Vanessa H. Tan

Music’s role in games and animation, particularly in dance content, is essential for creating immersive experiences. Although recent studies have made strides in generating dance music from videos, their practicality in integrating music into games and animation remains limited. In this context, we present a method capable of generating plausible dance music from 3D motion data and genre labels. Our approach leverages a combination of a UNET based latent diffusion model and a pre-trained VAE model. To evaluate our model’s performance, we employed metrics that assess various audio properties, including beat alignment, audio quality, motion-music correlation, and genre score. Quantitative results show that our approach is better than previous methods. Furthermore, we demonstrated that our model can generate audio that seamlessly fits to in-the-wild motion data. This capability enables us to create plausible dance music that complements the dynamic movements of characters and enhances the overall audiovisual experience in interactive media.

As earth observation satellites, Diwata microsatellites need to have a high degree of target pointing accuracy. Additionally, being in low orbit, they could experience strong external disturbances. Current methods for attitude control have proven to be effective. However, they are prone to changes in control and mass parameters. In this paper, we explore using Deep Reinforcement Learning (RL) for attitude control. This paper also leverages on Diwata’s simulator, MATA: Mission, Attitude, and Telemetry Analysis (MATA) software, in training the RL agent. We implemented two RL algorithms: Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC). We then simulated different scenarios and compared the performance of these algorithms to that of Diwata’s current attitude controller, the Proportional-Integral-Derivative (PID) control. Our results show that reinforcement learning can outperform traditional controllers in terms of settling time, overshoot, and stability. The results of this research will help solve problems in conventional attitude controllers and enable satellite engineers to design a better Attitude Determination and Control System (ADCS).

ANEEME focuses on building technologies that rapidly synthesize animated visual scenes. This virtual reality experience immerses users to different styles and culture of houses around the world. There are two modes in this application: build mode and play mode. In build mode, users can integrate 3D models from the local computer or from ANEEME’s online repository into the virtual environment. Using ANEEME’s automatic skeletal rigging, users can easily incorporate and animate humanoid objects such as toys, figurines, and even their own avatar. These enable users to design, build and customize their dream house. During play mode, users can interact with the objects inside the environment. They can watch videos, listen to music, play with different instruments, and many more. Photo and video capture capabilities available in this mode also allow users to easily share their virtual environment through their social media accounts.