5 accepted papers
Generation of 3D human motion holds significant importance in the creative industry. While recent notable advances have been made in generating common motions, existing methods struggle to generate diverse and rare motions due to the complexity of motions and limited training data. This work introdu…
"With the release of large-scale motion datasets with textual annotations, the task of establishing a robust latent space for language and 3D human motion has recently witnessed a surge of interest. Methods have been proposed to convert human motion and texts into features to achieve accurate corres…
To build a cross-modal latent space between 3D human motion and language acquiring large-scale and high-quality human motion data is crucial. However unlike the abundance of image data the scarcity of motion data has limited the performance of existing motion-language models. To counter this we intr…
This research tackles the problem of generating interaction between two human actors corresponding to textual description. We claim that certain interactions, which we call asymmetric interactions, involve a relationship between an actor and a receiver, whose motions significantly differ depending o…
This paper addresses the generation of referring expressions that not only refer to objects correctly but also let humans find them quickly. As a target becomes relatively less salient, identifying referred objects itself becomes more difficult. However, the existing studies regarded all sentences t…