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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

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Current state-of-the-art image-based scene reconstruction techniques are capable of generating high-fidelity 3D models when used under controlled capture conditions. However, they are often inadequate when used in more challenging environments such as sports scenes with moving cameras. Algorithms must be able to cope with relatively large calibration and segmentation errors as well as input images separated by a wide-baseline and possibly captured at different resolutions. In this paper, we propose a technique which, under these challenging conditions, is able to efficiently compute a high-quality scene representation via graph-cut optimisation of an energy function combining multiple image cues with strong priors. Robustness is achieved by jointly optimising scene segmentation and multiple view reconstruction in a view-dependent manner with respect to each input camera. Joint optimisation prevents propagation of errors from segmentation to reconstruction as is often the case with sequential approaches. View-dependent processing increases tolerance to errors in through-the-lens calibration compared to global approaches. We evaluate our technique in the case of challenging outdoor sports scenes captured with manually operated broadcast cameras as well as several indoor scenes with natural background. A comprehensive experimental evaluation including qualitative and quantitative results demonstrates the accuracy of the technique for high quality segmentation and reconstruction and its suitability for free-viewpoint video under these difficult conditions.Keywords Segmentation · Reconstruction · Free-viewpoint video · Graph-cuts Centre for Vision, Speech and Signal Processing University of Surrey, Guildford, GU2 7XH, UK Tel.: +44 1483 683958 Fax: +44 1483 686031 E-mail: J.Guillemaut@surrey.ac.uk Fig. 1 Two moving broadcast camera views (top) and a locked-off camera view (bottom-left) from a rugby match.

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

This paper presents a general approach based on the shape similarity tree for non-sequential alignment across databases of multiple unstructured mesh sequences from non-rigid surface capture. The optimal shape similarity tree for non-rigid alignment is defined as the minimum spanning tree in shape similarity space. Non-sequential alignment based on the shape similarity tree minimises the total non-rigid deformation required to register all frames in a database into a consistent mesh structure with surfaces in correspondence. This allows alignment across multiple sequences of different motions, reduces drift in sequential alignment and is robust to rapid non-rigid motion. Evaluation is performed on three benchmark databases of 3D mesh sequences with a variety of complex human and cloth motion. Comparison with sequential alignment demonstrates reduced errors due to drift and improved robustness to large non-rigid deformation, together with global alignment across multiple sequences which is not possible with previous sequential approaches.

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