Consistent4D: Consistent 360° Dynamic Object Generation from
  Monocular Video
Yanqin Jiang Li Zhang Jin Gao Weimin Hu Yao Yao
In this paper, we present Consistent4D, a novel approach for generating 4D
dynamic objects from uncalibrated monocular videos. Uniquely, we cast the
360-degree dynamic object reconstruction as a 4D generation problem,
eliminating the need for tedious multi-view data collection and camera
calibration. This is achieved by leveraging the object-level 3D-aware image
diffusion model as the primary supervision signal for training Dynamic Neural
Radiance Fields (DyNeRF). Specifically, we propose a Cascade DyNeRF to
facilitate stable convergence and temporal continuity under the supervision
signal which is discrete along the time axis. To achieve spatial and temporal
consistency, we further introduce an Interpolation-driven Consistency Loss. It
is optimized by minimizing the discrepancy between rendered frames from DyNeRF
and interpolated frames from a pre-trained video interpolation model. Extensive
experiments show that our Consistent4D can perform competitively to prior art
alternatives, opening up new possibilities for 4D dynamic object generation
from monocular videos, whilst also demonstrating advantage for conventional
text-to-3D generation tasks. Our project page is
https://consistent4d.github.io/.