NeRF-Texture: Synthesizing Neural Radiance Field Textures.

Masked graph autoencoder (MGAE) has emerged as a promising self-supervised graph pre-training (SGP) paradigm due to its simplicity and effectiveness. However, existing efforts perform the mask-then-reconstruct operation in the raw data space as is done in computer vision (CV) and natural language processing (NLP) areas, while neglecting the important non-Euclidean property of graph data. As a result, the highly unstable local structures largely increase the uncertainty in inferring masked data and decrease the reliability of the exploited self-supervision signals, leading to inferior representations for downstream evaluations. To address this issue, we propose a novel SGP method termed Robust mAsked gRaph autoEncoder (RARE) to improve the certainty in inferring masked data and the reliability of the self-supervision mechanism by further masking and reconstructing node samples in the high-order latent feature space. Through both theoretical and empirical analyses, we have discovered that performing a joint mask-then-reconstruct strategy in both latent feature and raw data spaces could yield improved stability and performance. To this end, we elaborately design a masked latent feature completion scheme, which predicts latent features of masked nodes under the guidance of high-order sample correlations that are hard to be observed from the raw data perspective. Specifically, we first adopt a latent feature predictor to predict the masked latent features from the visible ones. Next, we encode the raw data of masked samples with a momentum graph encoder and subsequently employ the resulting representations to improve the predicted results through latent feature matching. Extensive experiments on seventeen datasets have demonstrated the effectiveness and robustness of RARE against state-of-the-art (SOTA) competitors across three downstream tasks. Our source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/WxTu/RARE</uri> .

An improved image texture synthesis based on algorithm convolution neural network

Anomaly detection in videos is challenging due to the scarcity and variance in positive samples. Current anomaly detection methods can be categorized into reconstruction models and future frame prediction-based models. However, reconstruction models might be exceptionally adapted to abnormal events due to the learning capacity and generalization ability of deep neural networks, whereas prediction-based methods can be sensitive to noise. In this study, we propose an anomaly detection model based on the latent feature space, which combines advantages from both sides. We argue that the constraints in the latent feature space can promote reconstruction; moreover, the optical flow is also considered to introduce temporal information. We use SPyNet for accurate and efficient optical flow estimation. We extensively validate our method on the UCSD Ped1, UCSD Ped2, CUHK Avenue, and ShanghaiTech datasets. The results demonstrated the feasibility of the proposed method and the benefit of utilizing information in the latent feature space.

Texture Mapping plays a very important role in Computer Graphics. Texture Synthesis is one of the main methods to obtain textures, it makes use of sample textures to generate new textures. Texture Transfer is based on Texture Synthesis, it renders objects with textures taken from different objects. Currently, most of Texture Synthesis and Transfer methods use a single sample texture. A method for Texture Synthesis adn Transfer from multi samples was presented. For texture synthesis, the L-shaped neighborhood seaching approach was used. Users specify the proportion of each sample, the number of seed points, and these seed points are scattered randomly according to their samples in horizontal and vertical direction synchronously to synthesize textures. The synthesized textures are very good. For texture transfer, the luminance of the target image and the sample textures are analyzed. This procedure is from coarse to fine, and can produce a visually pleasing result.

Deep expectation-maximization network for unsupervised image segmentation and clustering

Computer graphics applications often use textures to decorate virtual objects without modeling geometric details. These textures can be seamlessly generated from sample textures using texture synthesis algorithms. However, most existing texture synthesis algorithms are designed for rectangular domains and cannot be easily extended to general surfaces. In this paper, we present a novel method for texture synthesis and texture growing on triangle mesh with arbitrary topology. Using the sampled texture patches as the building blocks for texture synthesis, the visually seamless texture can be synthesized for a wide variety of textures ranging from regular to stochastic. The synthesizing process generates an initial texture block from the input texture patches and then iteratively searches for all texture blocks from the sampled texture patches to select a patch which optimizes the smoothness of the overlapping boundaries of the synthesized texture. It is showed that the proposed scheme is effective and efficient for synthesizing and growing of a wide range of textures on surfaces.

