YANG , Di

Many attempts have been made towards combining RGB and 3D poses for the recognition of Activities of Daily Living (ADL). ADL may look very similar and often necessitate to model fine-grained details to distinguish them. Because the recent 3D ConvNets are too rigid to capture the subtle visual patterns across an action, this research direction...

Due to the remarkable progress of deep generative models, animating images has become increasingly efficient, whereas associated results have become increasingly realistic. Current animation-approaches commonly exploit structure representation extracted from driving videos. Such structure representation is instrumental in transferring motion...

Action recognition based on skeleton data has recently witnessed increasing attention and progress. State-of-the-art approaches adopting Graph Convolutional networks (GCNs) can effectively extract features on human skeletons relying on the pre-defined human topology. Despite associated progress, GCN-based methods have difficulties to generalize...

Action recognition based on human pose has witnessed increasing attention due to its robustness to changes in appearances, environments, and viewpoints. Despite associated progress, one remaining challenge has to do with occlusion in real-world videos that hinders the visibility of all joints. Such occlusion impedes representation of such...

Current self-supervised approaches for skeleton action representation learning often focus on constrained scenarios, where videos and skeleton data are recorded in laboratory settings. When dealing with estimated skeleton data in realworld videos, such methods perform poorly due to the large variations across subjects and camera viewpoints. To...

Video generation greatly benefits from integrating facial expressions, as they are highly pertinent in social interaction and hence increase realism in generated talking head videos. Motivated by this, we propose a method for editing emotions in head reenactment videos that is streamlined to modify the latent space of a pre-trained neural head...