Co-speech gesture generation is crucial for creating lifelike avatars and enhancing human-computer interactions by synchronizing gestures with speech. Despite recent advancements, existing methods struggle with accurately identifying the rhythmic or semantic triggers from audio for generating contextualized gesture patterns and achieving pixel-level realism. To address these challenges, we introduce Contextual Gesture, a framework that improves co-speech gesture video generation through three innovative components: (1) a chronological speech-gesture alignment that temporally connects two modalities, (2) a contextualized gesture tokenization that incorporate speech context into motion pattern representation through distillation, and (3) a structure-aware refinement module that employs edge connection to link gesture keypoints to improve video generation. Our extensive experiments demonstrate that Contextual Gesture not only produces realistic and speech-aligned gesture videos but also supports long-sequence generation and video gesture editing applications, shown in Fig.1

Near-range portrait photographs often contain perspective distortion artifacts that bias human perception and challenge both facial recognition and reconstruction techniques. We present the first deep learning based approach to remove such artifacts from unconstrained portraits. In contrast to the previous state-of-the-art approach [23], our method handles even portraits with extreme perspective distortion, as we avoid the inaccurate and error-prone step of first fitting a 3D face model. Instead, we predict a distortion correction flow map that encodes a per-pixel displacement that removes distortion artifacts when applied to the input image. Our method also automatically infers missing facial features, i.e. occluded ears caused by strong perspective distortion, with coherent details. We demonstrate that our approach significantly outperforms the previous state-of-the-art [23] both qualitatively and quantitatively, particularly for portraits with extreme perspective distortion or facial expressions. We further show that our technique benefits a number of fundamental tasks, significantly improving the accuracy of both face recognition and 3D reconstruction and enables a novel camera calibration technique from a single portrait. Moreover, we also build the first perspective portrait database with a large diversity in identities, expression and poses.

The child developmental period of ages 6-12 months marks a widely understood "critical period" for healthy language learning, during which, failure to receive exposure to language can place babies at risk for language and reading problems spanning life. Deaf babies constitute one vulnerable population as they can experience dramatically reduced or no access to usable linguistic input during this period. Technology has been used to augment linguistic input (e.g., auditory devices; language videotapes) but research finds limitations in learning. We evaluated an AI system that uses an Avatar (provides language and socially contingent interactions) and a robot (aids attention to the Avatar) to facilitate infants' ability to learn aspects of American Sign Language (ASL), and asked three questions: (1) Can babies with little/no exposure to ASL distinguish among the Avatar's different conversational modes (Linguistic Nursery Rhymes; Social Gestures; Idle/nonlinguistic postures; 3rd person observer)? (2) Can an Avatar stimulate babies' production of socially contingent responses, and crucially, nascent language responses? (3) What is the impact of parents' presence/absence of conversational participation? Surprisingly, babies (i) spontaneously distinguished among Avatar conversational modes, (ii) produced varied socially contingent responses to Avatar's modes, and (iii) parents influenced an increase in babies' response tokens to some Avatar modes, but the overall categories and pattern of babies' behavioral responses remained proportionately similar irrespective of parental participation. Of note, babies produced the greatest percentage of linguistic responses to the Avatar's Linguistic Nursery Rhymes versus other Avatar conversational modes. This work demonstrates the potential for Avatars to facilitate language learning in young babies.

Children with insufficient exposure to language during critical developmental periods in infancy are at risk for cognitive, language, and social deficits [55]. This is especially difficult for deaf infants, as more than 90% are born to hearing parents with little sign language experience [48]. We created an integrated multi-agent system involving a robot and virtual human designed to augment language exposure for 6-12 month old infants. Human-machine design for infants is challenging, as most screen-based media are unlikely to support learning in [33]. While presently, robots are incapable of the dexterity and expressiveness required for signing, even if it existed, developmental questions remain about the capacity for language from artificial agents to engage infants. Here we engineered the robot and avatar to provide visual language to effect socially contingent human conversational exchange. We demonstrate the successful engagement of our technology through case studies of deaf and hearing infants.

