Sergi Pujades

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In this paper, we address the problem of synthesizing novel views from a set of input images. State of the art methods, such as the Unstructured Lumigraph, have been using heuristics to combine information from the original views, often using an explicit or implicit approximation of the scene geometry. While the proposed heuristics have been largely(More)
This paper presents an occupancy based generative model of stereo and multi-view stereo images. In this model, the space is divided into empty and occupied regions. The depth of a pixel is naturally determined from the occupancy as the depth of the first occupied point in its viewing ray. The color of a pixel corresponds to the color of this 3D point. This(More)
Designing and simulating realistic clothing is challenging. Previous methods addressing the capture of clothing from 3D scans have been limited to single garments and simple motions, lack detail, or require specialized texture patterns. Here we address the problem of capturing regular clothing on fully dressed people in motion. People typically wear(More)
We address the problem of estimating human pose and body shape from 3D scans over time. Reliable estimation of 3D body shape is necessary for many applications including virtual try-on, health monitoring, and avatar creation for virtual reality. Scanning bodies in minimal clothing, however, presents a practical barrier to these applications. We address this(More)
We address the topic of novel view synthesis from a stereoscopic pair of images. The techniques have mainly 3 stages: the reconstruction of correspondences between the views, the estimation of the blending factor of each view for the final view, and the rendering. The state of the art has mainly focused on the correspondence topic, but little work addresses(More)
Data driven models of human poses and soft-tissue deformations can produce very realistic results, but they only model the visible surface of the human body and cannot create skin deformation due to interactions with the environment. Physical simulations can generalize to external forces, but their parameters are difficult to control. In this paper, we(More)
On BUFF dataset, we use λskin = 100, λoutside = 100, λfit = 3, λcpl = 1, and λprior = 0.1. At the stage of estimating per-frame shape TEst, we increase λskin by a factor 10 to retrieve more personalized details. On INRIA dataset, since texture information is not available, we consider all vertices as cloth and therefore set λskin = 0. We decreased λfit = 1(More)