Estimating 3D humans from images often produces implausible bodies that lean, float, or penetrate the floor. Such methods ignore fact are typically supported by scene. A physics engine can be used to enforce physical plausibility, but these not differentiable, rely on unrealistic proxy bodies, and difficult integrate into existing optimization learning frameworks. In contrast, we exploit novel intuitive-physics (IP) terms inferred a SMPL body interacting with Inspired biomechanics, infer...

Generating realistic 3D worlds occupied by moving humans has many applications in games, architecture, and synthetic data creation. But generating such scenes is expensive labor intensive. Recent work generates human poses motions given a scene. Here, we take the opposite approach generate indoor motion. Such can come from archival motion capture or IMU sensors worn on body, effectively turning movement into "scanner" of world. Intuitively, indicates free-space room contact surfaces objects...

The problem is to find a method predict the two-hour price of cryptocurrencies on basis Social Factors, which are increasingly used for online transactions worldwide. few previous methods proposed cryptocurrency inefficient because they fail take into consideration differences in attributes between real currencies and cryptocurrencies. In this paper, we focus two cryptocurrencies, namely Bitcoin Litecoin, each with large market size user base, attempt their future prices using multi-linear...

We address the problem of inferring 3D shape and pose dogs from images. Given lack training data, this is challenging, best methods lag behind those designed to estimate human pose. To make progress, we attack multiple sides at once. First, need a good prior, like available for humans. that end, learn dog-specific parametric model, called D-SMAL. Second, existing focus on in standing poses because when they sit or lie down, their legs are self occluded bodies deform. Without access prior an...

Addressing the trends of digitalization, decentralization, democratization, and decarbonization, local peer-to-peer (P2P) markets have potential to significantly accelerate decarbonization at communal level. However, due an increase in number energy consumers, such as electric vehicles or heat pumps, grid congestion can occur since actual low-voltage grids are not designed transmit large loads. This paper introduces a novel concept for platform combine advantages P2P trading with need...

Understanding how humans use physical contact to interact with the world is key enabling human-centric artificial intelligence. While inferring 3D crucial for modeling realistic and physically-plausible human-object interactions, existing methods either focus on 2D, consider body joints rather than surface, coarse regions, or do not generalize in-the-wild images. In contrast, we dense, between full surface objects in arbitrary To achieve this, first collect DAMON, a new dataset containing...

The energy sector is part of the critical infrastructure in modern society where security and privacy concerns customers this have been well studied addressed. In classical power grid, consumers are being supplied by a major grid operator who has government-sanctioned therefore to adhere guidelines. recent years, however, with shift usage renewable energies, new concepts for trading flexibilities emerged, that enable their own production sell spare back market. While reducing load from...

Generating realistic human motion is essential for many computer vision and graphics applications. The wide variety of body shapes sizes greatly impacts how people move. However, most existing models ignore these differences, relying on a standardized, average body. This leads to uniform across different types, where movements don't match their physical characteristics, limiting diversity. To solve this, we introduce new approach develop generative model based shape. We show that it's...

We focus on recovering 3D object pose and shape from single images. This is highly challenging due to strong (self-)occlusions, depth ambiguities, the enormous variance, lack of ground truth for natural Recent work relies mostly learning finite datasets, so it struggles generalizing, while focuses itself, largely ignoring alignment with pixels. Moreover, performs feed-forward inference, cannot refine estimates. tackle these limitations a novel framework, called SDFit. To this end, we make...

Potato Peel Waste (PPW) is a potential lignocellulosic biomass substrate for bioethanol production due to its high starch content and easy availability.In this study, we performed research optimization of fermentation process by Response Surface Methodology (RSM) using Plackett-Burman design.The herein included acid-base pre-treatment biomass, which was then followed enzymatic hydrolysis as step.The concentration reducing sugar in the hydrolysate thus obtained analyzed DNSA method.After...