A crucial aspect of virtual gaming experiences is the avatar: the player's virtual self-representation. While research has demonstrated benefits to using self-similar avatars in some virtual experiences, such avatars sometimes produce a more negative experience for women. To help researchers and game designers assess the cost-benefit tradeoffs of self-similar avatars, we compared players' performance and subjective experience in a search and rescue computer game when using two different photorealistic avatars: their own self or a friend, and when playing either a social (rescuing people) or a nonsocial (rescuing gems) version of the game. There was no effect of avatar appearance on players' performance or subjective experience in either game version, but we also found that women's experience with self-similar avatars was no more negative than men's. Our results suggest that avatar appearance may not make a difference to players in certain game contexts.

Digital doppelgangers are virtual humans that highly resemble the real self but behave independently. Digital doppelgangers possess great potential to serve as powerful models for behavioral change. An emerging technology, the Rapid Avatar Capture and Simulation (RACAS) system, enables low-cost and high-speed scanning of a human user and creation of a digital doppelganger that is a fully animatable virtual 3D model of the user. We designed a virtual role-playing game, DELTA, that implements a powerful cognitive dissonance-based paradigm for attitudinal and behavioral change, and integrated it with digital doppelgangers to influence a human user's attitude towards sexism on college campuses. In this paper, we discuss the design and evaluation the RACAS system and the DELTA game-based environment. Results indicate the potential impact of the DELTA game-based environment in creating an immersive virtual experience for attitudinal change.

Abstract We demonstrate a system that can generate a photorealistic, interactive 3‐D character from a human subject that is capable of movement, emotion, speech, and gesture in less than 20 min without the need for 3‐D artist intervention or specialized technical knowledge through a near automatic process. Our method uses mostly commodity or off‐the‐shelf hardware. We demonstrate the just‐in‐time use of generating such 3‐D models for virtual and augmented reality, games, simulation, and communication. We anticipate that the inexpensive generation of such photorealistic models will be useful in many venues where a just‐in‐time 3‐D reconstructions of digital avatars that resemble particular human subjects is necessary.

Abstract Current 3D capture and modeling technology can rapidly generate highly photo‐realistic 3D avatars of human subjects. However, while the avatars look like their human counterparts, their movements often do not mimic their own due to existing challenges in accurate motion capture and retargeting. A better understanding of factors that influence the perception of biological motion would be valuable for creating virtual avatars that capture the essence of their human subjects. To investigate these issues, we captured 22 subjects walking in an open space. We then performed a study where participants were asked to identify their own motion in varying visual representations and scenarios. Similarly, participants were asked to identify the motion of familiar individuals. Unlike prior studies that used captured footage with simple “point‐light” displays, we rendered the motion on photo‐realistic 3D virtual avatars of the subject. We found that self‐recognition was significantly higher for virtual avatars than with point‐light representations. Users were more confident of their responses when identifying their motion presented on their virtual avatar. Recognition rates varied considerably between motion types for recognition of others, but not for self‐recognition. Overall, our results are consistent with previous studies that used recorded footage and offer key insights into the perception of motion rendered on virtual avatars.

Creating and animating realistic 3D human faces is an important element of virtual reality, video games, and other areas that involve interactive 3D graphics. In this paper, we propose a system to generate photorealistic 3D blendshape-based face models automatically using only a single consumer RGB-D sensor. The capture and processing requires no artistic expertise to operate, takes 15 seconds to capture and generate a single facial expression, and approximately 1 minute of processing time per expression to transform it into a blendshape model. Our main contributions include a complete end-to-end pipeline for capturing and generating photorealistic blendshape models automatically and a registration method that solves dense correspondences between two face scans by utilizing facial landmarks detection and optical flows. We demonstrate the effectiveness of the proposed method by capturing different human subjects with a variety of sensors and puppeteering their 3D faces with real-time facial performance retargeting. The rapid nature of our method allows for just-in-time construction of a digital face. To that end, we also integrated our pipeline with a virtual reality facial performance capture system that allows dynamic embodiment of the generated faces despite partial occlusion of the user's real face by the head-mounted display.

