Digital content production traditionally requires highly skilled artists and animators to first manually craft shape and appearance models and then instill the models with a believable performance. Motion capture technology is now increasingly used to record the articulated motion of a real human performance to increase the visual realism in animation. Motion capture is limited to recording only the skeletal motion of the human body and requires the use of specialist suits and markers to track articulated motion.In this paper we present surface capture, a fully automated system to capture shape and appearance as well as motion from multiple video cameras as a basis to create highly realistic animated content from an actor's performance in full wardrobe. We address wide-baseline scene reconstruction to provide 360 degree appearance from just 8 camera views and introduce an efficient scene representation for level of detail control in streaming and rendering. Finally we demonstrate interactive animation control in a computer games scenario using a captured library of human animation, achieving a frame rate of 300fps on consumer level graphics hardware. Index TermsImage-based modelling and rendering, Video-based character animation

This paper presents a performance evaluation of shape similarity metrics for 3D video sequences of people with unknown temporal correspondence. Performance of similarity measures is compared by evaluating Receiver Operator Characteristics for classification against ground-truth for a comprehensive database of synthetic 3D video sequences comprising animations of fourteen people performing twentyeight motions. Static shape similarity metrics shape distribution, spin image, shape histogram and spherical harmonics are evaluated using optimal parameter settings for each approach. Shape histograms with volume sampling are found to consistently give the best performance for different people and motions. Static shape similarity is extended over time to eliminate the temporal ambiguity. Time-filtering of the static shape similarity together with two novel shape-flow descriptors are evaluated against temporal ground-truth. This evaluation demonstrates that shape-flow with a multi-frame alignment of motion sequences achieves the best performance, is stable for different people and motions, and overcome the ambiguity in static shape similarity. Time-filtering of the static shape histogram similarity measure with a fixed window size achieves marginally lower performance for linear motions with the same computational cost as static shape descriptors. Performance of the temporal shape descriptors is validated for real 3D video sequence of nine actors performing a variety of movements. Time-filtered shape histograms are shown to reliably identify frames from 3D video sequences with similar shape and motion for people with loose clothing and complex motion

Abstract

In this paper we introduce a video-based representation for free viewpoint visualization and motion control of

Multiple view 3D video reconstruction of actor performance captures a level-of-detail for body and clothing movement which is time-consuming to produce using existing animation tools. In this paper we present a framework for concatenative synthesis from multiple 3D video sequences according to user constraints on movement, position and timing. Multiple 3D video sequences of an actor performing different movements are automatically constructed into a surface motion graph which represents the possible transitions with similar shape and motion between sequences without unnatural movement artifacts. Shape similarity over an adaptive temporal window is used to identify transitions between 3D video sequences. Novel 3D video sequences are synthesized by finding the optimal path in the surface motion graph between user specified key-frames for control of movement, location and timing. The optimal path which satisfies the user constraints whilst minimizing the total transition cost between 3D video sequences is found using integer linear programming. Results demonstrate that this framework allows flexible production of novel 3D video sequences which preserve the detailed dynamics of the captured movement for an actress with loose clothing and long hair without visible artifacts.