Search Results for author:
Found 3 papers, 2 papers with code
ParCo: Part-Coordinating Text-to-Motion Synthesis
However, these methods encounter challenges such as the lack of coordination between different part motions and difficulties for networks to understand part concepts.
Ranked #7 on
Motion Synthesis
on HumanML3D
ILSGAN: Independent Layer Synthesis for Unsupervised Foreground-Background Segmentation
Unsupervised foreground-background segmentation aims at extracting salient objects from cluttered backgrounds, where Generative Adversarial Network (GAN) approaches, especially layered GANs, show great promise.
Learning Foreground-Background Segmentation from Improved Layered GANs
Deep learning approaches heavily rely on high-quality human supervision which is nonetheless expensive, time-consuming, and error-prone, especially for image segmentation task.
