242 research outputs found
Denoising Diffusion Probabilistic Models for Styled Walking Synthesis
Generating realistic motions for digital humans is time-consuming for many
graphics applications. Data-driven motion synthesis approaches have seen solid
progress in recent years through deep generative models. These results offer
high-quality motions but typically suffer in motion style diversity. For the
first time, we propose a framework using the denoising diffusion probabilistic
model (DDPM) to synthesize styled human motions, integrating two tasks into one
pipeline with increased style diversity compared with traditional motion
synthesis methods. Experimental results show that our system can generate
high-quality and diverse walking motions
Motion Capture Dataset for Practical Use of AI-based Motion Editing and Stylization
In this work, we proposed a new style-diverse dataset for the domain of
motion style transfer. The motion dataset uses an industrial-standard human
bone structure and thus is industry-ready to be plugged into 3D characters for
many projects. We claim the challenges in motion style transfer and encourage
future work in this domain by releasing the proposed motion dataset both to the
public and the market. We conduct a comprehensive study on motion style
transfer in the experiment using the state-of-the-art method, and the results
show the proposed dataset's validity for the motion style transfer task
Data-Driven Shape Analysis and Processing
Data-driven methods play an increasingly important role in discovering
geometric, structural, and semantic relationships between 3D shapes in
collections, and applying this analysis to support intelligent modeling,
editing, and visualization of geometric data. In contrast to traditional
approaches, a key feature of data-driven approaches is that they aggregate
information from a collection of shapes to improve the analysis and processing
of individual shapes. In addition, they are able to learn models that reason
about properties and relationships of shapes without relying on hard-coded
rules or explicitly programmed instructions. We provide an overview of the main
concepts and components of these techniques, and discuss their application to
shape classification, segmentation, matching, reconstruction, modeling and
exploration, as well as scene analysis and synthesis, through reviewing the
literature and relating the existing works with both qualitative and numerical
comparisons. We conclude our report with ideas that can inspire future research
in data-driven shape analysis and processing.Comment: 10 pages, 19 figure
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