Ballroom dancing is a structured yet expressive motion category. Its highly diverse movement and complex interactions between leader and follower dancers make the understanding and synthesis challenging. We demonstrate that the three-point trajectory …
We introduce a neural motion synthesis approach that uses accessible pose data to generate plausible character motions by transferring motion from existing motion capture datasets. Our method effectively combines motion features from the source …
We introduce a novel approach to learn a common phase manifold from motion datasets across different characters, such as human and dog, using vector quantized periodic autoencoders. This manifold clusters semantically similar motions into the same …
The emergence of neural networks revolutionized motion synthesis, yet synthesizing diverse motions remains challenging. We present MoDi, an unsupervised generative model trained on a diverse, unstructured, unlabeled dataset, capable of synthesizing …