  We present a method for learning 3D geometry and physics parameters of a
dynamic scene from only a monocular RGB video input. To decouple the learning
of underlying scene geometry from dynamic motion, we represent the scene as a
time-invariant signed distance function (SDF) which serves as a reference
frame, along with a time-conditioned deformation field. We further bridge this
neural geometry representation with a differentiable physics simulator by
designing a two-way conversion between the neural field and its corresponding
hexahedral mesh, enabling us to estimate physics parameters from the source
video by minimizing a cycle consistency loss. Our method also allows a user to
interactively edit 3D objects from the source video by modifying the recovered
hexahedral mesh, and propagating the operation back to the neural field
representation. Experiments show that our method achieves superior mesh and
video reconstruction of dynamic scenes compared to competing Neural Field
approaches, and we provide extensive examples which demonstrate its ability to
extract useful 3D representations from videos captured with consumer-grade
cameras.
