We present a GAN-based Transformer for general action-conditioned 3D human motion generation, including not only single-person actions but also multi-person interactive actions. Our approach consists of a powerful Action-conditioned motion TransFormer (ActFormer) under a GAN training scheme, equipped with a Gaussian Process latent prior. Such a design combines the strong spatio-temporal representation capacity of Transformer, superiority in generative modeling of GAN, and inherent temporal correlations from the latent prior. Furthermore, ActFormer can be naturally extended to multi-person motions by alternately modeling temporal correlations and human interactions with Transformer encoders. To further facilitate research on multi-person motion generation, we introduce a new synthetic dataset of complex multi-person combat behaviors. Extensive experiments on NTU-13, NTU RGB+D 120, BABEL and the proposed combat dataset show that our method can adapt to various human motion representations and achieve superior performance over the state-of-the-art methods on both single-person and multi-person motion generation tasks, demonstrating a promising step towards a general human motion generator. The project website can be found at https://liangxuy.github.io/actformer/.

To deploy deep learning algorithms on resource-limited scenarios, an emerging device-resistive random access memory (ReRAM) has been regarded as promising via analog computing. However, the practicability of ReRAM is primarily limited due to the weight drifting of ReRAM neural networks due to multi-factor reasons, including manufacturing, thermal noises, and etc. In this paper, we propose a novel Bayesian optimization method for fault tolerant neural network architecture (BayesFT). For neural architecture search space design, instead of conducting neural architecture search on the whole feasible neural architecture search space, we first systematically explore the weight drifting tolerance of different neural network components, such as dropout, normalization, number of layers, and activation functions in which dropout is found to be able to improve the neural network robustness to weight drifting. Based on our analysis, we propose an efficient search space by only searching for dropout rates for each layer. Then, we use Bayesian optimization to search for the optimal neural architecture robust to weight drifting. Empirical experiments demonstrate that our algorithmic framework has outperformed the state-of-the-art methods by up to 10 times on various tasks, such as image classification and object detection.

Monocular depth estimation has gained increasing attention in recent years, and various techniques have been proposed to tackle this problem. In this work, we aim to provide a comprehensive study on the techniques widely used in monocular depth estimation, and examine their individual influence on the performance. More specifically, we provide a study on: 1) network architectures, including different combinations of encoders/decoders. 2) supervision losses, including fully supervised losses and self-supervised losses and 3) other practices such as input resolution. The experiments are conducted on two commonly used public datasets, KITTI and NYU Depth v2. We also provide an analysis on the errors produced by different models, to reveal the limitations of current methods. Furthermore, by a careful redesign, we present a model for depth estimation, which achieves competitive performance on KITTI and state-of-the-art performance on NYU Depth v2. Our code is publicly available at https://github.com/zenithfang/supervised_dispnet.