41-Issue 2
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Browsing 41-Issue 2 by Author "Cohen-Or, Daniel"
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Item MoCo-Flow: Neural Motion Consensus Flow for Dynamic Humans in Stationary Monocular Cameras(The Eurographics Association and John Wiley & Sons Ltd., 2022) Chen, Xuelin; Li, Weiyu; Cohen-Or, Daniel; Mitra, Niloy J.; Chen, Baoquan; Chaine, Raphaƫlle; Kim, Min H.Synthesizing novel views of dynamic humans from stationary monocular cameras is a specialized but desirable setup. This is particularly attractive as it does not require static scenes, controlled environments, or specialized capture hardware. In contrast to techniques that exploit multi-view observations, the problem of modeling a dynamic scene from a single view is significantly more under-constrained and ill-posed. In this paper, we introduce Neural Motion Consensus Flow (MoCo-Flow), a representation that models dynamic humans in stationary monocular cameras using a 4D continuous time-variant function. We learn the proposed representation by optimizing for a dynamic scene that minimizes the total rendering error, over all the observed images. At the heart of our work lies a carefully designed optimization scheme, which includes a dedicated initialization step and is constrained by a motion consensus regularization on the estimated motion flow. We extensively evaluate MoCo-Flow on several datasets that contain human motions of varying complexity, and compare, both qualitatively and quantitatively, to several baselines and ablated variations of our methods, showing the efficacy and merits of the proposed approach. Pretrained model, code, and data will be released for research purposes upon paper acceptance.Item Z2P: Instant Visualization of Point Clouds(The Eurographics Association and John Wiley & Sons Ltd., 2022) Metzer, Gal; Hanocka, Rana; Giryes, Raja; Mitra, Niloy J.; Cohen-Or, Daniel; Chaine, Raphaƫlle; Kim, Min H.We present a technique for visualizing point clouds using a neural network. Our technique allows for an instant preview of any point cloud, and bypasses the notoriously difficult surface reconstruction problem or the need to estimate oriented normals for splat-based rendering. We cast the preview problem as a conditional image-to-image translation task, and design a neural network that translates point depth-map directly into an image, where the point cloud is visualized as though a surface was reconstructed from it. Furthermore, the resulting appearance of the visualized point cloud can be, optionally, conditioned on simple control variables (e.g., color and light). We demonstrate that our technique instantly produces plausible images, and can, on-the-fly effectively handle noise, non-uniform sampling, and thin surfaces sheets.