Search Results for author:
Found 3 papers, 0 papers with code
Temporal Interpolation Is All You Need for Dynamic Neural Radiance Fields
Despite the simplicity of the model architectures, our method achieved state-of-the-art performance both in rendering quality for the neural representation and in training speed for the grid representation.
SinGRAF: Learning a 3D Generative Radiance Field for a Single Scene
Generative models have shown great promise in synthesizing photorealistic 3D objects, but they require large amounts of training data.
NPRportrait 1.0: A Three-Level Benchmark for Non-Photorealistic Rendering of Portraits
Despite the recent upsurge of activity in image-based non-photorealistic rendering (NPR), and in particular portrait image stylisation, due to the advent of neural style transfer, the state of performance evaluation in this field is limited, especially compared to the norms in the computer vision and machine learning communities.
