  With advancements in deep model architectures, tasks in computer vision can
reach optimal convergence provided proper data preprocessing and model
parameter initialization. However, training on datasets with low
feature-richness for complex applications limit and detriment optimal
convergence below human performance. In past works, researchers have provided
external sources of complementary data at the cost of supplementary hardware,
which are fed in streams to counteract this limitation and boost performance.
We propose a generative multi-stream architecture, eliminating the need for
additional hardware with the intent to improve feature richness without risking
impracticability. We also introduce the compact spatio-temporal residual block
to the standard 3-dimensional convolutional model, C3D. Our rC3D model performs
comparatively to the top C3D residual variant architecture, the pseudo-3D
model, on the FASL-RGB dataset. Our methods have achieved 95.62% validation
accuracy with a variance of 1.42% from training, outperforming past models by
0.45% in validation accuracy and 5.53% in variance.
