Data driven and learning based solutions for modeling dynamic garments have significantly advanced, especially in the context of digital humans. We model the dynamics of a garment by exploiting its local interactions with the underlying human body. …
We present Generative Motion Matching (GenMM), a generative model that "mines" as many diverse motions as possible from a single or few example sequences. GenMM is training-free and can synthesize a high-quality motion within a fraction of a second, …
We present GANimator, a generative model that learns to synthesize novel motions from a single, short motion sequence. GANimator generates motions that resemble the core elements of the original motion, while simultaneously synthesizing novel and …
We develop a neural technique for articulating 3D characters using enveloping with a pre-defined skeletal structure, which is essential for animating character with motion capture (mocap) data. Furthermore, we propose neural blend shapes -- a set of …
We introduce a novel deep learning framework for data-driven motion retargeting between skeletons, which may have different structure, yet corresponding to homeomorphic graphs. Importantly, our approach learns how to retarget without requiring any …