This study investigates presentation techniques for a chatbased virtual human that communicates engagingly with users via a smartphone outside of the lab in natural settings. Our work compares the responses of users who interact with an animated 3D virtual character as opposed to a real human video character capable of displaying backchannel behaviors. The findings of our study demonstrate that people are socially attracted to a 3D animated character that does not display backchannel behaviors more than a real human video character that presents realistic backchannel behaviors. People engage in conversation more by talking for a longer amount of time when they interact with a 3D animated virtual human that exhibits backchannel behaviors, compared to communicating with a real human video character that does not display backchannel behaviors.

This study explores presentation techniques for a chat-based virtual human that communicates engagingly with users. Interactions with the virtual human occur via a smartphone outside of the lab in natural settings. Our work compares the responses of users who interact with an animated virtual character as opposed to a real human video character capable of displaying realistic backchannel behaviors. An audio-only interface is compared additionally with the two types of characters. The findings of our study suggest that people are socially attracted to a 3D animated character that does not display backchannel behaviors more than a real human video character that presents realistic backchannel behaviors. People engage in conversation more by talking for a longer amount of time when they interact with a 3D animated virtual human that exhibits realistic backchannel behaviors, compared to communicating with a real human video character that does not display backchannel behaviors.

Recent advances in scanning technology have enabled the widespread capture of 3D character models based on human subjects. Intuition suggests that, with these new capabilities to create avatars that look like their users, every player should have his or her own avatar to play video games or simulations. We explicitly test the impact of having one's own avatar (vs. a yoked control avatar) in a simulation (i.e., maze running task with mines). We test the impact of avatar identity on both subjective (e.g., feeling connected and engaged, liking avatar's appearance, feeling upset when avatar's injured, enjoying the game) and behavioral variables (e.g., time to complete task, speed, number of mines triggered, riskiness of maze path chosen). Results indicate that having an avatar that looks like the user improves their subjective experience, but there is no significant effect on how users perform in the simulation.

We present a novel interactive approach, PedVR, to generate plausible behaviors for a large number of virtual humans, and to enable natural interaction between the real user and virtual agents. Our formulation is based on a coupled approach that combines a 2D multi-agent navigation algorithm with 3D human motion synthesis. The coupling can result in plausible movement of virtual agents and can generate gazing behaviors, which can considerably increase the believability. We have integrated our formulation with the DK-2 HMD and demonstrate the benefits of our crowd simulation algorithm over prior decoupled approaches. Our user evaluation suggests that the combination of coupled methods and gazing behavior can considerably increase the behavioral plausibility.

This study explores presentation techniques for a 3D animated chat-based virtual human that communicates engagingly with users. Interactions with the virtual human occur via a smartphone outside of the lab in natural settings. Our work compares the responses of users who interact with no image or a static image of a virtual character as opposed to the animated visage of a virtual human capable of displaying appropriate nonverbal behavior. We further investigate users' responses to the animated character's gaze aversion which displayed the character's act of looking away from users and was presented as a listening behavior. The findings of our study demonstrate that people tend to engage in conversation more by talking for a longer amount of time when they interact with a 3D animated virtual human that averts its gaze, compared to an animated virtual human that does not avert its gaze, a static image of a virtual character, or an audio-only interface.

Good public speaking skills convey strong and effective communication, which is critical in many professions and used in everyday life. The ability to speak publicly requires a lot of training and practice. Recent technological developments enable new approaches for public speaking training that allow users to practice in a safe and engaging environment. We explore feedback strategies for public speaking training that are based on an interactive virtual audience paradigm. We investigate three study conditions: (1) a non-interactive virtual audience (control condition), (2) direct visual feedback, and (3) nonverbal feedback from an interactive virtual audience. We perform a threefold evaluation based on self-assessment questionnaires, expert assessments, and two objectively annotated measures of eye-contact and avoidance of pause fillers. Our experiments show that the interactive virtual audience brings together the best of both worlds: increased engagement and challenge as well as improved public speaking skills as judged by experts.

