We present a one-shot method to infer and render a photorealistic 3D representation from a
single unposed image (eg, face portrait) in real-time. Given a single RGB input, our image …

A key challenge for novel view synthesis of monocular portrait images is 3D consistency
under continuous pose variations. Most existing methods rely on 2D generative models …

Previous portrait image generation methods roughly fall into two categories: 2D GANs and
3D-aware GANs. 2D GANs can generate high fidelity portraits but with low view consistency …

Although neural radiance fields (NeRF) have shown impressive advances in novel view
synthesis, most methods require multiple input images of the same scene with accurate …

We present NeX, a new approach to novel view synthesis based on enhancements of
multiplane image (MPI) that can reproduce next-level view-dependent effects--in real time …

Human portraits exhibit various appearances when observed from different views under
different lighting conditions. We can easily imagine how the face will look like in another …

The recently proposed neural radiance fields (NeRF) use a continuous function formulated
as a multi-layer perceptron (MLP) to model the appearance and geometry of a 3D scene …

Novel view synthesis is a challenging and ill-posed inverse rendering problem. Neural
rendering techniques have recently achieved photorealistic image quality for this task. State …

Abstract We present Depth-aware Image-based NEural Radiance fields (DINER). Given a
sparse set of RGB input views, we predict depth and feature maps to guide the …

Abstract Neural Radiance Fields (NeRF) have emerged as a powerful representation for the
task of novel view synthesis due to their simplicity and state-of-the-art performance. Though …