9,983 research outputs found
Motor control and strategy discovery for physically simulated characters
In physics-based character animation, motions are realized through control of simulated characters along with their interactions with the virtual environment. In this thesis, we study the problem of character control on two levels: joint-level motor control which transforms control signals to joint torques, and high-level motion control which outputs joint-level control signals given the current state of the character and the environment and the task objective. We propose a Modified Articulated-Body Algorithm (MABA) which achieves stable proportional-derivative (PD) low-level motor control with superior theoretical time complexity, practical efficiency and stability than prior implementations. We further propose a high-level motion control framework based on deep reinforcement learning (DRL) which enables the discovery of appropriate motion strategies without human demonstrations to complete a task objective. To facilitate the learning of realistic human motions, we propose a Pose Variational Autoencoder (P-VAE) to constrain the DRL actions to a subspace of natural poses. Our learning framework can be further combined with a sample-efficient Bayesian Diversity Search (BDS) algorithm and novel policy seeking to discover diverse strategies for tasks with multiple modes, such as various athletic jumping tasks
CASE: Learning Conditional Adversarial Skill Embeddings for Physics-based Characters
We present CASE, an efficient and effective framework that learns
conditional Adversarial Skill Embeddings for physics-based characters. Our
physically simulated character can learn a diverse repertoire of skills while
providing controllability in the form of direct manipulation of the skills to
be performed. CASE divides the heterogeneous skill motions into distinct
subsets containing homogeneous samples for training a low-level conditional
model to learn conditional behavior distribution. The skill-conditioned
imitation learning naturally offers explicit control over the character's
skills after training. The training course incorporates the focal skill
sampling, skeletal residual forces, and element-wise feature masking to balance
diverse skills of varying complexities, mitigate dynamics mismatch to master
agile motions and capture more general behavior characteristics, respectively.
Once trained, the conditional model can produce highly diverse and realistic
skills, outperforming state-of-the-art models, and can be repurposed in various
downstream tasks. In particular, the explicit skill control handle allows a
high-level policy or user to direct the character with desired skill
specifications, which we demonstrate is advantageous for interactive character
animation.Comment: SIGGRAPH Asia 202
Locomotion-Action-Manipulation: Synthesizing Human-Scene Interactions in Complex 3D Environments
Synthesizing interaction-involved human motions has been challenging due to
the high complexity of 3D environments and the diversity of possible human
behaviors within. We present LAMA, Locomotion-Action-MAnipulation, to
synthesize natural and plausible long-term human movements in complex indoor
environments. The key motivation of LAMA is to build a unified framework to
encompass a series of everyday motions including locomotion, scene interaction,
and object manipulation. Unlike existing methods that require motion data
"paired" with scanned 3D scenes for supervision, we formulate the problem as a
test-time optimization by using human motion capture data only for synthesis.
LAMA leverages a reinforcement learning framework coupled with a motion
matching algorithm for optimization, and further exploits a motion editing
framework via manifold learning to cover possible variations in interaction and
manipulation. Throughout extensive experiments, we demonstrate that LAMA
outperforms previous approaches in synthesizing realistic motions in various
challenging scenarios. Project page: https://jiyewise.github.io/projects/LAMA/ .Comment: Accepted to ICCV 202
Example-based Motion Synthesis via Generative Motion Matching
We present GenMM, a generative model that "mines" as many diverse motions as
possible from a single or few example sequences. In stark contrast to existing
data-driven methods, which typically require long offline training time, are
prone to visual artifacts, and tend to fail on large and complex skeletons,
GenMM inherits the training-free nature and the superior quality of the
well-known Motion Matching method. GenMM can synthesize a high-quality motion
within a fraction of a second, even with highly complex and large skeletal
structures. At the heart of our generative framework lies the generative motion
matching module, which utilizes the bidirectional visual similarity as a
generative cost function to motion matching, and operates in a multi-stage
framework to progressively refine a random guess using exemplar motion matches.
In addition to diverse motion generation, we show the versatility of our
generative framework by extending it to a number of scenarios that are not
possible with motion matching alone, including motion completion, key
frame-guided generation, infinite looping, and motion reassembly. Code and data
for this paper are at https://wyysf-98.github.io/GenMM/Comment: SIGGRAPH 2023. Project page: https://wyysf-98.github.io/GenMM/,
Video: https://www.youtube.com/watch?v=lehnxcade4
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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