3D scans of human figures have become widely available through online marketplaces and have become relatively easy to acquire using commodity scanning hardware. In addition to static uses of such 3D models, such as 3D printed figurines or rendered 3D still imagery, there are numerous uses for an animated 3D character that uses such 3D scan data. In order to effectively use such models as dynamic 3D characters, the models must be properly rigged before they are animated. In this work, we demonstrate a method to automatically rig a 3D mesh by matching a set of morphable models against the 3D scan. Once the morphable model has been matched against the 3D scan, the skeleton position and skinning attributes are then copied, resulting in a skinning and rigging that is similar in quality to the original hand-rigged model. In addition, the use of a morphable model allows us to reshape and resize the 3D scan according to approximate human proportions. Thus, a human 3D scan can be modified to be taller, shorter, fatter or skinnier. Such manipulations of the 3D scan are useful both for social science research, as well as for visualization for applications such as fitness, body image, plastic surgery and the like.

Creating and animating a realistic 3D human face has been an important task in computer graphics. The capability of capturing the 3D face of a human subject and reanimate it quickly will find many applications in games, training simulations, and interactive 3D graphics. In this paper, we propose a system to capture photorealistic 3D faces and generate the blendshape models automatically using only a single commodity RGB-D sensor. Our method can rapidly generate a set of expressive facial poses from a single Microsoft Kinect and requires no artistic expertise on the part of the capture subject. The system takes only a matter of seconds to capture and produce a 3D facial pose and only requires 4 minutes of processing time to transform it into a blendshape model. Our main contributions include an end-to-end pipeline for capturing and generating face blendshape models automatically, and a registration method that solves dense correspondences between two face scans by utilizing facial landmark detection and optical flow. We demonstrate the effectiveness of the proposed method by capturing 3D facial models of different human subjects and puppeteering their models in an animation system with real-time facial performance retargeting.

Recent advances in scanning technology have enabled the widespread capture of 3D character models based on human subjects. However, in order to generate a recognizable 3D avatar, the movement and behavior of the human subject should be captured and replicated as well. We present a method of generating a 3D model from a scan, as well as a method to incorporate a subjects style of gesturing into a 3D character. We present a study which shows that 3D characters that used the gestural style as their original human subjects were more recognizable as the original subject than those that don't.

We present preliminary results of a framework that can synthesize parameterized locomotion with controllable quality from simple deformations over a single step cycle. Our approach enforces feet constraints per phase in order to appropriately perform motion deformation operations, resulting in a generative and controllable model that maintains the style of the input motion. The method is lightweight and has quantifiable motion quality related to the amount of deformation used. It only requires a single cycle of locomotion. An analysis of the deformation is presented with the quantification of the valid portion of the deformed motion space, informing on the parameterization coverage of the deformable motion cycle.

The USC Institute for Creative Technologies will demonstrate a pipline for automatic reconstruction and animation of lifelike 3D avatars acquired by rotating the user's body in front of a single Microsoft Kinect sensor. Based on a fusion of state-of-the-art techniques in computer vision, graphics, and animation, this approach can produce a fully rigged character model suitable for real-time virtual environments in less than four minutes.

ABSTRACT Humanoid three‐dimensional (3D) models can be easily acquired through various sources, including through online marketplaces. The use of such models within a game or simulation environment requires human input and intervention in order to associate such a model with a relevant set of motions and control mechanisms. In this paper, we demonstrate a pipeline where humanoid 3D models can be incorporated within seconds into an animation system and infused with a wide range of capabilities, such as locomotion, object manipulation, gazing, speech synthesis and lip syncing. We offer a set of heuristics that can associate arbitrary joint names with canonical ones and describe a fast retargeting algorithm that enables us to instill a set of behaviors onto an arbitrary humanoid skeleton on‐the‐fly. We believe that such a system will vastly increase the use of 3D interactive characters due to the ease that new models can be animated.Copyright © 2013 John Wiley & Sons, Ltd.

ABSTRACT We demonstrate a method of acquiring a 3D model of a human using commodity scanning hardware and then controlling that 3D figure in a simulated environment in only a few minutes. The model acquisition requires four static poses taken at 90° angles relative to each other. The 3D model is then given a skeleton and smooth binding information necessary for control and simulation. The 3D models that are captured are suitable for use in applications where recognition and distinction among characters by shape, form, or clothing is important, such as small group or crowd simulations or other socially oriented applications. Because of the speed at which a human figure can be captured and the low hardware requirements, this method can be used to capture, track, and model human figures as their appearances change over time. Copyright © 2014 John Wiley & Sons, Ltd.

