Cargando…
Deep Learning-Based Motion Style Transfer Tools, Techniques and Future Challenges
In the fourth industrial revolution, the scale of execution for interactive applications increased substantially. These interactive and animated applications are human-centric, and the representation of human motion is unavoidable, making the representation of human motions ubiquitous. Animators str...
Autores principales: | Akber, Syed Muhammad Abrar, Kazmi, Sadia Nishat, Mohsin, Syed Muhammad, Szczęsna, Agnieszka |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10007042/ https://www.ncbi.nlm.nih.gov/pubmed/36904801 http://dx.doi.org/10.3390/s23052597 |
Ejemplares similares
-
Multiday EMG-Based Classification of Hand Motions with Deep Learning Techniques
por: Zia ur Rehman, Muhammad, et al.
Publicado: (2018) -
Optical motion capture dataset of selected techniques in beginner and advanced Kyokushin karate athletes
por: Szczęsna, Agnieszka, et al.
Publicado: (2021) -
The impact of COVID-19 pandemic on air pollution: a global research framework, challenges, and future perspectives
por: Mehmood, Khalid, et al.
Publicado: (2022) -
Generation of microbial colonies dataset with deep learning style transfer
por: Pawłowski, Jarosław, et al.
Publicado: (2022) -
Deep Layer Aggregation Architectures for Photorealistic Universal Style Transfer
por: Dediu, Marius, et al.
Publicado: (2023)