Details in mesh animations are difficult to generate but they have great impact on visual quality. In this work, we demonstrate a practical software system for capturing such details from multi-view video recordings. Given a stream of synchronized video images that record a human performance from multiple viewpoints and an articulated template of the performer, our system captures the motion of both the skeleton and the shape. The output mesh animation is enhanced with the details observed in the image silhouettes. For example, a performance in casual loose-fitting clothes will generate mesh animations with flowing garment motions. We accomplish this with a fast pose tracking method followed by nonrigid deformation of the template to fit the silhouettes. The entire process takes less than sixteen seconds per frame and requires no markers or texture cues. Captured meshes are in full correspondence making them readily usable for editing operations including texturing, deformation transfer, and deformation model learning.

Figure 1: Left to right: an actor performing in the capture setup; one of sixteen views from the camera array; reconstructed T-shirt geometry; the real T-shirt is replaced by a rendering of the captured geometry with different appearance characteristics. A lot of research has recently focused on the problem of capturing the geometry and motion of garments. Such work usually relies on special markers printed on the fabric to establish temporally coherent correspondences between points on the garment’s surface at different times. Unfortunately, this approach is tedious and prevents the capture of off-the-shelf clothing made from interesting fabrics. In this paper, we describe a marker-free approach to capturing garment motion that avoids these downsides. We establish temporally coherent parameterizations between incomplete geometries that we extract at each timestep with a multiview stereo algorithm. We then fill holes in the geometry using a template. This approach, for the first time, allows us to capture the geometry and motion of unpatterned, off-the-shelf garments made from a range of different fabrics.

This paper proposes a method for capturing the performance of a human or an animal from a multi-view video sequence. Given an articulated template model and silhouettes from a multi-view image sequence, our approach recovers not only the movement of the skeleton, but also the possibly non-rigid temporal deformation of the 3D surface. While large scale deformations or fast movements are captured by the skeleton pose and approximate surface skinning, true small scale deformations or non-rigid garment motion are captured by fitting the surface to the silhouette. We further propose a novel optimization scheme for skeleton-based pose estimation that exploits the skeleton’s tree structure to split the optimization problem into a local one and a lower dimensional global one. We show on various sequences that our approach can capture the 3D motion of animals and humans accurately even in the case of rapid movements and wide apparel like skirts. 1.

Abstract. We propose a method to estimate the detailed 3D shape of a person from images of that person wearing clothing. The approach exploits a model of human body shapes that is learned from a database of over 2000 range scans. We show that the parameters of this shape model can be recovered independently of body pose. We further propose a generalization of the visual hull to account for the fact that observed silhouettes of clothed people do not provide a tight bound on the true 3D shape. With clothed subjects, different poses provide different constraints on the possible underlying 3D body shape. We consequently combine constraints across pose to more accurately estimate 3D body shape in the presence of occluding clothing. Finally we use the recovered 3D shape to estimate the gender of subjects and then employ genderspecific body models to refine our shape estimates. Results on a novel database of thousands of images of clothed and “naked ” subjects, as well as sequences from the HumanEva dataset, suggest the method may be accurate enough for biometric shape analysis in video. 1

We propose a novel method for computing a fourdimensional (4D) representation of the spatio-temporal visual hull of a dynamic scene, based on an extension of a recent provably correct Delaunay meshing algorithm. By considering time as an additional dimension, our approach exploits seamlessly the time coherence between different frames to produce a compact and high-quality 4D mesh representation of the visual hull. The 3D visual hull at a given time instant is easily obtained by intersecting this 4D mesh with a temporal plane, thus enabling interpolation of objects’ shape between consecutive frames. In addition, our approach offers easy and extensive control over the size and quality of the output mesh as well as over its associated reprojection error. Our numerical experiments demonstrate the effectiveness and flexibility of our approach for generating compact, high-quality, time-coherent visual hull representations from real silhouette image data.

