assets. Codes and more generation demos will be
released in https://github.com/yxymessi/DTC123.
