  Generating dynamic 3D object from a single-view video is challenging due to
the lack of 4D labeled data. An intuitive approach is to extend previous
image-to-3D pipelines by transferring off-the-shelf image generation models
such as score distillation sampling.However, this approach would be slow and
expensive to scale due to the need for back-propagating the information-limited
supervision signals through a large pretrained model. To address this, we
propose an efficient video-to-4D object generation framework called
Efficient4D. It generates high-quality spacetime-consistent images under
different camera views, and then uses them as labeled data to directly
reconstruct the 4D content through a 4D Gaussian splatting model. Importantly,
our method can achieve real-time rendering under continuous camera
trajectories. To enable robust reconstruction under sparse views, we introduce
inconsistency-aware confidence-weighted loss design, along with a lightly
weighted score distillation loss. Extensive experiments on both synthetic and
real videos show that Efficient4D offers a remarkable 10-fold increase in speed
when compared to prior art alternatives while preserving the quality of novel
view synthesis. For example, Efficient4D takes only 10 minutes to model a
dynamic object, vs 120 minutes by the previous art model Consistent4D.
