24,491 research outputs found
Visualisation techniques, human perception and the built environment
Historically, architecture has a wealth of visualisation techniques that have evolved throughout the period of structural design, with Virtual Reality (VR) being a relatively recent addition to the toolbox. To date the effectiveness of VR has been demonstrated from conceptualisation through to final stages and maintenance, however, its full potential has yet to be realised (Bouchlaghem et al, 2005). According to Dewey (1934), perceptual integration was predicted to be transformational; as the observer would be able to ‘engage’ with the virtual environment. However, environmental representations are predominately focused on the area of vision, regardless of evidence stating that the experience is multi sensory. In addition, there is a marked lack of research exploring the complex interaction of environmental design and the user, such as the role of attention or conceptual interpretation. This paper identifies the potential of VR models to aid communication for the Built Environment with specific reference to human perception issues
How instructions modify perception: An fMRI study investigating brain areas involved in attributing human agency
Behavioural studies suggest that the processing of movement stimuli is influenced by beliefs about the agency behind these actions. The current study examined how activity in social and action related brain areas differs when participants were instructed that identicalmovement stimuli were either human or computer generated.Participants viewed a series of point-light animation figures derived frommotion-capture recordings of amoving actor, while functional magnetic resonance imaging (fMRI) was used to monitor patterns of neural activity. The stimuli were scrambled to produce a range of stimulus realism categories; furthermore, before each trial participants were told that they were about to view either a recording of human movement or a computersimulated pattern of movement. Behavioural results suggested that agency instructions influenced participants' perceptions of the stimuli. The fMRI analysis indicated different functions within the paracingulate cortex: ventral paracingulate cortex was more active for human compared to computer agency instructed trials across all stimulus types, whereas dorsal paracingulate cortex was activated more highly in conflicting conditions (human instruction, lowrealismor vice versa). These findings support the hypothesis that ventral paracingulate encodes stimuli deemed to be of human origin,whereas dorsal paracingulate cortex is involvedmore in the ascertainment of human or intentional agency during the observation of ambiguous stimuli. Our results highlight the importance of prior instructions or beliefs on movement processing and the role of the paracingulate cortex in
integrating prior knowledge with bottom-up stimuli
On combining the facial movements of a talking head
We present work on Obie, an embodied conversational
agent framework. An embodied conversational agent, or
talking head, consists of three main components. The
graphical part consists of a face model and a facial muscle
model. Besides the graphical part, we have implemented
an emotion model and a mapping from emotions to facial
expressions. The animation part of the framework focuses
on the combination of different facial movements
temporally. In this paper we propose a scheme of
combining facial movements on a 3D talking head
A framework for realistic 3D tele-immersion
Meeting, socializing and conversing online with a group of people using teleconferencing systems is still quite differ- ent from the experience of meeting face to face. We are abruptly aware that we are online and that the people we are engaging with are not in close proximity. Analogous to how talking on the telephone does not replicate the experi- ence of talking in person. Several causes for these differences have been identified and we propose inspiring and innova- tive solutions to these hurdles in attempt to provide a more realistic, believable and engaging online conversational expe- rience. We present the distributed and scalable framework REVERIE that provides a balanced mix of these solutions. Applications build on top of the REVERIE framework will be able to provide interactive, immersive, photo-realistic ex- periences to a multitude of users that for them will feel much more similar to having face to face meetings than the expe- rience offered by conventional teleconferencing systems
Shape Animation with Combined Captured and Simulated Dynamics
We present a novel volumetric animation generation framework to create new
types of animations from raw 3D surface or point cloud sequence of captured
real performances. The framework considers as input time incoherent 3D
observations of a moving shape, and is thus particularly suitable for the
output of performance capture platforms. In our system, a suitable virtual
representation of the actor is built from real captures that allows seamless
combination and simulation with virtual external forces and objects, in which
the original captured actor can be reshaped, disassembled or reassembled from
user-specified virtual physics. Instead of using the dominant surface-based
geometric representation of the capture, which is less suitable for volumetric
effects, our pipeline exploits Centroidal Voronoi tessellation decompositions
as unified volumetric representation of the real captured actor, which we show
can be used seamlessly as a building block for all processing stages, from
capture and tracking to virtual physic simulation. The representation makes no
human specific assumption and can be used to capture and re-simulate the actor
with props or other moving scenery elements. We demonstrate the potential of
this pipeline for virtual reanimation of a real captured event with various
unprecedented volumetric visual effects, such as volumetric distortion,
erosion, morphing, gravity pull, or collisions
Capture, Learning, and Synthesis of 3D Speaking Styles
Audio-driven 3D facial animation has been widely explored, but achieving
realistic, human-like performance is still unsolved. This is due to the lack of
available 3D datasets, models, and standard evaluation metrics. To address
this, we introduce a unique 4D face dataset with about 29 minutes of 4D scans
captured at 60 fps and synchronized audio from 12 speakers. We then train a
neural network on our dataset that factors identity from facial motion. The
learned model, VOCA (Voice Operated Character Animation) takes any speech
signal as input - even speech in languages other than English - and
realistically animates a wide range of adult faces. Conditioning on subject
labels during training allows the model to learn a variety of realistic
speaking styles. VOCA also provides animator controls to alter speaking style,
identity-dependent facial shape, and pose (i.e. head, jaw, and eyeball
rotations) during animation. To our knowledge, VOCA is the only realistic 3D
facial animation model that is readily applicable to unseen subjects without
retargeting. This makes VOCA suitable for tasks like in-game video, virtual
reality avatars, or any scenario in which the speaker, speech, or language is
not known in advance. We make the dataset and model available for research
purposes at http://voca.is.tue.mpg.de.Comment: To appear in CVPR 201
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