Avatar Fingerprinting for Authorized Use of Synthetic Talking-Head Videos

Modern generators render talking-head videos with impressive levels of photorealism, ushering in new user experiences such as videoconferencing under constrained bandwidth budgets. Their safe adoption, however, requires a mechanism to verify if the rendered video is trustworthy. For instance, for videoconferencing we must identify cases in which a synthetic video portrait uses the appearance of an individual without their consent. We term this task avatar fingerprinting. We propose to tackle it by leveraging facial motion signatures unique to each person. Specifically, we learn an embedding in which the motion signatures of one identity are grouped together, and pushed away from those of other identities, regardless of the appearance in the synthetic video. Avatar fingerprinting algorithms will be critical as talking head generators become more ubiquitous, and yet no large scale datasets exist for this new task. Therefore, we contribute a large dataset of people delivering scripted and improvised short monologues, accompanied by synthetic videos in which we render videos of one person using the facial appearance of another. Project page: https://research.nvidia.com/labs/nxp/avatar-fingerprinting/.

Gaze-Sensing LEDs for Head Mounted Displays

We introduce a new gaze tracker for Head Mounted Displays (HMDs). We modify two off-the-shelf HMDs to be gaze-aware using Light Emitting Diodes (LEDs). Our key contribution is to exploit the sensing capability of LEDs to create low-power gaze tracker for virtual reality (VR) applications. This yields a simple approach using minimal hardware to achieve good accuracy and low latency using light-weight supervised Gaussian Process Regression (GPR) running on a mobile device. With our hardware, we show that Minkowski distance measure based GPR implementation outperforms the commonly used radial basis function-based support vector regression (SVR) without the need to precisely determine free parameters. We show that our gaze estimation method does not require complex dimension reduction techniques, feature extraction, or distortion corrections due to off-axis optical paths. We demonstrate two complete HMD prototypes with a sample eye-tracked application, and report on a series of subjective tests using our prototypes.

Toward Standardized Classification of Foveated Displays

Emergent in the field of head mounted display design is a desire to leverage the limitations of the human visual system to reduce the computation, communication, and display workload in power and form-factor constrained systems. Fundamental to this reduced workload is the ability to match display resolution to the acuity of the human visual system, along with a resulting need to follow the gaze of the eye as it moves, a process referred to as foveation. A display that moves its content along with the eye may be called a Foveated Display, though this term is also commonly used to describe displays with non-uniform resolution that attempt to mimic human visual acuity. We therefore recommend a definition for the term Foveated Display that accepts both of these interpretations. Furthermore, we include a simplified model for human visual Acuity Distribution Functions (ADFs) at various levels of visual acuity, across wide fields of view and propose comparison of this ADF with the Resolution Distribution Function of a foveated display for evaluation of its resolution at a particular gaze direction. We also provide a taxonomy to allow the field to meaningfully compare and contrast various aspects of foveated displays in a display and optical technology-agnostic manner.