Learning Bidirectional LSTM Networks for Synthesizing 3D Mesh Animation Sequences

Understand

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.