Clifford So

Reconstruction of 3D virtual buildings from 2D architectural floor plans

Visualizing architectural designs with t,he aid of Virtual Reality (VR) technology is becoming a common task among architects, design engineers, contractors and customers due to a more realistic resemblance to the real look of the final constructions. In order to preview virtual buildings, one needs to reconstruct a VR model from the two-dimensional architectural drawings. In the conventional approach, a large amount of manual tasks are necessary while using some common 3D authoring tools avaliable in the market. This gives us the incentive to examine the process of modelling and reconstruction of 2D designs for use in practical applications. In this paper, we identify the main tasks in the conventional reconstruction process and give a semi-aut,omated solution that aids in the process of ext,rusion and modelling of large building complexes.

Entropy-based motion extraction for motion capture animation

In this paper, we present a new segmentation solution for extracting motion patterns from motion capture data by searching for critical keyposes in the motion sequence. A rank is established for critical keyposes that identifies the significance of the directional change in motion data. The method is based on entropy metrics, specifically the mutual information measure. Displacement histograms between frames are evaluated and the mutual information metric is employed in order to calculate the inter‐frame dependency. The most significant keypose identifies the largest directional change in the motion data. This will have the lowest mutual information level from all the candidate keyposes. Less significant keyposes are then listed with higher mutual information levels. The results show that the method has higher sensitivity in the directional change than methods based on the magnitude of the velocity alone. This method is intended to provide a summary of a motion clip by ranked keyposes, which is highly useful in motion browsing and motion retrieve database system. Copyright

Entropy-based motion extraction for motion capture animation: Motion Capture and Retrieval

Not everything is perceived as it is provided by the environment. Depending on focus and attention perception can vary and therefore also the knowledge about the world. Virtual humans are sensing the virtual world, storing knowledge and using it to perform tasks. This paper describes our approach to model perceiving, storing and forgetting knowledge as the main regulation of tasks. We use different forms and levels of knowledge which can be independently adapted to different personalities and situations by combining computer graphics methods with psychological models. Copyright

Hypercube sweeping algorithm for subsequence motion matching in large motion databases

Current optical motion capture devices are capable of capturing motion at frequencies exceeding 1000Hz thereby generating gigabytes of motion data. In this paper we propose a method to solve the problem of subsequence motion matching in large motion databases. Our method supports non-uniform time-scaling. We begin with a polar-angle representation of the motion that gives a continuous thread in a multi-dimensional space. We improve the performance of the matching process by generating a motion curve index based on a representation of multiple 1-D signals rather than by partitioning the multi-dimensional space into subspaces as done in some previous work. Given a motion query, we sweep a hypercube along the query thread. Motion subsequences intersected by the hypercube form a matching set. Our method matches any possible non-uniform time-scaled subsequences between the query and the database, since any non-uniform time-scaled motion retains the same shape and location of the thread in the multi-dimensional space. We propose a new method to perform fast hypercube sweeping by utilizing a histogram. The histogram counts how many dimensions of each point on the thread are matched. A point is inside the hypercube when its count equals the total dimension d. The histogram is incrementally updated to minimize the sweeping cost. Our results show that the performance of our method depends on the speed of the query motion. We stress test our method by streaming the query motion against a motion database to determine its performance. The results show that the system can handle larger databases on slower query motion and vice versa.

Visualization of time-varying joint development of pitch and dynamics for speech emotion recognition

In this paper, we propose a new approach for visualizing the time-varying acoustic features for speech emotion recognition. Although the emotional state does not carry any linguistic information, it is a crucial factor that offers sentiment feedback to the listener. We propose to extract the two most prevalent acoustic features: pitch and dynamics, to identify the speech emotion of the speaker. We represent the time-varying pitch and dynamics as a trajectory in a two-dimensional feature space. Multiple trajectories are then segmented and clustered into signature patterns. This technique was successful in identifying and retargeting expressive musical performance styles. In evaluation, we use the German emotion language database. The database was created with ten professional actors (five males and five females) of ten emotionally unbiased sentences performed in six target emotions (Angry, Happy, Fear, Boredom, Sad, and Disgust). Results showed that the speech samples from the same actor of the same senten...

Visualising Singing Style under Common Musical Events Using Pitch-Dynamics Trajectories and Modified TRACLUS Clustering

We present a novel method for visualising the singing style of vocalists. To illustrate our method, we take 26 audio recordings of A cappella solo vocal music from two different professional singers and we visualise the performance style of each vocalist in a two-dimensional space of pitch and dynamics. We use our own novel modification of a trajectory clustering algorithm called TRACLUS to generate four representative paths, called trajectories, in that two dimensional space. Each trajectory represents the characteristic style of a vocalist during one of four common musical events: (1) Crescendo, (2) Diminuendo, (3) Ascending Pitches and (4) Descending Pitches. The unique shapes of these trajectories characterize the singing style of each vocalist with respect to each of these events. We present the details of our modified version of the TRACULUS algorithm and demonstrate graphically how the plots produced indicate distinct stylistic differences between singers. Potential applications for this method include: (a) automatic identification of singers and automatic classification of singing styles and (b) automatic retargeting of performance style to add human expression to computer generated vocal performances and allow singing synthesisers to imitate the styles of specific famous professional vocalists.