28 research outputs found
Efficiency of using recombinant morphogenentic protein in patients with aggressive (rapidly-progressing) generalized periodontis
Annotation. Currently, the inclusion in the complex therapy of generalized periodontitis of
drugs that significantly affect the processes of physiological remodeling and bone regeneration
is becoming more common.
Given the relevance of the search for drugs to restore periodontal bone structures, the
aim of our study was to increase the effectiveness of standard therapy for aggressive (rapidly
progressing) generalized periodontitis by additionally incorporating the morphogenetic bone
protein rhbmp-2 into the generally accepted treatment complex.
We observed a contingent of patients in the amount of 61 people, with a diagnosis of
aggressive (rapidly progressing) generalized periodontitis, which were divided into the main -
30 people, and the comparison group - 31 people. we used standard clinical, paraclinical and
laboratory research methods, supplemented by dental volumetric tomography. In the main
group, patients in addition to the standard treatment regimen (comparison group) included the
recombinant morphogenetic protein rhbmp-2.
The results of a clinical examination conducted after 6-12 months revealed the absence of
inflammatory phenomena in periodontium in 90% of the main group, and only 77.4% of patients
in the comparison group. Measurements of bone density on the hounsfield scale (hu) in the same
period showed a 2-fold increase in the density of periodontal bone structures, with a comparison
group. it follows that the inclusion in the standard regimen of complex treatment for patients with
rapidly progressing generalized periodontitis of the osteoinductive drug rhbmp-2 allows for longterm clinical and radiological remission, and creates conditions for the subsequent restoration of
the density of periodontal bone structures
Analyzing Input and Output Representations for Speech-Driven Gesture Generation
This paper presents a novel framework for automatic speech-driven gesture
generation, applicable to human-agent interaction including both virtual agents
and robots. Specifically, we extend recent deep-learning-based, data-driven
methods for speech-driven gesture generation by incorporating representation
learning. Our model takes speech as input and produces gestures as output, in
the form of a sequence of 3D coordinates. Our approach consists of two steps.
First, we learn a lower-dimensional representation of human motion using a
denoising autoencoder neural network, consisting of a motion encoder MotionE
and a motion decoder MotionD. The learned representation preserves the most
important aspects of the human pose variation while removing less relevant
variation. Second, we train a novel encoder network SpeechE to map from speech
to a corresponding motion representation with reduced dimensionality. At test
time, the speech encoder and the motion decoder networks are combined: SpeechE
predicts motion representations based on a given speech signal and MotionD then
decodes these representations to produce motion sequences. We evaluate
different representation sizes in order to find the most effective
dimensionality for the representation. We also evaluate the effects of using
different speech features as input to the model. We find that mel-frequency
cepstral coefficients (MFCCs), alone or combined with prosodic features,
perform the best. The results of a subsequent user study confirm the benefits
of the representation learning.Comment: Accepted at IVA '19. Shorter version published at AAMAS '19. The code
is available at
https://github.com/GestureGeneration/Speech_driven_gesture_generation_with_autoencode
A Comprehensive Review of Data-Driven Co-Speech Gesture Generation
Gestures that accompany speech are an essential part of natural and efficient
embodied human communication. The automatic generation of such co-speech
gestures is a long-standing problem in computer animation and is considered an
enabling technology in film, games, virtual social spaces, and for interaction
with social robots. The problem is made challenging by the idiosyncratic and
non-periodic nature of human co-speech gesture motion, and by the great
diversity of communicative functions that gestures encompass. Gesture
generation has seen surging interest recently, owing to the emergence of more
and larger datasets of human gesture motion, combined with strides in
deep-learning-based generative models, that benefit from the growing
availability of data. This review article summarizes co-speech gesture
generation research, with a particular focus on deep generative models. First,
we articulate the theory describing human gesticulation and how it complements
speech. Next, we briefly discuss rule-based and classical statistical gesture
synthesis, before delving into deep learning approaches. We employ the choice
of input modalities as an organizing principle, examining systems that generate
gestures from audio, text, and non-linguistic input. We also chronicle the
evolution of the related training data sets in terms of size, diversity, motion
quality, and collection method. Finally, we identify key research challenges in
gesture generation, including data availability and quality; producing
human-like motion; grounding the gesture in the co-occurring speech in
interaction with other speakers, and in the environment; performing gesture
evaluation; and integration of gesture synthesis into applications. We
highlight recent approaches to tackling the various key challenges, as well as
the limitations of these approaches, and point toward areas of future
development.Comment: Accepted for EUROGRAPHICS 202
Can we trust online crowdworkers? Comparing online and offline participants in a preference test of virtual agents
Conducting user studies is a crucial component in many scientific fields.
