Abstract Neural Radiance Fields (NeRFs) have demonstrated amazing ability to synthesize
images of 3D scenes from novel views. However, they rely upon specialized volumetric …

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 …

We propose a neural inverse rendering pipeline called IRON that operates on photometric
images and outputs high-quality 3D content in the format of triangle meshes and material …

Abstract Neural Radiance Fields (NeRF) has recently gained popularity for its impressive
novel view synthesis ability. This paper studies the problem of hallucinated NeRF: ie …

Abstract We present Reference-guided Super-Resolution Neural Radiance Field (RefSR-
NeRF) that extends NeRF to super resolution and photorealistic novel view synthesis …

We present NeSF, a method for producing 3D semantic fields from posed RGB images
alone. In place of classical 3D representations, our method builds on recent work in implicit …

Neural radiance fields (NeRF) show great success in novel-view synthesis. However, in real-
world scenes, recovering high-quality details from the source images is still challenging for …

Coordinate-based neural networks parameterizing implicit surfaces have emerged as
efficient representations of geometry. They effectively act as parametric level sets with the …

We present a method to automatically compute correct gradients with respect to geometric
scene parameters in neural SDF renderers. Recent physically-based differentiable …

Discontinuous visibility changes at object boundaries remain a persistent source of difficulty
in the area of differentiable rendering. Left untreated, they bias computed gradients so …