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Found 8 papers, 4 papers with code
Learning 3D-Aware GANs from Unposed Images with Template Feature Field
Collecting accurate camera poses of training images has been shown to well serve the learning of 3D-aware generative adversarial networks (GANs) yet can be quite expensive in practice.
SpatialTracker: Tracking Any 2D Pixels in 3D Space
Recovering dense and long-range pixel motion in videos is a challenging problem.
PanopticNeRF-360: Panoramic 3D-to-2D Label Transfer in Urban Scenes
Moreover, PanopticNeRF-360 enables omnidirectional rendering of high-fidelity, multi-view and spatiotemporally consistent appearance, semantic and instance labels.
Dyn-E: Local Appearance Editing of Dynamic Neural Radiance Fields
We extensively evaluate our approach on various scenes and show that our approach achieves spatially and temporally consistent editing results.
Painting 3D Nature in 2D: View Synthesis of Natural Scenes from a Single Semantic Mask
We introduce a novel approach that takes a single semantic mask as input to synthesize multi-view consistent color images of natural scenes, trained with a collection of single images from the Internet.
Panoptic NeRF: 3D-to-2D Label Transfer for Panoptic Urban Scene Segmentation
In this work, we present a novel 3D-to-2D label transfer method, Panoptic NeRF, which aims for obtaining per-pixel 2D semantic and instance labels from easy-to-obtain coarse 3D bounding primitives.
Animatable Implicit Neural Representations for Creating Realistic Avatars from Videos
Some recent works have proposed to decompose a non-rigidly deforming scene into a canonical neural radiance field and a set of deformation fields that map observation-space points to the canonical space, thereby enabling them to learn the dynamic scene from images.
Animatable Neural Radiance Fields for Modeling Dynamic Human Bodies
Moreover, the learned blend weight fields can be combined with input skeletal motions to generate new deformation fields to animate the human model.
