5,202 research outputs found
Splicing of concurrent upper-body motion spaces with locomotion
In this paper, we present a motion splicing technique for generating concurrent upper-body actions occurring simultaneously with the evolution of a lower-body locomotion sequence. Specifically, we show that a layered interpolation motion model generates upper-body poses while assigning different actions to each upper-body part. Hence, in the proposed motion splicing approach, it is possible to increase the number of generated motions as well as the number of desired actions that can be performed by virtual characters. Additionally, we propose an iterative motion blending solution, inverse pseudo-blending, to maintain a smooth and natural interaction between the virtual character and the virtual environment; inverse pseudo-blending is a constraint-based motion editing technique that blends the motions enclosed in a tetrahedron by minimising the distances between the end-effector positions of the actual and blended motions. Additionally, to evaluate the proposed solution, we implemented an example-based application for interactive motion splicing based on specified constraints. Finally, the generated results show that the proposed solution can be beneficially applied to interactive applications where concurrent actions of the upper-body are desired
Fast Simulation of Skin Sliding
Skin sliding is the phenomenon of the skin moving over underlying layers of fat, muscle and bone. Due to the complex interconnections between these separate layers and their differing elasticity properties, it is difficult to model and expensive to compute. We present a novel method to simulate this phenomenon at real--time by remeshing the surface based on a parameter space resampling. In order to evaluate the surface parametrization, we borrow a technique from structural engineering known as the force density method which solves for an energy minimizing form with a sparse linear system. Our method creates a realistic approximation of skin sliding in real--time, reducing texture distortions in the region of the deformation. In addition it is flexible, simple to use, and can be incorporated into any animation pipeline
A survey of real-time crowd rendering
In this survey we review, classify and compare existing approaches for real-time crowd rendering. We first overview character animation techniques, as they are highly tied to crowd rendering performance, and then we analyze the state of the art in crowd rendering. We discuss different representations for level-of-detail (LoD) rendering of animated characters, including polygon-based, point-based, and image-based techniques, and review different criteria for runtime LoD selection. Besides LoD approaches, we review classic acceleration schemes, such as frustum culling and occlusion culling, and describe how they can be adapted to handle crowds of animated characters. We also discuss specific acceleration techniques for crowd rendering, such as primitive pseudo-instancing, palette skinning, and dynamic key-pose caching, which benefit from current graphics hardware. We also address other factors affecting performance and realism of crowds such as lighting, shadowing, clothing and variability. Finally we provide an exhaustive comparison of the most relevant approaches in the field.Peer ReviewedPostprint (author's final draft
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
3D Cinemagraphy from a Single Image
We present 3D Cinemagraphy, a new technique that marries 2D image animation
with 3D photography. Given a single still image as input, our goal is to
generate a video that contains both visual content animation and camera motion.
We empirically find that naively combining existing 2D image animation and 3D
photography methods leads to obvious artifacts or inconsistent animation. Our
key insight is that representing and animating the scene in 3D space offers a
natural solution to this task. To this end, we first convert the input image
into feature-based layered depth images using predicted depth values, followed
by unprojecting them to a feature point cloud. To animate the scene, we perform
motion estimation and lift the 2D motion into the 3D scene flow. Finally, to
resolve the problem of hole emergence as points move forward, we propose to
bidirectionally displace the point cloud as per the scene flow and synthesize
novel views by separately projecting them into target image planes and blending
the results. Extensive experiments demonstrate the effectiveness of our method.
A user study is also conducted to validate the compelling rendering results of
our method.Comment: Accepted by CVPR 2023. Project page:
https://xingyi-li.github.io/3d-cinemagraphy
An Approach to the Procedural Generation of Worn Metal Surfaces
Motivated by the phenomenon that wear and tear tends to happen more near sharp cornersof a surface, this thesis presents a method for procedurally generating photorealistic metal surfacesbased upon evaluating curvature values. The thesis describes the development of eight metal shadersthat are used to replace the manual texture painting typically used in production. The approach isdemonstrated by applying these metal shaders to a robotic dog model from a short film involvinglive action and CG elements. Frames from a short animation of the robotic dog are presented, anda discussion of the strengths and weaknesses of this methodology
Learning to Dress {3D} People in Generative Clothing
Three-dimensional human body models are widely used in the analysis of human
pose and motion. Existing models, however, are learned from minimally-clothed
3D scans and thus do not generalize to the complexity of dressed people in
common images and videos. Additionally, current models lack the expressive
power needed to represent the complex non-linear geometry of pose-dependent
clothing shapes. To address this, we learn a generative 3D mesh model of
clothed people from 3D scans with varying pose and clothing. Specifically, we
train a conditional Mesh-VAE-GAN to learn the clothing deformation from the
SMPL body model, making clothing an additional term in SMPL. Our model is
conditioned on both pose and clothing type, giving the ability to draw samples
of clothing to dress different body shapes in a variety of styles and poses. To
preserve wrinkle detail, our Mesh-VAE-GAN extends patchwise discriminators to
3D meshes. Our model, named CAPE, represents global shape and fine local
structure, effectively extending the SMPL body model to clothing. To our
knowledge, this is the first generative model that directly dresses 3D human
body meshes and generalizes to different poses. The model, code and data are
available for research purposes at https://cape.is.tue.mpg.de.Comment: CVPR-2020 camera ready. Code and data are available at
https://cape.is.tue.mpg.d
Behavioural facial animation using motion graphs and mind maps
We present a new behavioural animation method that combines motion graphs for synthesis of animation and mind maps as behaviour controllers for the choice of motions, significantly reducing the cost of animating secondary characters. Motion graphs are created for each facial region from the analysis of a motion database, while synthesis occurs by minimizing the path distance that connects automatically chosen nodes. A Mind map is a hierarchical graph built on top of the motion graphs, where the user visually chooses how a stimulus affects the character's mood, which in turn will trigger motion synthesis. Different personality traits add more emotional complexity to the chosen reactions. Combining behaviour simulation and procedural animation leads to more emphatic and autonomous characters that react differently in each interaction, shifting the task of animating a character to one of defining its behaviour.</p
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