Generating dynamic three-dimensional (3D) object from a single-view video is
challenging due to the lack of 4D labeled data. Existing methods extend
text-to-3D pipelines by transferring off-the-shelf image generation models such
as score distillation sampling, but they are slow and expensive to scale (e.g.,
150 minutes per object) due to the need for back-propagating the
information-limited supervision signals through a large pretrained model. To
address this limitation, 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 train a novel 4D Gaussian splatting model with explicit point cloud
geometry, enabling real-time rendering under continuous camera trajectories.
Extensive experiments on synthetic and real videos show that Efficient4D offers
a remarkable 10-fold increase in speed when compared to prior art alternatives
while preserving the same level of innovative view synthesis quality. For
example, Efficient4D takes only 14 minutes to model a dynamic object.

提出了一种高效的视频到 4D 对象生成框架 Efficient4D，能够在连续的摄像机轨迹下实时渲染高质量的时空一致图像，通过直接训练具有明确点云几何结构的新颖 4D 高斯涂层模型，大大提升了速度，同时保持创新视角合成质量。