Hao Li

TL;DR: A state-of-the-art regression-based facial tracking framework with segmented face images as training is adopted, and accurate and uninterrupted facial performance capture is demonstrated in the presence of extreme occlusion and even side views.

TL;DR: In this paper, an end-to-end and effective semi-supervised object detection framework called Instant-Teaching is proposed, which uses instant pseudo labeling with extended weak-strong data augmentations for teaching during each training iteration.

TL;DR: A novel shape completion technique for creating temporally coherent watertight surfaces from real-time captured dynamic performances that does not suffer error accumulation typically introduced by noise, large deformations, and drastic topological changes is presented.

TL;DR: In this paper, the authors focus on compressing and accelerating deep models with network weights represented by very small numbers of bits, referred to as extremely low bit neural network, and model this problem as a discretely constrained optimization problem.

Abstract: We present a real-time method for synthesizing highly complex human motions using a novel training regime we call the auto-conditioned Recurrent Neural Network (acRNN). Recently, researchers have attempted to synthesize new motion by using autoregressive techniques, but existing methods tend to freeze or diverge after a couple of seconds due to an accumulation of errors that are fed back into the network. Furthermore, such methods have only been shown to be reliable for relatively simple human motions, such as walking or running. In contrast, our approach can synthesize arbitrary motions with highly complex styles, including dances or martial arts in addition to locomotion. The acRNN is able to accomplish this by explicitly accommodating for autoregressive noise accumulation during training. Our work is the first to our knowledge that demonstrates the ability to generate over 18,000 continuous frames (300 seconds) of new complex human motion w.r.t. different styles.