MIMO: Controllable Character Video Synthesis with Spatial Decomposed
  Modeling
Authors: name: Yifang Men, name: Yuan Yao, name: Miaomiao Cui, name: Liefeng Bo

Character video synthesis aims to produce realistic videos of animatable
characters within lifelike scenes. As a fundamental problem in the computer
vision and graphics community, 3D works typically require multi-view captures
for per-case training, which severely limits their applicability of modeling
arbitrary characters in a short time. Recent 2D methods break this limitation
via pre-trained diffusion models, but they struggle for pose generality and
scene interaction. To this end, we propose MIMO, a novel framework which can
not only synthesize character videos with controllable attributes (i.e.,
character, motion and scene) provided by simple user inputs, but also
simultaneously achieve advanced scalability to arbitrary characters, generality
to novel 3D motions, and applicability to interactive real-world scenes in a
unified framework. The core idea is to encode the 2D video to compact spatial
codes, considering the inherent 3D nature of video occurrence. Concretely, we
lift the 2D frame pixels into 3D using monocular depth estimators, and
decompose the video clip to three spatial components (i.e., main human,
underlying scene, and floating occlusion) in hierarchical layers based on the
3D depth. These components are further encoded to canonical identity code,
structured motion code and full scene code, which are utilized as control
signals of synthesis process. The design of spatial decomposed modeling enables
flexible user control, complex motion expression, as well as 3D-aware synthesis
for scene interactions. Experimental results demonstrate effectiveness and
robustness of the proposed method.