2,369 research outputs found
Shape Animation with Combined Captured and Simulated Dynamics
We present a novel volumetric animation generation framework to create new
types of animations from raw 3D surface or point cloud sequence of captured
real performances. The framework considers as input time incoherent 3D
observations of a moving shape, and is thus particularly suitable for the
output of performance capture platforms. In our system, a suitable virtual
representation of the actor is built from real captures that allows seamless
combination and simulation with virtual external forces and objects, in which
the original captured actor can be reshaped, disassembled or reassembled from
user-specified virtual physics. Instead of using the dominant surface-based
geometric representation of the capture, which is less suitable for volumetric
effects, our pipeline exploits Centroidal Voronoi tessellation decompositions
as unified volumetric representation of the real captured actor, which we show
can be used seamlessly as a building block for all processing stages, from
capture and tracking to virtual physic simulation. The representation makes no
human specific assumption and can be used to capture and re-simulate the actor
with props or other moving scenery elements. We demonstrate the potential of
this pipeline for virtual reanimation of a real captured event with various
unprecedented volumetric visual effects, such as volumetric distortion,
erosion, morphing, gravity pull, or collisions
LiveCap: Real-time Human Performance Capture from Monocular Video
We present the first real-time human performance capture approach that
reconstructs dense, space-time coherent deforming geometry of entire humans in
general everyday clothing from just a single RGB video. We propose a novel
two-stage analysis-by-synthesis optimization whose formulation and
implementation are designed for high performance. In the first stage, a skinned
template model is jointly fitted to background subtracted input video, 2D and
3D skeleton joint positions found using a deep neural network, and a set of
sparse facial landmark detections. In the second stage, dense non-rigid 3D
deformations of skin and even loose apparel are captured based on a novel
real-time capable algorithm for non-rigid tracking using dense photometric and
silhouette constraints. Our novel energy formulation leverages automatically
identified material regions on the template to model the differing non-rigid
deformation behavior of skin and apparel. The two resulting non-linear
optimization problems per-frame are solved with specially-tailored
data-parallel Gauss-Newton solvers. In order to achieve real-time performance
of over 25Hz, we design a pipelined parallel architecture using the CPU and two
commodity GPUs. Our method is the first real-time monocular approach for
full-body performance capture. Our method yields comparable accuracy with
off-line performance capture techniques, while being orders of magnitude
faster
A survey on 2d object tracking in digital video
This paper presents object tracking methods in video.Different algorithms based on rigid, non rigid and articulated object tracking are studied. The goal of this article is to review the state-of-the-art tracking methods, classify them
into different categories, and identify new trends.It is often the case that tracking objects in consecutive frames is supported by a prediction scheme. Based on information extracted from previous frames and any high level information that can be obtained, the state (location) of the
object is predicted.An excellent framework for prediction is kalman filter, which additionally estimates prediction error.In complex scenes, instead of single hypothesis, multiple hypotheses using Particle filter can be used.Different
techniques are given for different types of constraints in video
Deformable and articulated 3D reconstruction from monocular video sequences
PhDThis thesis addresses the problem of deformable and articulated structure from motion from
monocular uncalibrated video sequences. Structure from motion is defined as the problem of
recovering information about the 3D structure of scenes imaged by a camera in a video sequence.
Our study aims at the challenging problem of non-rigid shapes (e.g. a beating heart or a smiling
face). Non-rigid structures appear constantly in our everyday life, think of a bicep curling, a
torso twisting or a smiling face. Our research seeks a general method to perform 3D shape
recovery purely from data, without having to rely on a pre-computed model or training data.
Open problems in the field are the difficulty of the non-linear estimation, the lack of a real-time
system, large amounts of missing data in real-world video sequences, measurement noise and
strong deformations. Solving these problems would take us far beyond the current state of the
art in non-rigid structure from motion. This dissertation presents our contributions in the field
of non-rigid structure from motion, detailing a novel algorithm that enforces the exact metric
structure of the problem at each step of the minimisation by projecting the motion matrices
onto the correct deformable or articulated metric motion manifolds respectively. An important
advantage of this new algorithm is its ability to handle missing data which becomes crucial
when dealing with real video sequences. We present a generic bilinear estimation framework,
which improves convergence and makes use of the manifold constraints. Finally, we demonstrate
a sequential, frame-by-frame estimation algorithm, which provides a 3D model and camera
parameters for each video frame, while simultaneously building a model of object deformation
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