Given an arbitrary speech clip and a facial image, talking face generation aims to synthesize a talking face video with precise lip synchronization as well as a smooth transition of facial motion over the entire video speech. Most existing methods mainly focus on either disentangling the information in a single image or learning temporal information between frames. However, speech audio and video often have cross-modality coherence that has not been well addressed during synthesis. Therefore, this paper proposes a novel high-resolution talking face generation model for arbitrary person by discovering the cross-modality coherence via Mutual Information Approximation (MIA). By assuming the modality difference between audio and video is larger that of real video and generated video, we estimate mutual information between real audio and video, and then use a discriminator to enforce generated video distribution approach real video distribution. Furthermore, we introduce a dynamic attention technique on the mouth to enhance the robustness during the training stage. Experimental results on benchmark dataset LRW transcend the state-of-the-art methods on prevalent metrics with robustness on gender, pose variations and high-resolution synthesizing.
One of the pioneers is to calculate the relative frequencies on appropriate partitions to approximate mutual information @cite_6 . propose a popular KNN-based estimator modified from the entropy estimator . Recent works try to employ parameters-free approaches , or rely on approximate Gaussianity of data distribution to estimate the mutual information. In order to reduce the bias and preserve the variance, @cite_22 propose to estimate the entropy or divergence by ensembling some simple plug-in estimators with varying neighborhood sizes.
@cite_22 @cite_6
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We demonstrate that it is possible to approximate the mutual information arbitrarily closely in probability by calculating the relative frequencies on appropriate partitions and achieving conditional independence on the rectangles of which the partitions are made. Empirical results, including a comparison with maximum-likelihood estimators, are presented. We present some new results on the nonparametric estimation of entropy and mutual information. First, we use an exact local expansion of the entropy function to prove almost sure consistency and central limit theorems for three of the most commonly used discretized information estimators. The setup is related to Grenander's method of sieves and places no assumptions on the underlying probability measure generating the data. Second, we prove a converse to these consistency theorems, demonstrating that a misapplication of the most common estimation techniques leads to an arbitrarily poor estimate of the true information, even given unlimited data. This "inconsistency" theorem leads to an analytical approximation of the bias, valid in surprisingly small sample regimes and more accurate than the usual 1 N formula of Miller and Madow over a large region of parameter space. The two most practical implications of these results are negative: (1) information estimates in a certain data regime are likely contaminated by bias, even if "bias-corrected" estimators are used, and (2) confidence intervals calculated by standard techniques drastically underestimate the error of the most common estimation methods.Finally, we note a very useful connection between the bias of entropy estimators and a certain polynomial approximation problem. By casting bias calculation problems in this approximation theory framework, we obtain the best possible generalization of known asymptotic bias results. More interesting, this framework leads to an estimator with some nice properties: the estimator comes equipped with rigorous bounds on the maximum error over all possible underlying probability distributions, and this maximum error turns out to be surprisingly small. We demonstrate the application of this new estimator on both real and simulated data. We analyze the estimation of information theoretic measures of continuous random variables such as: differential entropy, mutual information or Kullback-Leibler divergence. The objective of this paper is two-fold. First, we prove that the information theoretic measure estimates using the k-nearest-neighbor density estimation with fixed k converge almost surely, even though the k-nearest-neighbor density estimation with fixed k does not converge to its true measure. Second, we show that the information theoretic measure estimates do not converge for k growing linearly with the number of samples. Nevertheless, these nonconvergent estimates can be used for solving the two-sample problem and assessing if two random variables are independent. We show that the two-sample and independence tests based on these nonconvergent estimates compare favorably with the maximum mean discrepancy test and the Hilbert Schmidt independence criterion. A new universal estimator of divergence is presented for multidimensional continuous densities based on k-nearest-neighbor (k-NN) distances. Assuming independent and identically distributed (i.i.d.) samples, the new estimator is proved to be asymptotically unbiased and mean-square consistent. In experiments with high-dimensional data, the k-NN approach generally exhibits faster convergence than previous algorithms. It is also shown that the speed of convergence of the k-NN method can be further improved by an adaptive choice of k.