Adrian Hilton

Surface Capture for Performance-Based Animation

Creating realistic animated models of people is a central task in digital content production. Traditionally, highly skilled artists and animators construct shape and appearance models for digital character. They then define the characters motion at each time frame or specific key-frames in a motion sequence to create a digital performance. Increasingly, producers are using motion capture technology to record animations from an actors performance. This technology reduces animation production time and captures natural movements to create a more believable production. However, motion capture requires the use of specialist suits and markers and only records skeletal motion. It lacks the detailed secondary surface dynamics of cloth and hair that provide the visual realism of a live performance. Over the last decade, we have investigated studio capture technology with the objective of creating models of real people that accurately reflect the time-varying shape and appearance of the whole body with clothing. Surface capture is a fully automated system for capturing a humans shape and appearance as well as motion from multiple video cameras to create highly realistic animated content from an actors performance in full wardrobe. Our system solves two key problems in performance capture: scene capture from a limited number of camera views and efficient scene representation for visualization

Reliable Surface Reconstructiuon from Multiple Range Images

This paper addresses the problem of reconstructing an integrated 3D model from multiple 2.5D range images. A novel integration algorithm is presented based on a continuous implicit surface representation. This is the first reconstruction algorithm to use operations in 3D space only. The algorithm is guaranteed to reconstruct the correct topology of surface features larger than the range image sampling resolution. Reconstruction of triangulated models from multi-image data sets is demonstrated for complex objects. Performance characterization of existing range image integration algorithms is addressed in the second part of this paper. This comparison defines the relative computational complexity and geometric limitations of existing integration algorithms.

The i3DPost Multi-View and 3D Human Action/Interaction Database

In this paper a new multi-view/3D human action/interaction database is presented. The database has been created using a convergent eight camera setup to produce high definition multi-view videos, where each video depicts one of eight persons performing one of twelve different human motions. Various types of motions have been recorded, i.e., scenes where one person performs a specific movement, scenes where a person executes different movements in a succession and scenes where two persons interact with each other. Moreover, the subjects have different body sizes, clothing and are of different sex, nationalities, etc.. The multi-view videos have been further processed to produce a 3D mesh at each frame describing the respective 3D human body surface. To increase the applicability of the database, for each person a multi-view video depicting the person performing sequentially the six basic facial expressions separated by the neutral expression has also been recorded. The database is freely available for research purposes.

Realistic synthesis of novel human movements from a database of motion capture examples

Presents a system that can synthesize novel motion sequences from a database of motion capture examples. This is achieved through learning a statistical model from the captured data which enables the realistic synthesis of new movements by sampling the original captured sequences. New movements are synthesized by specifying the start and end keyframes. The statistical model identifies segments of the original motion capture data to generate novel motion sequences between the keyframes. The advantage of this approach is that it combines the flexibility of keyframe animation with the realism of motion capture data.

Virtual people: capturing human models to populate virtual worlds

A new technique is introduced for automatically building recognisable moving 3D models of individual people. Realistic modelling of people is essential for advanced multimedia, augmented reality and immersive virtual reality. Current systems for whole-body model capture are based on active 3D sensing to measure the shape of the body surface. Such systems are prohibitively expensive and do not enable capture of high-quality photo-realistic colour. This results in geometrically accurate but unrealistic human models. The goal of this research is to achieve automatic low cost modelling of people suitable for personalised avatars to populate virtual worlds. A model based approach is presented for automatic reconstruction of recognisable avatars from a set of low cost colour images of a person taken from four orthogonal views. A generic 3D human model represents both the human shape and kinematic joint structure. The shape of a specific person is captured by mapping 2D silhouette information from the orthogonal view colour images onto the generic 3D model. Colour texture mapping is achieved by projecting the set of images onto the deformed 3D model. This results in the capture of a recognisable 3D facsimile of an individual person suitable for articulated movement in a virtual world. The system is low cost, requires single shot capture, is reliable for large variations in shape and size and can cope with clothing of moderate complexity.

Registration of multiple point sets

Registering 3D point sets subject to rigid body motion is a common problem in computer vision. The optimal transformation is usually specified to be the minimum of a weighted least squares cost. The case of 2 point sets has been solved by several authors using analytic methods such as SVD. In this paper we present a numerical method for solving the problem when there are more than 2 point sets. Although of general applicability the new method is particularly aimed at the multiview surface registration problem. To date almost all authors have registered only two point sets at a time. This approach discards information and we show in quantitative terms the errors caused.

Correspondence labelling for wide-timeframe free-form surface matching

This paper addresses the problem of estimating dense correspondence between arbitrary frames from captured sequences of shape and appearance for surfaces undergoing free-form deformation. Previous techniques require either a prior model, limiting the range of surface deformations, or frame-to-frame surface tracking which suffers from stabilisation problems over complete motion sequences and does not provide correspondence between sequences. The primary contribution of this paper is the introduction of a system for wide-timeframe surface matching without the requirement for a prior model or tracking. Deformation- invariant surface matching is formulated as a locally isometric mapping at a discrete set of surface points. A set of feature descriptors are presented that are invariant to isometric deformations and a novel MAP-MRF framework is presented to label sparse-to-dense surface correspondence, preserving the relative distribution of surface features while allowing for changes in surface topology. Performance is evaluated on challenging data from a moving person with loose clothing. Ground-truth feature correspondences are manually marked and the recall-accuracy characteristic is quantified in matching. Results demonstrate an improved performance compared to non-rigid point-pattern matching using robust matching and graph-matching using relaxation labelling, with successful matching achieved across wide variations in human body pose and surface topology.

Whole-body modelling of people from multiview images to populate virtual worlds

In this paper a new technique is introduced for automatically building recognisable, moving 3D models of individual people. A set of multiview colour images of a person is captured from the front, sides and back by one or more cameras. Model-based reconstruction of shape from silhouettes is used to transform a standard 3D generic humanoid model to approximate a persons shape and anatomical structure. Realistic appearance is achieved by colour texture mapping from the multiview images. The results show the reconstruction of a realistic 3D facsimile of the person suitable for animation in a virtual world. The system is inexpensive and is reliable for large variations in shape, size and clothing. This is the first approach to achieve realistic model capture for clothed people and automatic reconstruction of animated models. A commercial system based on this approach has recently been used to capture thousands of models of the general public.

Shape Similarity for 3D Video Sequences of People

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 twenty-eight 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.

Visual Analysis of Humans

This unique text/reference provides a coherent and comprehensive overview of all aspects of video analysis of humans. Broad in coverage and accessible in style, the text presents original perspectives collected from preeminent researchers gathered from across the world. In addition to presenting state-of-the-art research, the book reviews the historical origins of the different existing methods, and predicts future trends and challenges. Features: with a Foreword by Professor Larry Davis; contains contributions from an international selection of leading authorities in the field; includes an extensive glossary; discusses the problems associated with detecting and tracking people through camera networks; examines topics related to determining the time-varying 3D pose of a person from video; investigates the representation and recognition of human and vehicular actions; reviews the most important applications of activity recognition, from biometrics and surveillance, to sports and driver assistance.