Topic
View synthesis
About: View synthesis is a research topic. Over the lifetime, 1701 publications have been published within this topic receiving 42333 citations.
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21 Jun 1995TL;DR: It is shown that two basis views are sufficient to predict the appearance of the scene within a specific range of new viewpoints and that generating this range of views is a theoretically well-posed problem, requiring neither knowledge of camera positions nor 3D scene reconstruction.
Abstract: Image warping is a popular tool for smoothly transforming one image to another. "Morphing" techniques based on geometric image interpolation create compelling visual effects, but the validity of such transformations has not been established. In particular, does 2D interpolation of two views of the same scene produce a sequence of physically valid in-between views of that scene? We describe a simple image rectification procedure which guarantees that interpolation does in fact produce valid views, under generic assumptions about visibility and the projection process. Towards this end, it is first shown that two basis views are sufficient to predict the appearance of the scene within a specific range of new viewpoints. Second, it is demonstrated that interpolation of the rectified basis images produces exactly this range of views. Finally, it is shown that generating this range of views is a theoretically well-posed problem, requiring neither knowledge of camera positions nor 3D scene reconstruction. A scanline algorithm for view interpolation is presented that requires only four user-provided feature correspondences to produce valid orthographic views. The quality of the resulting images is demonstrated with interpolations of real imagery.
177 citations
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01 Oct 2019TL;DR: Zhang et al. as mentioned in this paper propose to use a 3D body mesh recovery module to disentangle the pose and shape, which can not only model the joint location and rotation but also characterize the personalized body shape.
Abstract: We tackle the human motion imitation, appearance transfer, and novel view synthesis within a unified framework, which means that the model once being trained can be used to handle all these tasks. The existing task-specific methods mainly use 2D keypoints (pose) to estimate the human body structure. However, they only expresses the position information with no abilities to characterize the personalized shape of the individual person and model the limbs rotations. In this paper, we propose to use a 3D body mesh recovery module to disentangle the pose and shape, which can not only model the joint location and rotation but also characterize the personalized body shape. To preserve the source information, such as texture, style, color, and face identity, we propose a Liquid Warping GAN with Liquid Warping Block (LWB) that propagates the source information in both image and feature spaces, and synthesizes an image with respect to the reference. Specifically, the source features are extracted by a denoising convolutional auto-encoder for characterizing the source identity well. Furthermore, our proposed method is able to support a more flexible warping from multiple sources. In addition, we build a new dataset, namely Impersonator (iPER) dataset, for the evaluation of human motion imitation, appearance transfer, and novel view synthesis. Extensive experiments demonstrate the effectiveness of our method in several aspects, such as robustness in occlusion case and preserving face identity, shape consistency and clothes details. All codes and datasets are available on https://svip-lab.github.io/project/impersonator.html.
174 citations
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TL;DR: A new hole filling approach for DIBR using texture synthesis is presented and results show that the proposed approach provides improved rendering results in comparison to the latest MPEG view synthesis reference software (VSRS) version 3.6.
Abstract: A depth image-based rendering (DIBR) approach with advanced inpainting methods is presented. The DIBR algorithm can be used in 3-D video applications to synthesize a number of different perspectives of the same scene, e.g., from a multiview-video-plus-depth (MVD) representation. This MVD format consists of video and depth sequences for a limited number of original camera views of the same natural scene. Here, DIBR methods allow the computation of additional new views. An inherent problem of the view synthesis concept is the fact that image information which is occluded in the original views may become visible, especially in extrapolated views beyond the viewing range of the original cameras. The presented algorithm synthesizes these occluded textures. The synthesizer achieves visually satisfying results by taking spatial and temporal consistency measures into account. Detailed experiments show significant objective and subjective gains of the proposed method in comparison to the state-of-the-art methods.
172 citations
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01 Nov 2019TL;DR: Extreme View Synthesis as mentioned in this paper estimates a depth probability volume, rather than just a single depth value for each pixel of the novel view, and combines learned image priors and the depth uncertainty to synthesize a refined image with less artifacts.
Abstract: We present Extreme View Synthesis, a solution for novel view extrapolation that works even when the number of input images is small---as few as two. In this context, occlusions and depth uncertainty are two of the most pressing issues, and worsen as the degree of extrapolation increases. We follow the traditional paradigm of performing depth-based warping and refinement, with a few key improvements. First, we estimate a depth probability volume, rather than just a single depth value for each pixel of the novel view. This allows us to leverage depth uncertainty in challenging regions, such as depth discontinuities. After using it to get an initial estimate of the novel view, we explicitly combine learned image priors and the depth uncertainty to synthesize a refined image with less artifacts. Our method is the first to show visually pleasing results for baseline magnifications of up to 30x.
170 citations
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01 Oct 2017
TL;DR: In this paper, a convolutional neural network (CNN) is used to estimate scene geometry, a stage that renders a Lambertian light field using that geometry, and a second CNN that predicts occluded rays and non-Lambertian effects.
Abstract: We present a machine learning algorithm that takes as input a 2D RGB image and synthesizes a 4D RGBD light field (color and depth of the scene in each ray direction). For training, we introduce the largest public light field dataset, consisting of over 3300 plenoptic camera light fields of scenes containing flowers and plants. Our synthesis pipeline consists of a convolutional neural network (CNN) that estimates scene geometry, a stage that renders a Lambertian light field using that geometry, and a second CNN that predicts occluded rays and non-Lambertian effects. Our algorithm builds on recent view synthesis methods, but is unique in predicting RGBD for each light field ray and improving unsupervised single image depth estimation by enforcing consistency of ray depths that should intersect the same scene point.
168 citations