Avatars Grow Legs: Generating Smooth Human Motion from Sparse Tracking Inputs with Diffusion Model
Yuming Du, Robin Kips, Albert Pumarola, Sebastian Starke, Ali Thabet, Artsiom Sanakoyeu
Conference on Human Factors in Computing Systems (CHI)
We present a study to examine one’s learning and processing capacity of broadband tactile information, such as that derived from speech. In Study 1, we tested a user’s capability to recognize tactile locations and movements on the forearm in the presence of masking stimuli and determined 9 distinguishable tactile symbols. We associated these symbols to 9 phonemes using two approaches, random and articulation associations. Study 2 showed that novice participants can learn both associations. However, performance for retention, construction of words and knowledge transfer to recognize unlearned words was better with articulation association. In study 3, we trained novel participants to directly recognize words before learning phonemes. Our results show that novel users can retain and generalize the knowledge to recognize new words faster when they were directly train on words. Finally, Study 4 examined optimal presentation rate for the tactile symbols without compromising learning and recognition rate
Yuming Du, Robin Kips, Albert Pumarola, Sebastian Starke, Ali Thabet, Artsiom Sanakoyeu
Bilge Acun, Benjamin Lee, Fiodar Kazhamiaka, Kiwan Maeng, Manoj Chakkaravarthy, Udit Gupta, David Brooks, Carole-Jean Wu
Ilkan Esiyok, Pascal Berrang, Katriel Cohn-Gordon, Robert Künnemann