Learning to Reconstruct People in Clothing from a Single RGB Camera

We present a learning-based model to infer the personalized 3D shape of people from a few frames (1-8) of a monocular video in which the person is moving, in less than 10 seconds with a reconstruction accuracy of 5mm. Our model learns to predict the parameters of a statistical body model and instance displacements that add clothing and hair to the shape. The model achieves fast and accurate predictions based on two key design choices. First, by predicting shape in a canonical T-pose space, the network learns to encode the images of the person into pose-invariant latent codes, where the information is fused. Second, based on the observation that feed-forward predictions are fast but do not always align with the input images, we predict using both, bottom-up and top-down streams (one per view) allowing information to flow in both directions. Learning relies only on synthetic 3D data. Once learned, the model can take a variable number of frames as input, and is able to reconstruct shapes even from a single image with an accuracy of 6mm. Results on 3 different datasets demonstrate the efficacy and accuracy of our approach

Interacting Unities: An Agent-Based System

Recently architects have been inspired by Thompsonis Cartesian deformations and Waddingtonis flexible topological surface to work within a dynamic field characterized by forces. In this more active space of interactions, movement is the medium through which form evolves. This paper explores the interaction between pedestrians and their environment by regarding it as a process occurring between the two. It is hypothesized that the recurrent interaction between pedestrians and environment can lead to a structural coupling between those elements. Every time a change occurs in each one of them, as an expression of its own structural dynamics, it triggers changes to the other one. An agent-based system has been developed in order to explore that interaction, where the two interacting elements, agents (pedestrians) and environment, are autonomous units with a set of internal rules. The result is a landscape where each agent locally modifies its environment that in turn affects its movement, while the other agents respond to the new environment at a later time, indicating that the phenomenon of stigmergy is possible to take place among interactions with human analogy. It is found that it is the environmentis internal rules that determine the nature and extent of change

The Dam1 ring binds to the E-hook of tubulin and diffuses along the microtubule.

There has been much effort in recent years aimed at understanding the molecular mechanism by which the Dam1 kinetochore complex is able to couple microtubule depolymerization to poleward movement. Both a biased diffusion and a forced walk model have been proposed, and several key functional aspects of Dam1-microtubule binding are disputed. Here, we investigate the elements involved in tubulin-Dam1 complex interactions and directly visualize Dam1 rings on microtubules in order to infer their dynamic behavior on the microtubule lattice and its likely relevance at the kinetochore. We find that the Dam1 complex has a preference for native tubulin over tubulin that is lacking its acidic C-terminal tail. Statistical mechanical analysis of images of Dam1 rings on microtubules, applied to both the distance between rings and the tilt angle of the rings with respect to the microtubule axis, supports a diffusive ring model. We also present a cryo-EM reconstruction of the Dam1 ring, likely the relevant assembly form of the complex for energy coupling during microtubule depolymerization in budding yeast. The present studies constitute a significant step forward by linking structural and biochemical observations toward a comprehensive understanding of the Dam1 complex