This paper addresses the problem of human motion tracking from multiple image sequences. The human body is described by five articulated mechanical chains and human body-parts are described by volumetric primitives with curved surfaces. If such a surface is observed with a camera, an extremal contour appears in the image whenever the surface turns smoothly away from the viewer. We describe a method that recovers human motion through a kinematic parameterization of these extremal contours. The method exploits the fact that the observed image motion of these contours is a function of both the rigid displacement of the surface and of the relative position and orientation between the viewer and the curved surface. First, we describe a parameterization of an extremal-contour point velocity for the case of developable surfaces. Second, we use the zeroreference kinematic representation and we derive an explicit formula that links extremal contour velocities to the angular velocities associated with the kinematic model. Third, we show how the chamfer-distance may be used to measure the discrepancy between predicted extremal contours and observed image contours; moreover we show how the chamfer distance can be used as a differentiable multi-valued function and how the tracker based on this distance can be cast into a continuous non-linear optimization framework. Fourth, we describe implementation issues associated with a practical human-body tracker that may use an arbitrary number of cameras. One great methodological and practical advantage of our method is that it relies neither on model-toimage, nor on image-to-image point matches. In practice we

Abstract — We address the problem of human motion tracking by registering a surface to 3-D data. We propose a method that iteratively computes two things: Maximum likelihood estimates for both the kinematic and free-motion parameters of a kinematic human-body representation, as well as probabilities that the data are assigned either to a body part, or to an outlier cluster. We introduce a new metric between observed points and normals on one side, and a parameterized surface on the other side, the latter being defined as a blending over a set of ellipsoids. We claim that this metric is well suited when one deals with either visual-hull or visual-shape observations. We illustrate the method by tracking human motions using sparse visual-shape data (3-D surface points and normals) gathered from imperfect silhouettes. Index Terms — model-based tracking, human motion capture, articulated implicit surface, shape from silhouettes, robust surface registration, expectation-maximization. I.

In this contribution we introduce the Multiocular Contracting Curve Density algorithm (MOCCD), a novel method for fitting a 3D parametric curve. The MOCCD is integrated into a tracking system and its suitability for tracking human body parts in 3D in front of cluttered background is examined. The developed system can be applied to a variety of body parts, as the object model is replaceable in a simple manner. Based on the example of tracking the human hand-forearm limb it is shown that the use of three MOCCD algorithms with three different kinematic models within the system leads to an accurate and temporally stable tracking. All necessary information is obtained from the images, only a coarse initialisation of the model parameters is required. The investigations are performed on 14 real-world test sequences. These contain movements of different hand-forearm configurations in front of a complex cluttered background. We find that the use of three cameras is essential for an accurate and temporally stable system performance since otherwise the pose estimation and tracking results are strongly affected by the aperture problem. Our best method achieves 95 % recognition rate, compared to about 30 % for the reference methods of 3D Active Contours and a curve model tracked by a Particle Filter. Hence only 5 % of the estimated model points exceed a distance of 12 cm with respect to the ground truth, using the proposed method. 1

Abstract. Multi-camera networks bring in potentials for a variety of vision-based applications through provisioning of rich visual information. In this paper a method of image segmentation for human gesture analysis in multi-camera networks is presented. Aiming to employ manifold sources of visual information provided by the network, an opportunistic fusion framework is described and incorporated in the proposed method for gesture analysis. A 3D human body model is employed as the converging point of spatiotemporal and feature fusion. It maintains both geometric parameters of the human posture and the adaptively learned appearance attributes, all of which are updated from the three dimensions of space, time and features of the opportunistic fusion. In sufficient confidence levels parameters of the 3D human body model are again used as feedback to aid subsequent vision analysis. The 3D human body model also serves as an intermediate level for gesture interpretation in different applications. The image segmentation method described in this paper is part of the gesture analysis problem. It aims to reduce raw visual data in a single camera to concise descriptions for more efficient communication between cameras. Color distribution registered in the model is used to initialize segmentation. Perceptually Organized Expectation Maximization (POEM) is then applied to refine color segments with observations from a single camera. Finally ellipse fitting is used to parameterize segments. Experimental results for segmentation are illustrated. Some examples for skeleton fitting based on the elliptical segments will also be shown to demonstrate motivation and capability of the model-based segmentation approach for multi-view human gesture analysis. 1

for movement analysis