In the research of virtual hepatic surgery, the synthesis of hepatic texture space and the texture mapping of the hepatic body model are inseparable. Different methods for hepatic volume texture on the authenticity and mapping speed tend to have much difference. Based on computer unified device architecture (CUDA), this paper tries to present a texture synthesis and mapping method for the accelerating of hepatic body, which is used to solve the problem that traditional method under the hepatic volumetric texture synthesis and mapping process cost too much time. This new method may eliminate the traditional serial correlation of texture synthesis. What’s more, it may improve the efficiency of texture synthesis when the multithreading parallel processing for synthesis in the hepatic volumetric texture space in selecting and distributing blocks based on CUDA architecture.

Motivated by the increasing need for fast distributed processing of large-scale graphs such as the Web graph and various social networks, we study a number of fundamental graph problems in the message-passing model, where we have k machines that jointly perform computation on an arbitrary n-node (typically, n > k) input graph. The graph is assumed to be randomly partitioned among the k ≥ 2 machines (a common implementation in many real world systems). The communication is point-to-point, and the goal is to minimize the time complexity i.e., the number of communication rounds, of solving various fundamental graph problems.We present lower bounds that quantify the fundamental time limitations of distributively solving graph problems. We first show a lower bound of Ω(n/k) rounds for computing a spanning tree (ST) of the input graph. This result also implies the same bound for other fundamental problems such as computing a minimum spanning tree (MST), breadth-first tree (BFS), and shortest paths tree (SPT). We also show an Ω(n/k2) lower bound for connectivity ST verification and other related problems. Our lower bounds develop and use new bounds in random-partition communication complexity.To complement our lower bounds, we also give algorithms for various fundamental graph problems, e.g., PageRank, MST, connectivity, ST verification, shortest paths, cuts, spanners, covering problems, densest subgraph, subgraph isomorphism, finding triangles, etc. We show that problems such as PageRank, MST, connectivity, and graph covering can be solved in O(n/k) time (the notation O hides polylog(n) factors and an additive polylog(n) term); this shows that one can achieve almost linear (in k) speedup, whereas for shortest paths, we present algorithms that run in O(n/Ok) time (for (1 + e)-factor approximation) and in O(n/k) time (for O(log n)-factor approximation) respectively.Our results are a step towards understanding the complexity of distributively solving large-scale graph problems.

Anchor link prediction exacerbates the risk of privacy leakage via the de-anonymization of social network data. Embedding-based methods for anchor link prediction are limited by the excessive similarity of the associated nodes in a latent feature space and the variation between latent feature spaces caused by the semantics of different networks. In this article, we propose a novel method which reduces the impact of semantic discrepancies between different networks in the latent feature space. The proposed method consists of two phases. First, graph embedding focuses on the network structural roles of nodes and increases the distinction between the associated nodes in the embedding space. Second, a federated adversarial learning framework which performs graph embedding on each social network and an adversarial learning model on the server according to the observable anchor links is used to associate independent graph embedding approaches on different social networks. The combination of distinction enhancement and the association of graph embedding approaches alleviates variance between the latent feature spaces caused by the semantics of different social networks. Extensive experiments on real social networks demonstrate that the proposed method significantly outperforms the state-of-the-art methods in terms of both precision and robustness.

Fingerprinting approach based on a radio map is one of the well-received methods for indoor localization. However, owing to the radio devices reconfigured or deteriorated, the radio map will be degraded, rendering the positioning outcomes unreliable. We introduce an unsupervised anomaly detection model, termed Radio Map Anomaly Detection for Fingerprint Indoor Positioning with GAN (RAD-GAN), which trains only the normal RSSI (Received Signal Strength Indication) samples to learn the normality distribution of the radio signal in the spatial indoor domain. We exploit an encoder-decoder-encoder neural network for latent feature extraction. Afterward, an adversarial training scheme is employed to minimize the reconstruction error both within RSSI space and latent feature space. The higher reconstruction deviation of the RSSI vector is indicative of an anomaly from a normal distribution. We evaluate RAD-GAN in a BLE (Bluetooth Low Energy) signal environment with the power gain actively modified and the public UJI long-term Wi-Fi fingerprinting dataset. Experimental results show that RAD-GAN is more sensitive and robust than the former state-of-the-art models.