<p>Prediction of financial markets is a challenging task due to its volatility and the presence noise. This work comparatively analyses implementations LSTM (Long Short Term Memory) three kernels SVR (Support Vector Regressor), namely linear kernel, RBF (Radial Basis Function) kernel polynomial kernel. A series experiments are conducted, results accuracy compared. The indicate that single not sufficient predict stocks for all days, although if considered, one them gets close result but...

<p>Prediction of financial markets is a challenging task due to its volatility and the presence noise. This work comparatively analyses implementations LSTM (Long Short Term Memory) three kernels SVR (Support Vector Regressor), namely linear kernel, RBF (Radial Basis Function) kernel polynomial kernel. A series experiments are conducted, results accuracy compared. The indicate that single not sufficient predict stocks for all days, although if considered, one them gets close result but...

With the rising numbers for IoT objects, it is becoming easier to penetrate counterfeit objects into mainstream market by adversaries. Such infiltration of bogus products can be addressed with third-party-verifiable identification. Generally, state-of-the-art identification schemes do not guarantee that an identifier e.g. barcodes or RFID itself cannot forged. This paper introduces patterns representing intrinsic identity robust hashes and only generated patterns. Inspired these two notions,...

Understanding how humans use physical contact to interact with the world is key enabling human-centric artificial intelligence. While inferring 3D crucial for modeling realistic and physically-plausible human-object interactions, existing methods either focus on 2D, consider body joints rather than surface, coarse regions, or do not generalize in-the-wild images. In contrast, we dense, between full surface objects in arbitrary To achieve this, first collect DAMON, a new dataset containing...

Estimating 3D humans from images often produces implausible bodies that lean, float, or penetrate the floor. Such methods ignore fact are typically supported by scene. A physics engine can be used to enforce physical plausibility, but these not differentiable, rely on unrealistic proxy bodies, and difficult integrate into existing optimization learning frameworks. In contrast, we exploit novel intuitive-physics (IP) terms inferred a SMPL body interacting with Inspired biomechanics, infer...

We present a task-aware approach to synthetic data generation. Our framework employs trainable synthesizer network that is optimized produce meaningful training samples by assessing the strengths and weaknesses of `target' network. The target networks are trained in an adversarial manner wherein each updated with goal outdo other. Additionally, we ensure generates realistic pairing it discriminator on real-world images. Further, make classifier invariant blending artefacts, introduce these...

Recovering 3D human pose from 2D joints is a highly unconstrained problem. We propose novel neural network framework, PoseNet3D, that takes as input and outputs skeletons SMPL body model parameters. By casting our learning approach in student-teacher we avoid using any data such paired/unpaired data, motion capture sequences, depth images or multi-view during training. first train teacher skeletons, only poses for The distills its knowledge to student predicts representation. Finally, both...

Top-down methods for monocular human mesh recovery have two stages: (1) detect bounding boxes; (2) treat each box as an independent single-human task. Unfortunately, the assumption does not hold in images with multi-human occlusion and crowding. Consequently, top-down difficulties recovering accurate 3D meshes under severe person-person occlusion. To address this, we present Occluded Human Mesh Recovery (OCHMR) - a novel approach that incorporates image spatial context to overcome...

Blockchains provide environments where parties can interact transparently and securely peer-to-peer without needing a trusted third party. Parties trust the integrity correctness of transactions verifiable execution binary code on blockchain (smart contracts) inside system. Including information from outside remains challenging. A challenge is data privacy. In public system, shared becomes and, coming single source, often lacks credibility. private system gives control over their sources but...

<p>Prediction of financial markets is a challenging task due to its volatility and the presence noise. This work comparatively analyses implementations LSTM (Long Short Term Memory) three kernels SVR (Support Vector Regressor), namely linear kernel, RBF (Radial Basis Function) kernel polynomial kernel. A series experiments are conducted, results accuracy compared. The indicate that single not sufficient predict stocks for all days, although if considered, one them gets close result but...