8,110 research outputs found
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
Automatic Animation of Hair Blowing in Still Portrait Photos
We propose a novel approach to animate human hair in a still portrait photo.
Existing work has largely studied the animation of fluid elements such as water
and fire. However, hair animation for a real image remains underexplored, which
is a challenging problem, due to the high complexity of hair structure and
dynamics. Considering the complexity of hair structure, we innovatively treat
hair wisp extraction as an instance segmentation problem, where a hair wisp is
referred to as an instance. With advanced instance segmentation networks, our
method extracts meaningful and natural hair wisps. Furthermore, we propose a
wisp-aware animation module that animates hair wisps with pleasing motions
without noticeable artifacts. The extensive experiments show the superiority of
our method. Our method provides the most pleasing and compelling viewing
experience in the qualitative experiments and outperforms state-of-the-art
still-image animation methods by a large margin in the quantitative evaluation.
Project url: \url{https://nevergiveu.github.io/AutomaticHairBlowing/}Comment: Accepted to ICCV 202
Data-driven techniques for animating virtual characters
One of the key goals of current research in data-driven computer animation is the synthesis of new motion sequences from existing motion data. This thesis presents three novel techniques for synthesising the motion of a virtual character from existing motion data and develops a framework of solutions to key character animation problems.
The first motion synthesis technique presented is based on the character’s locomotion composition process. This technique examines the ability of synthesising a variety of character’s locomotion behaviours while easily specified constraints (footprints) are placed in the three-dimensional space. This is achieved by analysing existing motion data, and by assigning the locomotion behaviour transition process to transition graphs that are responsible for providing information about this process.
However, virtual characters should also be able to animate according to different style variations. Therefore, a second technique to synthesise real-time style variations of character’s motion. A novel technique is developed that uses correlation between two different motion styles, and by assigning the motion synthesis process to a parameterised maximum a posteriori (MAP) framework retrieves the desire style content of the input motion in real-time, enhancing the realism of the new synthesised motion sequence.
The third technique presents the ability to synthesise the motion of the character’s fingers either o↵-line or in real-time during the performance capture process. The advantage of both techniques is their ability to assign the motion searching process to motion features. The presented technique is able to estimate and synthesise a valid motion of the character’s fingers, enhancing the realism of the input motion.
To conclude, this thesis demonstrates that these three novel techniques combine in to a framework that enables the realistic synthesis of virtual character movements, eliminating the post processing, as well as enabling fast synthesis of the required motion
An Extendable Multiagent Model for Behavioural Animation
This paper presents a framework for visually
simulating the behaviour of actors in virtual environments.
In principle, the environmental interaction
follows a cyclic processing of perception,
decision, and action. As natural life-forms
perceive their environment by active sensing,
our approach also tends to let the artificial actor
actively sense the virtual world. This allows
us to place the characters in non-preprocessed
virtual dynamic environments, what we call
generic environments. A main aspect within
our framework is the strict distinction between
a behaviour pattern, that we term model, and
its instances, named characters, which use the
pattern. This allows them sharing one or more
behaviour models. Low-level tasks like sensing
or acting are took over by so called subagents,
which are subordinated modules extendedly
plugged in the character. In a demonstration
we exemplarily show the application of
our framework. We place the same
character in different environments and let it
climb and descend stairs, ramps and hills autonomously.
Additionally the reactiveness for
moving objects is tested. In future, this approach
shall go into action for a simulation of an urban
environment
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