Shape Similarity for 3D Video Sequences of People

Abstract

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

Global Non-rigid Alignment of Surface Sequences

“…Throughout this work shape similarity is computed with a spherical shape histogram of 1.5 m radius with bin sizes ( r, θ, φ) = (0.3 m, 18 • , 18 • ) (Huang et al 2010). Figure 6 presents the similarity matrix for each database of unstructured mesh sequences.…”

“…The input is a database of unstructured mesh sequences of different nonrigid motions reconstructed from multiple view video or other sensors. Shape similarity between pairs of meshes with unknown correspondence (Huang et al 2010) is used as a measure of the cost of pairwise non-rigid alignment. The shape similarity tree is introduced to represent the nonsequential alignment which minimises the total non-rigid deformation.…”

“…A number of similarity measures for mesh sequences taking into account both shape and motion have been investigated (Tung and Matsuyama 2010;Huang et al 2010). In this work we utilise the temporally filtered volumetric shape histogram as a measure of shape and non-rigid motion similarity which has been shown to give good performance on reconstructed mesh sequences of people (Huang et al 2010). …”

“…The volumetric shape histogram subdivides 3 space into radial and angular bins ( r, θ, φ) based on a spherical coordinate system located at the centroid of the mesh M (Huang et al 2010), as illustrated in Fig. 2 (t v ) by optimising for the maximum overlap between their corresponding radial bins with respect to rotation about the centroid.…”

“…A measure of shape and non-rigid motion similarity is obtained by a weighted average of shape similarity over a temporal window. This can be efficiently evaluated by diagonal filtering of the shape similarity matrix A (Huang et al 2010). An example similarity matrix is shown in Fig.…”