1,756 research outputs found
Visual Dynamics: Stochastic Future Generation via Layered Cross Convolutional Networks
We study the problem of synthesizing a number of likely future frames from a
single input image. In contrast to traditional methods that have tackled this
problem in a deterministic or non-parametric way, we propose to model future
frames in a probabilistic manner. Our probabilistic model makes it possible for
us to sample and synthesize many possible future frames from a single input
image. To synthesize realistic movement of objects, we propose a novel network
structure, namely a Cross Convolutional Network; this network encodes image and
motion information as feature maps and convolutional kernels, respectively. In
experiments, our model performs well on synthetic data, such as 2D shapes and
animated game sprites, and on real-world video frames. We present analyses of
the learned network representations, showing it is implicitly learning a
compact encoding of object appearance and motion. We also demonstrate a few of
its applications, including visual analogy-making and video extrapolation.Comment: Journal preprint of arXiv:1607.02586 (IEEE TPAMI, 2019). The first
two authors contributed equally to this work. Project page:
http://visualdynamics.csail.mit.ed
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
Training Physics-based Controllers for Articulated Characters with Deep Reinforcement Learning
In this thesis, two different applications are discussed for using machine learning techniques to train coordinated motion controllers in arbitrary characters in absence of motion capture data. The methods highlight the resourcefulness of physical simulations to generate synthetic and generic motion data that can be used to learn various targeted skills. First, we present an unsupervised method for learning loco-motion skills in virtual characters from a low dimensional latent space which captures the coordination between multiple joints. We use a technique called motor babble, wherein a character interacts with its environment by actuating its joints through uncoordinated, low-level (motor) excitation, resulting in a corpus of motion data from which a manifold latent space can be extracted. Using reinforcement learning, we then train the character to learn locomotion (such as walking or running) in the low-dimensional latent space instead of the full-dimensional joint action space. The thesis also presents an end-to-end automated framework for training physics-based characters to rhythmically dance to user-input songs. A generative adversarial network (GAN) architecture is proposed that learns to generate physically stable dance moves through repeated interactions with the environment. These moves are then used to construct a dance network that can be used for choreography. Using DRL, the character is then trained to perform these moves, without losing balance and rhythm, in the presence of physical forces such as gravity and friction
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
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