665 research outputs found
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
Analysis of Design Principles and Requirements for Procedural Rigging of Bipeds and Quadrupeds Characters with Custom Manipulators for Animation
Character rigging is a process of endowing a character with a set of custom
manipulators and controls making it easy to animate by the animators. These
controls consist of simple joints, handles, or even separate character
selection windows.This research paper present an automated rigging system for
quadruped characters with custom controls and manipulators for animation.The
full character rigging mechanism is procedurally driven based on various
principles and requirements used by the riggers and animators. The automation
is achieved initially by creating widgets according to the character type.
These widgets then can be customized by the rigger according to the character
shape, height and proportion. Then joint locations for each body parts are
calculated and widgets are replaced programmatically.Finally a complete and
fully operational procedurally generated character control rig is created and
attached with the underlying skeletal joints. The functionality and feasibility
of the rig was analyzed from various source of actual character motion and a
requirements criterion was met. The final rigged character provides an
efficient and easy to manipulate control rig with no lagging and at high frame
rate.Comment: 21 pages, 24 figures, 4 Algorithms, Journal Pape
Unsupervised Training for 3D Morphable Model Regression
We present a method for training a regression network from image pixels to 3D
morphable model coordinates using only unlabeled photographs. The training loss
is based on features from a facial recognition network, computed on-the-fly by
rendering the predicted faces with a differentiable renderer. To make training
from features feasible and avoid network fooling effects, we introduce three
objectives: a batch distribution loss that encourages the output distribution
to match the distribution of the morphable model, a loopback loss that ensures
the network can correctly reinterpret its own output, and a multi-view identity
loss that compares the features of the predicted 3D face and the input
photograph from multiple viewing angles. We train a regression network using
these objectives, a set of unlabeled photographs, and the morphable model
itself, and demonstrate state-of-the-art results.Comment: CVPR 2018 version with supplemental material
(http://openaccess.thecvf.com/content_cvpr_2018/html/Genova_Unsupervised_Training_for_CVPR_2018_paper.html
FaceVerse: a Fine-grained and Detail-controllable 3D Face Morphable Model from a Hybrid Dataset
We present FaceVerse, a fine-grained 3D Neural Face Model, which is built
from hybrid East Asian face datasets containing 60K fused RGB-D images and 2K
high-fidelity 3D head scan models. A novel coarse-to-fine structure is proposed
to take better advantage of our hybrid dataset. In the coarse module, we
generate a base parametric model from large-scale RGB-D images, which is able
to predict accurate rough 3D face models in different genders, ages, etc. Then
in the fine module, a conditional StyleGAN architecture trained with
high-fidelity scan models is introduced to enrich elaborate facial geometric
and texture details. Note that different from previous methods, our base and
detailed modules are both changeable, which enables an innovative application
of adjusting both the basic attributes and the facial details of 3D face
models. Furthermore, we propose a single-image fitting framework based on
differentiable rendering. Rich experiments show that our method outperforms the
state-of-the-art methods.Comment: https://github.com/LizhenWangT/FaceVers
THREE DIMENSIONAL MODELING AND ANIMATION OF FACIAL EXPRESSIONS
Facial expression and animation are important aspects of the 3D environment featuring human characters. These animations are frequently used in many kinds of applications and there have been many efforts to increase the realism. Three aspects are still stimulating active research: the detailed subtle facial expressions, the process of rigging a face, and the transfer of an expression from one person to another. This dissertation focuses on the above three aspects.
A system for freely designing and creating detailed, dynamic, and animated facial expressions is developed. The presented pattern functions produce detailed and animated facial expressions. The system produces realistic results with fast performance, and allows users to directly manipulate it and see immediate results.
Two unique methods for generating real-time, vivid, and animated tears have been developed and implemented. One method is for generating a teardrop that continually changes its shape as the tear drips down the face. The other is for generating a shedding tear, which is a kind of tear that seamlessly connects with the skin as it flows along the surface of the face, but remains an individual object. The methods both broaden CG and increase the realism of facial expressions.
A new method to automatically set the bones on facial/head models to speed up the rigging process of a human face is also developed. To accomplish this, vertices that describe the face/head as well as relationships between each part of the face/head are grouped. The average distance between pairs of vertices is used to place the head bones. To set the bones in the face with multi-density, the mean value of the vertices in a group is measured. The time saved with this method is significant.
A novel method to produce realistic expressions and animations by transferring an existing expression to a new facial model is developed. The approach is to transform the source model into the target model, which then has the same topology as the source model. The displacement vectors are calculated. Each vertex in the source model is mapped to the target model. The spatial relationships of each mapped vertex are constrained
Neural Volumetric Blendshapes: Computationally Efficient Physics-Based Facial Blendshapes
Computationally weak systems and demanding graphical applications are still
mostly dependent on linear blendshapes for facial animations. The accompanying
artifacts such as self-intersections, loss of volume, or missing soft tissue
elasticity can be avoided by using physics-based animation models. However,
these are cumbersome to implement and require immense computational effort. We
propose neural volumetric blendshapes, an approach that combines the advantages
of physics-based simulations with realtime runtimes even on consumer-grade
CPUs. To this end, we present a neural network that efficiently approximates
the involved volumetric simulations and generalizes across human identities as
well as facial expressions. Our approach can be used on top of any linear
blendshape system and, hence, can be deployed straightforwardly. Furthermore,
it only requires a single neutral face mesh as input in the minimal setting.
Along with the design of the network, we introduce a pipeline for the
challenging creation of anatomically and physically plausible training data.
Part of the pipeline is a novel hybrid regressor that densely positions a skull
within a skin surface while avoiding intersections. The fidelity of all parts
of the data generation pipeline as well as the accuracy and efficiency of the
network are evaluated in this work. Upon publication, the trained models and
associated code will be released
Comparing and Evaluating Real Time Character Engines for Virtual Environments
As animated characters increasingly become vital parts of virtual environments, then the engines that drive these characters increasingly become vital parts of virtual environment software. This paper gives an overview of the state of the art in character engines, and proposes a taxonomy of the features that are commonly found in them. This taxonomy can be used as a tool for comparison and evaluation of different engines. In order to demonstrate this we use it to compare three engines. The first is Cal3D, the most commonly used open source engine. We also introduce two engines created by the authors, Piavca and HALCA. The paper ends with a brief discussion of some other popular engines
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