Exploring the use of skeletal tracking for cheaper motion graphs and on-set decision making in Free-Viewpoint Video production

In free-viewpoint video (FVV), the motion and surface appearance of a real-world performance is captured as an animated mesh. While this technology can produce high-fidelity recreations of actors, the required 3D reconstruction step has substantial processing demands. This means FVV experiences are currently expensive to produce, and the processing delay means on-set decisions are hampered by a lack of feedback. This work explores the possibility of using RGB-camera-based skeletal tracking to reduce the amount of content that must be 3D reconstructed, as well as aiding on-set decision making. One particularly relevant application is in the construction of Motion Graphs, where state-of-the-art techniques require large amounts of content to be 3D reconstructed before a graph can be built, resulting in large amounts of wasted processing effort. Here, we propose the use of skeletons to assess which clips of FVV content to process, resulting in substantial cost savings with a limited impact on performance accuracy. Additionally, we explore how this technique could be utilised on set to reduce the possibility of requiring expensive reshoots

Identifying protein complexes directly from high-throughput TAP data with Markov random fields

<p>Abstract</p> <p>Background</p> <p>Predicting protein complexes from experimental data remains a challenge due to limited resolution and stochastic errors of high-throughput methods. Current algorithms to reconstruct the complexes typically rely on a two-step process. First, they construct an interaction graph from the data, predominantly using heuristics, and subsequently cluster its vertices to identify protein complexes.</p> <p>Results</p> <p>We propose a model-based identification of protein complexes directly from the experimental observations. Our model of protein complexes based on Markov random fields explicitly incorporates false negative and false positive errors and exhibits a high robustness to noise. A model-based quality score for the resulting clusters allows us to identify reliable predictions in the complete data set. Comparisons with prior work on reference data sets shows favorable results, particularly for larger unfiltered data sets. Additional information on predictions, including the source code under the GNU Public License can be found at http://algorithmics.molgen.mpg.de/Static/Supplements/ProteinComplexes.</p> <p>Conclusion</p> <p>We can identify complexes in the data obtained from high-throughput experiments without prior elimination of proteins or weak interactions. The few parameters of our model, which does not rely on heuristics, can be estimated using maximum likelihood without a reference data set. This is particularly important for protein complex studies in organisms that do not have an established reference frame of known protein complexes.</p

A Smart Algorithm for Column Chart Labeling

This paper presents a smart algorithm for labeling column charts and their derivatives. To efficiently solve the problem, we separate it into two sub-problems. We first present a geometric algorithm to solve the problem of finding a good labeling for the labels of a single column, given that some other columns have already been labeled. We then present a strategy for finding a good order in which columns should be labeled, which repeatedly uses the first algorithm for some limited lookahead. The presented algorithm is being used in a commercial product to label charts, and has shown in practice to produce satisfactory results

Machine Learning for Video-Based Rendering

We pres[ t techniques for rendering and animation of realis4: ss4: by analyzing and training onsS7] videosoS7(1[(S This workextends the new paradigm for computer animation, video textures, whichus[ recorded video to generate novelanimations by replaying the videos amples in a new order. Here we concentrate on video sprites , which are a s ecial type of video texture. In videos prites ins tead of s oring whole images the object of interes is s eparated from the background and the videos amples are seSF1 as as equence of alpha-matteds prites with ash ciated velocity information. They can be rendered anywhere on the sS een to create a novel animation of the object. We presF t methods to creates uch animations by finding as equence ofs prites amples that is bothvis4 lly sy oth and follows a des7 ed path. To esF[( te visRR s oothnesR we train a linear clas71(S to es imate vis7R s7R] rity between videos amples If the motion pathis known in advance, weus beams earch to find a goods7174 s7174(Sfi We cans pecify the motion interactively by precomputing thesSFR4][ co s functionus[) Q-learning.

Head Tracking Using a Textured Polygonal Model

We describe the use of a three-dimensional textured model of the human head under perspective projection to track a person&apos;s face. The system is hand-initialized by projecting an image of the face onto a polygonal head model. Tracking is achieved by finding the six translation and rotation parameters to register the rendered images of the textured model with the video images. We find the parameters by mapping the derivative of the error with respect to the parameters to intensity gradients in the image. We use a robust estimator to pool the information and do gradient descent to find an error minimum. 1. Introduction Head tracking is an important processing step for many vision-driven interactive user interfaces. The obtained position and orientation allow for pose determination and recognition of simple gestures such as nodding and head shaking. The stabilized image obtained by perspective dewarping of the facial image according to the acquired parameters is ideal for facial expressi..

Controlled Animation of Video Sprites

velocity vector Figure 1: Creating an animation from video sprite samples. We introduce a new optimization algorithm for video sprites to animate realistic-looking characters. Video sprites are animations created by rearranging recorded video frames of a moving object. Our new technique to find good frame arrangements is based on repeated partial replacements of the sequence. It allows the user to specify animations using a flexible cost function. We also show a fast technique to compute video sprite transitions and a simple algorithm to correct for perspective effects of the input footage. We use our techniques to create character animations of animals, which are difficult both to train in the real world and to animate as 3D models