  In this paper, we present a novel method for learning to synthesize 3D mesh
animation sequences with long short-term memory (LSTM) blocks and mesh-based
convolutional neural networks (CNNs). Synthesizing realistic 3D mesh animation
sequences is a challenging and important task in computer animation. To achieve
this, researchers have long been focusing on shape analysis to develop new
interpolation and extrapolation techniques. However, such techniques have
limited learning capabilities and therefore can produce unrealistic animation.
Deep architectures that operate directly on mesh sequences remain unexplored,
due to the following major barriers: meshes with irregular triangles, sequences
containing rich temporal information and flexible deformations. To address
these, we utilize convolutional neural networks defined on triangular meshes
along with a shape deformation representation to extract useful features,
followed by LSTM cells that iteratively process the features. To allow
completion of a missing mesh sequence from given endpoints, we propose a new
weight-shared bidirectional structure. The bidirectional generation loss also
helps mitigate error accumulation over iterations. Benefiting from all these
technical advances, our approach outperforms existing methods in sequence
prediction and completion both qualitatively and quantitatively. Moreover, this
network can also generate follow-up frames conditioned on initial shapes and
improve the accuracy as more bootstrap models are provided, which other works
in the geometry processing domain cannot achieve.
