Dance Dance ConvLSTM
\textit{Dance Dance Revolution} is a rhythm game consisting of songs and accompanying choreography, referred to as charts. Players press arrows on a device referred to as a dance pad in time with steps determined by the song's chart. In 2017, the authors of Dance Dance Convolution (DDC) developed an algorithm for the automatic generation of \textit{Dance Dance Revolution} charts, utilizing a CNN-LSTM architecture. We introduce Dance Dance ConvLSTM (DDCL), a new method for the automatic generation of DDR charts using a ConvLSTM based model, which improves upon the DDC methodology and substantially increases the accuracy of chart generation.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Alternating ConvLSTM: Learning Force Propagation with Alternate State Updates
Data-driven simulation is an important step-forward in computational physics when traditional numerical methods meet their limits. Learning-based simulators have been widely studied in past years; however, most previous …
A Comparative Analysis of Recurrent and Attention Architectures for Isolated Sign Language Recognition
This study presents a systematic comparative analysis of recurrent and attention-based neural architectures for isolated sign language recognition. We implement and evaluate two representative models-ConvLSTM and Vanilla…
Sign Language RecognitionComputational EfficiencyBenchmarking ConvLSTM for One-Day-Ahead IMDAA Rainfall-Field Prediction across Four Indian Cities
Convolutional long short-term memory networks (ConvLSTMs) are widely used for precipitation forecasting, but most evidence for their performance comes from dense, high-frequency radar sequences. This study tests whether …
Precipitation ForecastingLight Field Saliency Detection with Dual Local Graph Learning andReciprocative Guidance
The application of light field data in salient object de-tection is becoming increasingly popular recently. The diffi-culty lies in how to effectively fuse the features within the fo-cal stack and how to cooperate them w…
Graph LearningSaliency DetectionLight Field Saliency Detection With Dual Local Graph Learning and Reciprocative Guidance
The application of light field data in salient object detection is becoming increasingly popular in recent years. The difficulty lies in how to effectively fuse the features within the focal stack and how to cooperat…
AllGraph Learningobject-detectionObject Detection+3