Realistic Lip Syncing for Virtual Character Using Common Viseme Set

Speech is one of the most important interaction methods between the humans. Therefore, most of avatar researches focus on this area with significant attention. Creating animated speech requires a facial model capable of representing the myriad shapes the human face expressions during speech. Moreover, a method to produce the correct shape at the correct time is also in order. One of the main challenges is to create precise lip movements of the avatar and synchronize it with a recorded audio. This paper proposes a new lip synchronization algorithm for realistic applications, which can be employed to generate synchronized facial movements among the audio generated from natural speech or through a text-to-speech engine. This method requires an animator to construct animations using a canonical set of visemes for all pair wise combination of a reduced phoneme set. These animations are then stitched together smoothly to construct the final animation

Lip syncing method for realistic expressive three-dimensional face model

Lip synchronization of 3D face model is now being used in a multitude of important fields. It brings a more human and dramatic reality to computer games, films and interactive multimedia, and is growing in use and importance. High level realism can be used in demanding applications such as computer games and cinema. Authoring lip syncing with complex and subtle expressions is still difficult and fraught with problems in terms of realism. Thus, this study proposes a lip syncing method of realistic expressive 3D face model. Animated lips require a 3D face model capable of representing the movement of face muscles during speech and a method to produce the correct lip shape at the correct time. The 3D face model is designed based on MPEG-4 facial animation standard to support lip syncing that is aligned with input audio file. It deforms using Raised Cosine Deformation function that is grafted onto the input facial geometry. This study also proposes a method to animate the 3D face model over time to create animated lip syncing using a canonical set of visemes for all pairwise combinations of a reduced phoneme set called ProPhone. Finally, this study integrates emotions by considering both Ekman model and Plutchik’s wheel with emotive eye movements by implementing Emotional Eye Movements Markup Language to produce realistic 3D face model. The experimental results show that the proposed model can generate visually satisfactory animations with Mean Square Error of 0.0020 for neutral, 0.0024 for happy expression, 0.0020 for angry expression, 0.0030 for fear expression, 0.0026 for surprise expression, 0.0010 for disgust expression, and 0.0030 for sad expression

Hashing for Multimedia Similarity Modeling and Large-Scale Retrieval

In recent years, the amount of multimedia data such as images, texts, and videos have been growing rapidly on the Internet. Motivated by such trends, this thesis is dedicated to exploiting hashing-based solutions to reveal multimedia data correlations and support intra-media and inter-media similarity search among huge volumes of multimedia data. We start by investigating a hashing-based solution for audio-visual similarity modeling and apply it to the audio-visual sound source localization problem. We show that synchronized signals in audio and visual modalities demonstrate similar temporal changing patterns in certain feature spaces. We propose to use a permutation-based random hashing technique to capture the temporal order dynamics of audio and visual features by hashing them along the temporal axis into a common Hamming space. In this way, the audio-visual correlation problem is transformed into a similarity search problem in the Hamming space. Our hashing-based audio-visual similarity modeling has shown superior performances in the localization and segmentation of sounding objects in videos. The success of the permutation-based hashing method motivates us to generalize and formally define the supervised ranking-based hashing problem, and study its application to large-scale image retrieval. Specifically, we propose an effective supervised learning procedure to learn optimized ranking-based hash functions that can be used for large-scale similarity search. Compared with the randomized version, the optimized ranking-based hash codes are much more compact and discriminative. Moreover, it can be easily extended to kernel space to discover more complex ranking structures that cannot be revealed in linear subspaces. Experiments on large image datasets demonstrate the effectiveness of the proposed method for image retrieval. We further studied the ranking-based hashing method for the cross-media similarity search problem. Specifically, we propose two optimization methods to jointly learn two groups of linear subspaces, one for each media type, so that features\u27 ranking orders in different linear subspaces maximally preserve the cross-media similarities. Additionally, we develop this ranking-based hashing method in the cross-media context into a flexible hashing framework with a more general solution. We have demonstrated through extensive experiments on several real-world datasets that the proposed cross-media hashing method can achieve superior cross-media retrieval performances against several state-of-the-art algorithms. Lastly, to make better use of the supervisory label information, as well as to further improve the efficiency and accuracy of supervised hashing, we propose a novel multimedia discrete hashing framework that optimizes an instance-wise loss objective, as compared to the pairwise losses, using an efficient discrete optimization method. In addition, the proposed method decouples the binary codes learning and hash function learning into two separate stages, thus making the proposed method equally applicable for both single-media and cross-media search. Extensive experiments on both single-media and cross-media retrieval tasks demonstrate the effectiveness of the proposed method