Human speech is often accompanied by hand and arm gestures. Given audio speech input, we generate plausible gestures to go along with the sound. Specifically, we perform cross-modal translation from "in-the-wild'' monologue speech of a single speaker to their hand and arm motion. We train on unlabeled videos for which we only have noisy pseudo ground truth from an automatic pose detection system. Our proposed model significantly outperforms baseline methods in a quantitative comparison. To support research toward obtaining a computational understanding of the relationship between gesture and speech, we release a large video dataset of person-specific gestures. The project website with video, code and data can be found at this http URL .
More closely related to our approach are methods that learn gestures from speech and text, without requiring an author to hand-specify rules. Notably, @cite_32 synthesized gestures using natural language processing of spoken text, and Neff @cite_25 proposed a system for making person-specific gestures. Levine al @cite_22 learned to map acoustic prosody features to motion using a HMM. Later work @cite_37 extended this approach to use reinforcement learning and speech recognition, combined acoustic analysis with text @cite_17 , created hybrid rule-based systems @cite_5 , and used restricted Boltzmann machines for inference @cite_9 . Since the goal of these methods is to generate motions for virtual agents, they use lab-recorded audio, text, and motion capture. This allows them to use simplifying assumptions that present challenges for in-the-wild video analysis like ours: , @cite_22 requires precise 3D pose and assumes that motions occur on syllable boundaries, and @cite_9 assumes that gestures are initiated by an upward motion of the wrist. In contrast with these methods, our approach does not explicitly use any text or language information during training---it learns gestures from raw audio-visual correspondences---nor does it use hand-defined gesture categories: arm hand pose are predicted directly from audio.
@cite_37 @cite_22 @cite_9 @cite_32 @cite_5 @cite_25 @cite_17
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We demonstrate a method for generating a 3D virtual character performance from the audio signal by inferring the acoustic and semantic properties of the utterance. Through a prosodic analysis of the acoustic signal, we perform an analysis for stress and pitch, relate it to the spoken words and identify the agitation state. Our rule-based system performs a shallow analysis of the utterance text to determine its semantic, pragmatic and rhetorical content. Based on these analyses, the system generates facial expressions and behaviors including head movements, eye saccades, gestures, blinks and gazes. Our technique is able to synthesize the performance and generate novel gesture animations based on coarticulation with other closely scheduled animations. Because our method utilizes semantics in addition to prosody, we are able to generate virtual character performances that are more appropriate than methods that use only prosody. We perform a study that shows that our technique outperforms methods that use prosody alone. In this paper, we propose an HMM trajectory-guided, real image sample concatenation approach to photo-realistic talking head synthesis. An audio-visual database of a person is recorded first for training a statistical Hidden Markov Model (HMM) of Lips movement. The HMM is then used to generate the dynamic trajectory of lips movement for given speech signals in the maximum probability sense. The generated trajectory is then used as a guide to select, from the original training database, an optimal sequence of lips images which are then stitched back to a background head video. We also propose a minimum generation error (MGE) training method to refine the audio-visual HMM to improve visual speech trajectory synthesis. Compared with the traditional maximum likelihood (ML) estimation, the proposed MGE training explicitly optimizes the quality of generated visual speech trajectory, where the audio-visual HMM modeling is jointly refined by using a heuristic method to find the optimal state alignment and a probabilistic descent algorithm to optimize the model parameters under the MGE criterion. In objective evaluation, compared with the ML-based method, the proposed MGE-based method achieves consistent improvement in the mean square error reduction, correlation increase, and recovery of global variance. For as short as 20 min recording of audio video footage, the proposed system can synthesize a highly photo-realistic talking head in sync with the given speech signals (natural or TTS synthesized). This system won the first place in the A V consistency contest in LIPS Challenge, perceptually evaluated by recruited human subjects. This paper presents a novel and real-time system for interaction with an application or video game via hand gestures. Our system includes detecting and tracking bare hand in cluttered background using skin detection and hand posture contour comparison algorithm after face subtraction, recognizing hand gestures via bag-of-features and multiclass support vector machine (SVM) and building a grammar that generates gesture commands to control an application. In the training stage, after extracting the keypoints for every training image using the scale invariance feature transform (SIFT), a vector quantization technique will map keypoints from every training image into a unified dimensional histogram vector (bag-of-words) after K-means clustering. This histogram is treated as an input vector for a multiclass SVM to build the training classifier. In the testing stage, for every frame captured from a webcam, the hand is detected using our algorithm, then, the keypoints are extracted for every small image that contains the detected hand gesture only and fed into the cluster model to map them into a bag-of-words vector, which is finally fed into the multiclass SVM training classifier to recognize the hand gesture. This paper proposes a novel representation of articulated human actions and gestures and facial expressions. The main goals of the proposed approach are: 1) to enable recognition using very few examples, i.e., one or k-shot learning, and 2) meaningful organization of unlabeled datasets by unsupervised clustering. Our proposed representation is obtained by automatically discovering high-level subactions or motion primitives, by hierarchical clustering of observed optical flow in four-dimensional, spatial, and motion flow space. The completely unsupervised proposed method, in contrast to state-of-the-art representations like bag of video words, provides a meaningful representation conducive to visual interpretation and textual labeling. Each primitive action depicts an atomic subaction, like directional motion of limb or torso, and is represented by a mixture of four-dimensional Gaussian distributions. For one--shot and k-shot learning, the sequence of primitive labels discovered in a test video are labeled using KL divergence, and can then be represented as a string and matched against similar strings of training videos. The same sequence can also be collapsed into a histogram of primitives or be used to learn a Hidden Markov model to represent classes. We have performed extensive experiments on recognition by one and k-shot learning as well as unsupervised action clustering on six human actions and gesture datasets, a composite dataset, and a database of facial expressions. These experiments confirm the validity and discriminative nature of the proposed representation.