Generalized Pose Space Embeddings for Training In-the-Wild using Anaylis-by-Synthesis

Modern pose estimation models are trained on large, manually-labelled datasets which are costly and may not cover the full extent of human poses and appearances in the real world. With advances in neural rendering, analysis-by-synthesis and the ability to not only predict, but also render the pose, is becoming an appealing framework, which could alleviate the need for large scale manual labelling efforts. While recent work have shown the feasibility of this approach, the predictions admit many flips due to a simplistic intermediate skeleton representation, resulting in low precision and inhibiting the acquisition of any downstream knowledge such as three-dimensional positioning. We solve this problem with a more expressive intermediate skeleton representation capable of capturing the semantics of the pose (left and right), which significantly reduces flips. To successfully train this new representation, we extend the analysis-by-synthesis framework with a training protocol based on synthetic data. We show that our representation results in less flips and more accurate predictions. Our approach outperforms previous models trained with analysis-by-synthesis on standard benchmarks.

Nonlinear Bipartite Output Regulation with Application to Turing Pattern

In this paper, a bipartite output regulation problem is solved for a class of nonlinear multi-agent systems subject to static signed communication networks. A nonlinear distributed observer is proposed for a nonlinear exosystem with cooperation-competition interactions to address the problem. Sufficient conditions are provided to guarantee its existence and stability. The exponential stability of the observer is established. As a practical application, a leader-following bipartite consensus problem is solved for a class of nonlinear multi-agent systems based on the observer. Finally, a network of multiple pendulum systems is treated to support the feasibility of the proposed design. The possible application of the approach to generate specific Turing patterns is also presented.