ControlMat: A Controlled Generative Approach to Material Capture

Material reconstruction from a photograph is a key component of 3D content creation democratization. We propose to formulate this ill-posed problem as a controlled synthesis one, leveraging the recent progress in generative deep networks. We present ControlMat, a method which, given a single photograph with uncontrolled illumination as input, conditions a diffusion model to generate plausible, tileable, high-resolution physically-based digital materials. We carefully analyze the behavior of diffusion models for multi-channel outputs, adapt the sampling process to fuse multi-scale information and introduce rolled diffusion to enable both tileability and patched diffusion for high-resolution outputs. Our generative approach further permits exploration of a variety of materials which could correspond to the input image, mitigating the unknown lighting conditions. We show that our approach outperforms recent inference and latent-space-optimization methods, and carefully validate our diffusion process design choices. Supplemental materials and additional details are available at: https://gvecchio.com/controlmat/

Non-verbal communication in the speeches of politicians (Невербальні засоби комунікації у виступах політиків)

The article outlines the features of non-verbal communication, their role in the communication process. Also considered their species, interpretation on examples of Ukrainian politician’s speeches (У статті визначено особливості невербальних засобів комунікацій, їх роль у процесі комунікації. Також розглянуто їхні види, тлумачення на прикладах виступів українських політиків

Material acquisition using deep learning

International audienceTexture, highlights, and shading are some of many visual cues that allow humans to perceive material appearance in pictures. Designing algorithms able to leverage these cues to recover spatially-varying bi-directional reflectance distribution functions (SVBRDFs) from a few images has challenged computer graphics researchers for decades. I explore the use of deep learning to tackle lightweight appearance capture and make sense of these visual cues. Our networks are capable of recovering per-pixel normals, diffuse albedo, specular albedo and specular roughness from as little as one picture of a flat surface lit by a hand-held flash. We propose a method which improves its prediction with the number of input pictures, and reaches high quality reconstructions with up to 10 images -- a sweet spot between existing single-image and complex multi-image approaches. We introduce several innovations on training data acquisition and network design, bringing clear improvement over the state of the art for lightweight material capture

Flexible SVBRDF Capture with a Multi-Image Deep Network

International audienceEmpowered by deep learning, recent methods for material capture can estimate a spatially-varying reflectance from a single photograph. Such lightweight capture is in stark contrast with the tens or hundreds of pictures required by traditional optimization-based approaches. However, a single image is often simply not enough to observe the rich appearance of real-world materials. We present a deep-learning method capable of estimating material appearance from a variable number of uncalibrated and unordered pictures captured with a handheld camera and flash. Thanks to an order-independent fusing layer, this architecture extracts the most useful information from each picture, while benefiting from strong priors learned from data. The method can handle both view and light direction variation without calibration. We show how our method improves its prediction with the number of input pictures, and reaches high quality reconstructions with as little as 1 to 10 images-a sweet spot between existing single-image and complex multi-image approaches

Changing recycling behaviour: an evaluation of attitudes and behaviour to recycling in the Western Riverside area of London

Key to improving recycling performance in the UK is the need to more effectively engage the public and improve levels of participation. The research described in this paper combines quantitative and qualitative analysis of attitudinal and behavioural changes as part of an evaluation project to measure the impact of a multi-faceted waste awareness and education campaign in the Western Riverside Waste Authority area of central London. Analysis links attitudinal and behavioural responses to infrastructure provision and performance indicators for each area surveyed, as well as socio-demographic indicators. This paper presents the results and analysis of the attitudes, motivations and behaviours of a representative profile of households from this area of central London, in an attempt to better understand how behavioural change can be achieved and recycling targets met

Capturing composites manufacturing waste flows through process mapping

The necessity of high performance materials has become latent in high technology sectors such as aerospace, automotive, renewable energy, nuclear engineering and sports. The expanding impact on future manufacturing of the EU waste management legislation and increasing price of current waste management methods stress the importance of an efficient and sustainable way of recycling waste generated in the composites industry. Aerospace companies estimated that 30e50% of materials in aircraft production are scrapped due to the way it is manufactured. Companies need to pay for landfilling the composite materials that otherwise can be a valuable resource. In a view that looking at individual production waste outputs could maximise the material reuse or recycling capability, gaining information about the type of scrap materials could inform the development of composite reuse/recycling supply chain. This research paper focuses on understanding the scale of scrap created in individual composites manufacturing processes to assess its potential value in terms of reuse/recycle capabilities. A Material flow analysis based data collection workshop has been performed with four composite manufacturers. Through the case studies it has been identified that there are three fibre related waste outputs captured: dry fibres, fibre material sheet off-cuts, and curried composite off-cuts. The captured information allows for the material specification development. This allows bridging the gap between the manufacturers and the waste processors in composites to address the lack of infrastructure and lack of waste material specification barriers outlined by the Composites Strategy 2009

Late Glacial to Holocene relative sea-level change in Assynt, northwest Scotland, UK

Relative sea-level change (RSL), from the Late Glacial through to the late Holocene, is reconstructed for the Assynt region, northwest Scotland, based on bio- and lithostratigraphical analysis. Four new radiocarbon-dated sea-level index points help constrain RSL change for the Late Glacial to the late Holocene. These new data, in addition to published material, capture the RSL fall during the Late Glacial and the rise and fall associated with the mid-Holocene highstand. Two of these index points constrain the Late Glacial RSL history in Assynt for the first time, reconstructing RSL falling from 2.47 ± 0.59 m OD to 0.15 ± 0.59 m OD at c. 14,000–15,000 cal yr BP. These new data test model predictions of glacial isostatic adjustment (GIA), particularly during the early deglacial period which is currently poorly constrained throughout the British Isles. Whilst the empirical data from the mid- to late-Holocene to present matches quite well with the recent GIA model output, there is a relatively poor fit between the timing of the Late Glacial RSL fall and early Holocene RSL rise. This mismatch, also evident elsewhere in northwest Scotland, may result from uncertainties associated with both the global and local ice components of GIA models.</jats:p