We introduce a simple and effective deep learning approach to automatically generate natural looking speech animation that synchronizes to input speech.
We create a solution for multi-user interactions in AR/MR, where a group can share the same augmented environment with any computer generated (CG) asset and interact in a shared story sequence through a third-person POV.
The proposed one-shot learning achieves performance that is competitive with supervised methods but uses only a single example rather than the hundreds required for the fully supervised case.
We propose a weakly-supervised approach that takes image-sentence pairs as input and learns to visually ground (i.e., localize) arbitrary linguistic phrases, in the form of spatial attention masks.
In this paper, we study non-linear tensor factorization methods based on deep variational autoencoders.
We present a novel computational approach to optimizing the morphological design of robots.
In this paper, we envision a robot that collaborates with a child to create oral stories in a highly interactive manner.
We performed three studies to examine the effects of accurate program response times, repeating unanswered questions, and providing feedback on the children’s likelihood of response.
In this work, we investigate how a robot can use information from prior conversations with the same child to foster a sense of relationship over time.
Our experiments illustrate the merits of the proposed approach in challenging re-identification scenarios including crowded public spaces.
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