We present the articulated global registration algorithm to reconstruct articulated 3D models from dynamic range scan sequences. This new algorithm aligns multiple range scans simultaneously to reconstruct a full 3D model from the geometry of these scans. Unlike other methods, we express the surface motion in terms of a reduced deformable model and solve for joints and skinning weights. This allows a user to interactively manipulate the reconstructed 3D model to create new animations. We express the global registration as an optimization of both the alignment of the range scans and the articulated structure of the model. We employ a graph-based representation for the skinning weights that successfully handles difficult topological cases well. Joints between parts are estimated automatically and are used in the optimization to preserve the connectivity between parts. The algorithm also robustly handles difficult cases where parts suddenly disappear or reappear in the range scans. The global registration produces a more accurate registration compared to a sequential registration approach, because it estimates the articulated structure based on the motion observed in all input frames. We show that we can automatically reconstruct a variety of articulated models without the use of markers, user-placed correspondences, segmentation, or template model.

Abstract We present an unsupervised method for registering range scans of deforming, articulated shapes. The key idea is to model the motion of the underlying object using a reduced deformable model. We use a linear skinning model for its simplicity and represent the weight functions on a regular grid localized to the surface geometry. This decouples the deformation model from the surface representation and allows us to deal with the severe occlusion and missing data that is inherent in range scan data. We formulate the registration problem using an objective function that enforces close alignment of the 3D data and includes an intuitive notion of joints. This leads to an optimization problem that we solve using an efficient EM‐type algorithm. With our algorithm we obtain smooth deformations that accurately register pairs of range scans with significant motion and occlusion. The main advantages of our approach are that it does not require user specified markers, a template, nor manual segmentation of the surface geometry into rigid parts.

Abstract We present an unsupervised algorithm for aligning a pair of shapes in the presence of significant articulated motion and missing data, while assuming no knowledge of a template, user‐placed markers, segmentation, or the skeletal structure of the shape. We explicitly sample the motion, which gives a priori the set of possible rigid transformations between parts of the shapes. This transforms the problem into a discrete labeling problem, where the goal is to find an optimal assignment of transformations for aligning the shapes. We then apply graph cuts to optimize a novel cost function, which encodes a preference for a consistent motion assignment from both source to target and target to source. We demonstrate the robustness of our method by aligning several synthetic and real‐world datasets.

We accurately capture the shape and appearance of a person's hairstyle. We use triangulation and a sweep with planes of light for the geometry. Multiple projectors and cameras address the challenges raised by the reflectance and intricate geometry of hair. We introduce the use of structure tensors to infer the hidden geometry between the hair surface and the scalp. Our triangulation approach affords substantial accuracy improvement and we are able to measure elaborate hair geometry including complex curls and concavities. To reproduce the hair appearance, we capture a six-dimensional reflectance field. We introduce a new reflectance interpolation technique that leverages an analytical reflectance model to alleviate cross-fading artifacts caused by linear methods. Our results closely match the real hairstyles and can be used for animation.