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Many graphics and vision problems can be expressed as non-linear least squares optimizations of objective functions over visual data, such as images and meshes. The mathematical descriptions of these
Face2Face: real-time face capture and reenactment of RGB videos
Face2Face addresses the under-constrained problem of facial identity recovery from monocular video by non-rigid model-based bundling and convincingly re-render the synthesized target face on top of the corresponding video stream such that it seamlessly blends with the real-world illumination.
Real-time 3D reconstruction at scale using voxel hashing
An online system for large and fine scale volumetric reconstruction based on a memory and speed efficient data structure that compresses space, and allows for real-time access and updates of implicit surface data, without the need for a regular or hierarchical grid data structure.
BundleFusion: real-time globally consistent 3D reconstruction using on-the-fly surface re-integration
This work systematically addresses issues with a novel, real-time, end-to-end reconstruction framework, which outperforms state-of-the-art online systems with quality on par to offline methods, but with unprecedented speed and scan completeness.
MoFA: Model-Based Deep Convolutional Face Autoencoder for Unsupervised Monocular Reconstruction
A novel model-based deep convolutional autoencoder that addresses the highly challenging problem of reconstructing a 3D human face from a single in-the-wild color image and can be trained end-to-end in an unsupervised manner, which renders training on very large real world data feasible.
Deferred Neural Rendering: Image Synthesis using Neural Textures
This work proposes Neural Textures, which are learned feature maps that are trained as part of the scene capture process that can be utilized to coherently re-render or manipulate existing video content in both static and dynamic environments at real-time rates.
VolumeDeform: Real-Time Volumetric Non-rigid Reconstruction
This work presents a novel approach for the reconstruction of dynamic geometric shapes using a single hand-held consumer-grade RGB-D sensor at real-time rates, and casts finding the optimal deformation of space as a non-linear regularized variational optimization problem by enforcing local smoothness and proximity to the input constraints.
DeepVoxels: Learning Persistent 3D Feature Embeddings
This work proposes DeepVoxels, a learned representation that encodes the view-dependent appearance of a 3D scene without having to explicitly model its geometry, based on a Cartesian 3D grid of persistent embedded features that learn to make use of the underlying3D scene structure.
Deep video portraits
The first to transfer the full 3D head position, head rotation, face expression, eye gaze, and eye blinking from a source actor to a portrait video of a target actor using only an input video is presented.
Self-Supervised Multi-level Face Model Learning for Monocular Reconstruction at Over 250 Hz
This first approach that jointly learns a regressor for face shape, expression, reflectance and illumination on the basis of a concurrently learned parametric face model is presented, which compares favorably to the state-of-the-art in terms of reconstruction quality, better generalizes to real world faces, and runs at over 250 Hz.