Numerous task-specific variants of autoregressive networks have been developed for dance generation. Nonetheless, a severe limitation remains in that all existing algorithms can return repeated patterns for a given initial pose, which may be inferior. We examine and analyze several key challenges of previous works, and propose variations in both model architecture (namely MNET++) and training methods to address these. In particular, we devise the beat synchronizer and dance synthesizer. First, generated dance should be locally and globally consistent with given music beats, circumvent repetitive patterns, and look realistic. To achieve this, the beat synchronizer implicitly catches the rhythm enabling it to stay in sync with the music as it dances. Then, the dance synthesizer infers the dance motions in a seamless patch-by-patch manner conditioned by music. Second, to generate diverse dance lines, adversarial learning is performed by leveraging the transformer architecture. Furthermore, MNET++ learns a dance genre-aware latent representation that is scalable for multiple domains to provide fine-grained user control according to the dance genre. Compared with the state-of-the-art methods, our method synthesizes plausible and diverse outputs according to multiple dance genres as well as generates remarkable dance sequences qualitatively and quantitatively.

Answering the question “what is the most preferable view of a three-dimensional (3-D) human model?” is a challenge in computer vision, computer graphics, and cinematography applications because the appearance of a human, for a given pose, relies on the viewpoint of the user. Currently, to the best of the authors’ knowledge, solid research on the most preferable viewing angle for obtaining numerical subjective evaluation scores has not been conducted. In this study, we investigate a metric that can be used to quantify the view of a 3-D human model, whose value is maximized at the most favorable camera angle in accordance with subjective assessments done by users. For an objective assessment in a numerical form, in this study, we define three view selection metrics: the 1)normalized limb length sum; 2)normalized area of a two-dimensional bounding box; and 3)normalized visible area of a 3-D bounding box. Finally, we formulate a viewpoint optimization problem whose objective function is the sum of the metrics. However, the objective function is nonconcave, and the solution set of the constraint is nonconvex. To overcome this difficulty, we employ decomposition and penalty methods. From the simulation results, it is verified that the average of the viewpoint selection error between the ground truth viewpoint and the optimal viewpoint obtained by the proposed algorithm is very close to the lower bound of the viewpoint selection error.

In this paper, we derive the closed forms of residual interference that occurs over a multipath channel of a filter bank multicarrier-quadrature amplitude modulation (FBMC-QAM) system, in which the system adopts two different prototype filters to guarantee that the orthogonality condition is met for both even and odd subcarriers without using a guard interval. The filter coefficients of the two filters are designed differently to meet the orthogonality condition, and thus, their degrees of robustness are different over the multipath channel. In order to clarify the influence of the multipath channel on the performance of the FBMC-QAM, we both derive an effective channel frequency response that includes the filter response and analyze the residual interference over the multipath channel. Based on this analysis, we propose a unique scheme for both even-biased reference symbol mapping and symbol/signal-level channel estimation and equalization that is suitable for the FBMC-QAM system transceiver structure. The simulation results show that the performance of the proposed scheme is comparable to that of a conventional orthogonal frequency division multiplexing-quadrature amplitude modulation system in terms of the mean squared error, bit error rate, and effective data rate.

In this paper, we propose a joint transmission and reception with phase control for beamforming in a multi-cell environment. For generated transmit weight vectors of multiple base stations (BSs), a mobile station (MS) calculates phases to maximize an achievable rate with low rate feedback. By using the phases, the multiple transmit weight vectors are coordinated to improve the signal to noise ratio (SNR). In order to find optimal phases, we present a phase control method for the effective channel via geometrical approach.

In this paper, we investigate efficient multi-cell cooperation based on CoMP-joint processing and transmission (CoMP-JPT) with orthogonal beamforming. Through the use of a combinatorial optimization algorithm, the optimal user scheduling for joint transmission using multiple transmitters is accomplished. The throughput of the CoMP-JPT can be significantly improved while maintaining fairness among users over a multi-cell environment.