While some studies require participants to be physically present, other studies
can be conducted both physically (e.g. in-lab) and online (e.g. via
crowdsourcing). Inviting participants to the lab can be a time-consuming and
logistically difficult endeavor, not to mention that sometimes research groups
might not be able to run in-lab experiments, because of, for example, a
pandemic. Crowdsourcing platforms such as Amazon Mechanical Turk (AMT) or
Prolific can therefore be a suitable alternative to run certain experiments,
such as evaluating virtual agents. Although previous studies investigated the
use of crowdsourcing platforms for running experiments, there is still
uncertainty as to whether the results are reliable for perceptual studies. Here
we replicate a previous experiment where participants evaluated a gesture
generation model for virtual agents. The experiment is conducted across three
participant pools -- in-lab, Prolific, and AMT -- having similar demographics
across the in-lab participants and the Prolific platform. Our results show no
difference between the three participant pools in regards to their evaluations
of the gesture generation models and their reliability scores. The results
indicate that online platforms can successfully be used for perceptual
evaluations of this kind.Comment: Accepted to IVA 2020. Patrik Jonell and Taras Kucherenko contributed
equally to this wor
HEMVIP: Human Evaluation of Multiple Videos in Parallel
In many research areas, for example motion and gesture generation, objective
measures alone do not provide an accurate impression of key stimulus traits
such as perceived quality or appropriateness. The gold standard is instead to
evaluate these aspects through user studies, especially subjective evaluations
of video stimuli. Common evaluation paradigms either present individual stimuli
to be scored on Likert-type scales, or ask users to compare and rate videos in
a pairwise fashion. However, the time and resources required for such
evaluations scale poorly as the number of conditions to be compared increases.
Building on standards used for evaluating the quality of multimedia codecs,
this paper instead introduces a framework for granular rating of multiple
comparable videos in parallel. This methodology essentially analyses all
condition pairs at once. Our contributions are 1) a proposed framework, called
HEMVIP, for parallel and granular evaluation of multiple video stimuli and 2) a
validation study confirming that results obtained using the tool are in close
agreement with results of prior studies using conventional multiple pairwise
comparisons.Comment: 8 pages, 2 figure
Understanding the Predictability of Gesture Parameters from Speech and their Perceptual Importance
Gesture behavior is a natural part of human conversation. Much work has
focused on removing the need for tedious hand-animation to create embodied
conversational agents by designing speech-driven gesture generators. However,
these generators often work in a black-box manner, assuming a general
relationship between input speech and output motion. As their success remains
limited, we investigate in more detail how speech may relate to different
aspects of gesture motion. We determine a number of parameters characterizing
gesture, such as speed and gesture size, and explore their relationship to the
speech signal in a two-fold manner. First, we train multiple recurrent networks
to predict the gesture parameters from speech to understand how well gesture
attributes can be modeled from speech alone. We find that gesture parameters
can be partially predicted from speech, and some parameters, such as path
length, being predicted more accurately than others, like velocity. Second, we
design a perceptual study to assess the importance of each gesture parameter
for producing motion that people perceive as appropriate for the speech.
Results show that a degradation in any parameter was viewed negatively, but
some changes, such as hand shape, are more impactful than others. A video
summarization can be found at https://youtu.be/aw6-_5kmLjY.Comment: To be published in the Proceedings of the 20th ACM International
Conference on Intelligent Virtual Agents (IVA 20
The GENEA Challenge 2023: A large scale evaluation of gesture generation models in monadic and dyadic settings
This paper reports on the GENEA Challenge 2023, in which participating teams
built speech-driven gesture-generation systems using the same speech and motion
dataset, followed by a joint evaluation. This year's challenge provided data on
both sides of a dyadic interaction, allowing teams to generate full-body motion
for an agent given its speech (text and audio) and the speech and motion of the
interlocutor. We evaluated 12 submissions and 2 baselines together with
held-out motion-capture data in several large-scale user studies. The studies
focused on three aspects: 1) the human-likeness of the motion, 2) the
appropriateness of the motion for the agent's own speech whilst controlling for
the human-likeness of the motion, and 3) the appropriateness of the motion for
the behaviour of the interlocutor in the interaction, using a setup that
controls for both the human-likeness of the motion and the agent's own speech.
We found a large span in human-likeness between challenge submissions, with a
few systems rated close to human mocap. Appropriateness seems far from being
solved, with most submissions performing in a narrow range slightly above
chance, far behind natural motion. The effect of the interlocutor is even more
subtle, with submitted systems at best performing barely above chance.
Interestingly, a dyadic system being highly appropriate for agent speech does
not necessarily imply high appropriateness for the interlocutor. Additional
material is available via the project website at
https://svito-zar.github.io/GENEAchallenge2023/ .Comment: The first three authors made equal contributions. Accepted for
publication at the ACM International Conference on Multimodal Interaction
(ICMI