given that keypoints or regions are inadequately predicted

Note that differentiating the gradient of Lw.r.t to x requires a second-order derivative of the considered parametrized function and L-BFGSneeds to construct a third-order derivative approximation, which is challenging for neural networkswith ReLU units for which higher-order derivatives are discontinuous

These works make strong assumptions on the model architecture andmodel parameters that make reconstructions easier, but violate the threat model that we consider inthis work and lead to less realistic scenarios

anyinput to a fully connected layer can be reconstructed analytically independent ofthe remaining architecture

advanced talking head editing tasks, like pose-manipulation and background-replacement.

The final head pose in theoutput image is given by Rd Ð RuRd and td Ð tu `td. Invideo conferencing, we can change a person’s head pose inthe video stream freely despite the original view angle

entropy coding scheme

apply this processing to each frame in the driving video

we reuse xc,k, which wereextracted from the source image s. This is because theface in the output image must have the same identity as theone in the source image s.

3D keypoint decompositio

We note that theextracted keypoints are meant to be independent of the face’spose and expression. They shall only encode a person’sgeometry signature in a neutral pose and expression.

The Jacobian represents how a local patch around thekeypoint can be transformed into the corresponding patchin another image via an affine transformation

paramountimportance

roviding explicit control over the generated face froma pretrained StyleGAN

inverse mapping from images to latent codes is nontrivial

accessories

footage

ubject-dependent andsubject-agnostic models

Humans are able to guess the whole scene given a partialobservation of it. In a similar fashion, we aim to build a generator that trains with image patches, andinference images of unbounded arbitrary-large size.

inbetweening

latent space interpolations

The properties ofthe latent space

he latent factors of varia-tion.

linearity of the intermediate latent space

we further believe that our investigations to the separa-tion of high-level attributes and stochastic effects,

Multi-Head Attention

compatibility function

To facilitate these residual connections

auto-regressive

sequence of symbol representations (x1,...,xn) to a sequenceof continuous representations z = (z1,...,zn). Given z, the decoder then generates an outputsequence (y1,...,ym) of sy

counterac

albeit

This makesit more difficult to learn dependencies between distant position

ore parallelization

eschewing

sequence transductio

leverage

stray

approximations

from a collection of single-view 2D photographs

only a single source image

Deep generative models have had lessof an impact, due to the difficulty of approximating many intractable probabilistic computations thatarise in maximum likelihood estimation and related strategies, and due to difficulty of leveragingthe benefits of piecewise linear units in the generative context. We propose a new generative modelestimation procedure that sidesteps these difficulties.

unrolled approximate inference

Avatars

an unordered set generation problem

autoregressively

a set of rules

incorporating

view-dependent emitted radiance

urface element represen-tation

Point clouds are a simple representation thatalso supports arbitrary topology [21, 39, 77] and does notrequire data registration, but highly detailed geometry re-quires many points.

producing high-quality point clouds

To enable learn-ing, the choice of representation is the key

entry

A semantic position encoding mechanismis designed to facilitate semantic-level position information andpreserve the texture patterns in the exemplars

memory cos

comes at a price.

learning thistransformation completely without built-in priors and caneven learn to predict depth in an unsupervised fashion

To remedy these issues

Extrapolation

desired change in viewpoint.

a probabilistic formulation necessary to capture the ambi-guity inherent in predicting novel views from a single image,thereby overcoming the limitations of previous approachesthat are restricted to relatively small viewpoint changes

high fidelity.

fully automatic system

interpenetrate

Despite this progress, a significant limi-tation of these environments is that they do not contain peo-ple.

conditional variational autoencoder

placing 3D human bodies in 3D environmentsnaturally

affordance

the 3D scene structure and the proximitybetween the body and the scene are not explicitly modeled,especially for the regions that are occluded from the cam-era view, making it hard to effectively enforce constraints in3D, such as no inter-penetration and proper contact.

primal reason

their results are still far behindthe captured real human-scene interaction such as

generated proximal relation-ship

basis points

Best results except the ground truth areshown in boldface

purely geometry-

which however often leads toloss of fine spatial information