Disney Research Los Angeles

Anamorphosis for 2D displays can provide viewer centric perspective viewing, enabling 3D appearance, eye contact and engagement, by adapting dynamically in real time to a single moving viewer’s viewpoint, but at the cost of distorted viewing for other viewers. We present a method for constructing non-linear projections as a combination of anamorphic rendering of selective objects whilst reverting to normal perspective rendering of the rest of the scene. Our study defines a scene consisting of five characters, with one of these characters selectively rendered in anamorphic perspective.

In this paper, we propose recurrent neural network (RNN) based incremental processing towards the SLU task of intent detection. The proposed methodology offers lower latencies than a typical SLU system, without any significant reduction in system accuracy. We introduce and analyze different recurrent neural network architectures for incremental and online processing of the ASR transcripts and compare it to the existing offline systems.

Robots with kinematic loops are known to have superior mechanical performance. However, due to these loops, their modeling and control is challenging, and prevents a more widespread use. In this paper, we describe a versatile Inverse Kinematics (IK) formulation for the retargeting of expressive motions onto mechanical systems with loops.

We present a novel real-time face detail reconstruction method capable of recovering high quality geometry on consumer mobile devices.

Smiles and laughs have been the subject of many studies over the past decades, due to their frequent occurrence in interactions, as well as their social and emotional functions in dyadic conversations. In this paper we push forward previous work by providing a first study on the influence one interacting partner’s smiles and laughs have on their interlocutor’s, taking into account these expressions’ intensities. Our second contribution is a study on the patterns of laugh and smile sequences during the dialogs, again taking the intensity into account. Finally, we discuss the effect of the interlocutor’s role on smiling and laughing. In order to achieve this, we use a database of naturalistic dyadic conversations which was collected and annotated for the purpose of this study.

In order to achieve more believable interactions with artificial agents, there is a need to produce dialogue that is not only relevant, but also emotionally appropriate and consistent. This paper presents a comprehensive system that models the emotional state of users and an agent to dynamically adapt dialogue utterance selection.

This paper describes the development of a system for lifelike gaze in human-robot interactions using a humanoid animatronic bust. We present a general architecture that seeks not only to create gaze interactions from a technological standpoint, but also through the lens of character animation where the fidelity and believability of motion is paramount; that is, we seek to create an interaction which demonstrates the illusion of life.

Augmented reality devices enable new approaches for character animation, e.g., given that character posing is three dimensional in nature it follows that interfaces with higher degrees-of-freedom (DoF) should outperform 2D interfaces. We present PoseMMR, allowing Multiple users to animate characters in a Mixed Reality environment, like how a stop-motion animator would manipulate a physical puppet, frame-by-frame, to create the scene. We explore the potential advantages of the PoseMMR can facilitate immersive posing, animation editing, version control and collaboration, and provide a set of guidelines to foster the development of immersive technologies as tools for collaborative authoring of character animation.

In this paper we investigate the role of haptic feedback in human/robot handshaking by comparing different force controllers. The basic hypothesis is that in human handshaking force control there is a balance between an intrinsic (open--loop) and extrinsic (closed--loop) contribution. We use an underactuated anthropomorphic robotic hand, the Pisa/IIT hand, instrumented with a set of pressure sensors estimating the grip force applied by humans. In a first set of experiments we ask subjects to mimic a given force profile applied by the robot hand, to understand how human perceive and are able to reproduce a handshaking force.