Sample based texture synthesis allows the generation of arbitrarily sized textures based on a small sample image. One main drawback of the technique is that a new pixel or patch is selected only considering local information in the generated texture. Thus structural error can't assure to be avoided for structural textures. To accelerate texture synthesis process and to avoid structural mismatch in the output picture, we present in this paper a structural pattern analysis (SPA) algorithm and a texture synthesis method using the algorithm. The pattern analysis tool also quantizes the texture randomness in macro scope view. It can be used in other applications such as automatic texture classification and quality evaluation of synthesized texture. We illustrate our result with some examples.

We develop two different techniques to study volume mapping problem in Computer Graphics and Medical Imaging fields.The first one is to find a harmonic map from a 3 manifold to a 3D solid sphere and the second is a sphere carving algorithm which calculates the simplicial decomposition of volume adapted to surfaces.We derive the 3D harmonic energy equation and it can be easily extended to higher dimensions.We use a textrehedral mesh to represent the volume data.We demonstrate our method on various solid 3D models.We suggest that 3D harmonic mapping of volume can provide a canonical coordinate system for feature identification and registration for computer animation and medical imaging.

A wide range of problems in computer graphics and vision can be formulated as sparse least squares problems. For example, Laplacian mesh deformation, Least Squares Conformal Maps, Poisson image editing, and as-rigid-as-possible (ARAP) image warping involve solving a linear or non-linear sparse least squares problem. High performance is crucial in many of these applications for interactive user feedback. For these applications, we show that the matrices produced by factorization methods such as QR have a special structure: the off-diagonal blocks are low-rank. We leverage this property to produce a fast sparse approximate QR factorization, spaQR, for these matrices in near-linear time. In our benchmarks, spaQR shows up to 57% improvement over solving the normal equations using Cholesky and 63% improvement over a standard preconditioner with Conjugate Gradient Least Squares (CGLS).

Motivated by the increasing need for fast distributed processing of large-scale graphs such as the Web graph and various social networks, we study a message-passing distributed computing model for graph processing and present lower bounds and algorithms for several graph problems. This work is inspired by recent large-scale graph processing systems (e.g., Pregel and Giraph) which are designed based on the message-passing model of distributed computing. Our model consists of a point-to-point communication network of $k$ machines interconnected by bandwidth-restricted links. Communicating data between the machines is the costly operation (as opposed to local computation). The network is used to process an arbitrary $n$-node input graph (typically $n \gg k > 1$) that is randomly partitioned among the $k$ machines (a common implementation in many real world systems). Our goal is to study fundamental complexity bounds for solving graph problems in this model. We present techniques for obtaining lower bounds on the distributed time complexity. Our lower bounds develop and use new bounds in random-partition communication complexity. We first show a lower bound of $\Omega(n/k)$ rounds for computing a spanning tree (ST) of the input graph. This result also implies the same bound for other fundamental problems such as computing a minimum spanning tree (MST). We also show an $\Omega(n/k^2)$ lower bound for connectivity, ST verification and other related problems. We give algorithms for various fundamental graph problems in our model. We show that problems such as PageRank, MST, connectivity, and graph covering can be solved in $\tilde{O}(n/k)$ time, whereas for shortest paths, we present algorithms that run in $\tilde{O}(n/\sqrt{k})$ time (for $(1+\epsilon)$-factor approx.) and in $\tilde{O}(n/k)$ time (for $O(\log n)$-factor approx.) respectively.

Realistic facial synthesis is one of the most fundamental problems in computer graphics and one of the most difficult. Human lips synchronization of the faces is very important part of this. In this paper it solves the issue of how to make human lips synchronization in one of the most popular 3D modelling software - Autodesk Maya. It is described an automatic way to analyse voice, using Adobe after effects and how the result interacts to Maya, but this way is still in progress, so voice is analyzed and classified manually. Using programming language of Maya - MEL (Maya Embedded Language) the recognized phonemes are associated with mouth positions to provide visemes for computer animation of speech. Different mouth positions are created using blend shape deformers, that let you to deform a surface into the shapes of other surfaces. Lip animation is facilitated by activating facial muscles and the jaw on the given facial model created specially for animation. Highspeed natural-looking synchronized lips animation is achieved. It is planed to model the expressive visual features of expressive speech. Informal user testing suggests that the addition of detailed internal mouth structures, such as the tongue, would improve the phrase recognition.