We demonstrate a lip animation (lip sync) algorithm for real-time applications that can be used to generate synchronized facial movements with audio generated from natural speech or a text-to-speech engine. Our method requires an animator to construct animations using a canonical set of visemes for all pairwise combinations of a reduced phoneme set (phone bigrams). These animations are then stitched together to construct the final animation, adding velocity and lip-pose constraints. This method can be applied to any character that uses the same, small set of visemes. Our method can operate efficiently in multiple languages by reusing phone bigram animations that are shared among languages, and specific word sounds can be identified and changed on a per-character basis. Our method uses no machine learning, which offers two advantages over techniques that do: 1) data can be generated for non-human characters whose faces can not be easily retargeted from a human speaker's face, and 2) the specific facial poses or shapes used for animation can be specified during the setup and rigging stage, and before the lip animation stage, thus making it suitable for game pipelines or circumstances where the speech targets poses are predetermined, such as after acquisition from an online 3D marketplace.

Synchronizing the lip and mouth movements naturally along with animation is an important part of convincing 3D character performance. We present a simple, portable and editable lip-synchronization method that works for multiple languages, requires no machine learning, can be constructed by a skilled animator, runs in real time, and can be personalized for each character. Our method associates animation curves designed by an animator on a fixed set of static facial poses, with sequential pairs of phonemes (diphones), and then stitch the diphones together to create a set of curves for the facial poses. Diphone- and triphone-based methods have been explored in various previous works [Deng et al. 2006], often requiring machine learning. However, our experiments have shown that diphones are sufficient for producing high-quality lip syncing, and that longer sequences of phonemes are not necessary. Our experiments have shown that skilled animators can sufficiently generate the data needed for good quality results. Thus our algorithm does not need any specific rules about coarticulation, such as dominance functions [Cohen and Massaro 1993] or language rules. Such rules are implicit within the artist-produced data. In order to produce a tractable set of data, our method reduces the full set of 40 English phonemes to a smaller set of 21, which are then annotated by an animator. Once the full diphone set of animations has been generated, it can be reused for multiple characters. Each additional character requires a small set of eight static poses or blendshapes. In addition, each language requires a new set of diphones, although similar phonemes among languages can share the same diphone curves. We show how to reuse our English diphone set to adapt to a Mandarin diphone set.

We synthesize natural-looking locomotion, reaching and grasping for a virtual character in order to accomplish a wide range of movement and manipulation tasks in real time. Our virtual characters can move while avoiding obstacles, as well as manipulate arbitrarily shaped objects, regardless of height, location or placement in a virtual environment. Our characters can touch, reach and grasp objects while maintaining a high quality appearance. We demonstrate a system that combines these skills in an interactive setting suitable for interactive games and simulations.

Editing recorded motions to make them suitable for different sets of environmental constraints is a general and difficult open problem. In this paper we solve a significant part of this problem by modifying full-body motions with an interactive randomized motion planner. Our method is able to synthesize collision-free motions for specified linkages of multiple animated characters in synchrony with the characters' full-body motions. The proposed method runs at interactive speed for dynamic environments of realistic complexity. We demonstrate the effectiveness of our interactive motion editing approach with two important applications: (a) motion correction (to remove collisions) and (b) synthesis of realistic object manipulation sequences on top of locomotion.

We implement a framework for animating interactive characters by combining kinematic animation with physical simulation. The combination of animation techniques allows the characters to exploit the advantages of each technique. For example, characters can perform natural-looking kinematic gaits and react dynamically to unexpected situations. Kinematic techniques such as those based on motion capture data can create very natural-looking animation. However, motion capture based techniques are not suitable for modeling the complex interactions between dynamically interacting characters. Physical simulation, on the other hand, is well suited for such tasks. Our work develops kinematic and dynamic controllers and transition methods between the two control methods for interactive character animation. In addition, we utilize the motion graph technique to develop complex kinematic animation from shorter motion clips as a method of kinematic control.

No abstract available.