Recent advances in laser scanning technology have made it possible to faithfully scan a real object with tiny geometric details, such as pores and wrinkles. However, a faithful digital model should not only capture static details of the real counterpart but also be able to reproduce the deformed versions of such details. In this paper, we develop a data-driven model that has two components; the first accommodates smooth large-scale deformations and the second captures high-resolution details. Large-scale deformations are based on a nonlinear mapping between sparse control points and bone transformations. A global mapping, however, would fail to synthesize realistic geometries from sparse examples, for highly deformable models with a large range of motion. The key is to train a collection of mappings defined over regions locally in both the geometry and the pose space. Deformable fine-scale details are generated from a second nonlinear mapping between the control points and per-vertex displacements. We apply our modeling scheme to scanned human hand models, scanned face models, face models reconstructed from multiview video sequences, and manually constructed dinosaur models. Experiments show that our deformation models, learned from extremely sparse training data, are effective and robust in synthesizing highly deformable models with rich fine features, for keyframe animation as well as performance-driven animation. We also compare our results with those obtained by alternative techniques.

This paper introduces a new approach for acquiring high-fidelity 3D facial performances with realistic dynamic wrinkles and fine-scale facial details. Our approach leverages state-of-the-art motion capture technology and advanced 3D scanning technology for facial performance acquisition. We start the process by recording 3D facial performances of an actor using a marker-based motion capture system and perform facial analysis on the captured data, thereby determining a minimal set of face scans required for accurate facial reconstruction. We introduce a two-step registration process to efficiently build dense consistent surface correspondences across all the face scans. We reconstruct high-fidelity 3D facial performances by combining motion capture data with the minimal set of face scans in the blendshape interpolation framework. We have evaluated the performance of our system on both real and synthetic data. Our results show that the system can capture facial performances that match both the spatial resolution of static face scans and the acquisition speed of motion capture systems.

Most of existing importance sampling methods for direct illumination exploit importance of illumination and surface BRDF. Without taking the visibility into consideration, they can not adaptively adjust the number of samples for each pixel during the sampling process. As a result, these methods tend to produce images with noise in partially occluded regions. In this paper, we introduce an incremental wavelet importance sampling approach, in which the visibility information is used to determine the number of samples at run time. For this purpose, we present a perceptual-based variance that is computed from visibility of samples. In the sampling process, the Halton sample points are incrementally warped for each pixel until the variance of warped samples converges. We demonstrate that our method is more efficient than existing importance sampling approaches.

In this paper, a method for automatically detecting the end of lively broadcasted sports programs is proposed, which enables users to record the full TV programs when they run over time. Taking advantage of the property of relatively high content consistency within sports programs, this method is based on checking the break point of this content consistency. A scalable video segment similarity measure is proposed to measure content consistency of video segments in different similarity levels. Based on this measure, a two-round end detection scheme is applied, in which the first round, Candidate End Point Finding, is able to find a coarse candidate end point, and the second round, End Point Refining, is able to find a more accurate end. Experiments show that the proposed end detection scheme is able to detect the real ends with high accuracy.