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In mechanical engineering, a three-dimensional force system is a system of forces acting in three dimensions, with forces applied along the x, y, and z coordinate axes. The three-dimensional force system is an important concept in mechanical engineering, as it allows engineers to understand and analyze the behavior of objects and structures in three dimensions. By understanding the forces acting on a system, engineers can design more efficient and effective mechanical systems that can withstand...
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Marco de referencia adaptativo dinámico espaciotemporal impulsado por aprendizaje contrastivo para la generación de

Zhiqiang Song1, Ruyan Zhang2, Shuangjun Li1

  • 1College of Physical Education, Shandong Sport University, Jinan, China.

PloS one
|February 19, 2026
PubMed
Resumen
Este resumen es generado por máquina.

Este estudio presenta un nuevo marco para generar movimientos humanos en 3D estilizados. Mejora la captura de estilo local y el detalle en movimientos dinámicos utilizando aprendizaje contrastivo y mecanismos de atención.

Palabras clave:
aprendizaje contrastivogeneración de movimiento humano en 3Dmovimiento humano estilizadoadaptación espaciotemporalatenciónvisión por computadora

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Área de la Ciencia:

  • Visión por Computadora
  • Inteligencia Artificial
  • Gráficos por Computadora

Sus antecedentes:

  • Los métodos existentes de generación de movimiento humano en 3D a menudo pasan por alto las variaciones estilísticas locales, lo que resulta en secuencias generadas que carecen de detalles expresivos.
  • Las estadísticas globales de estilo temporal son insuficientes para capturar los matices de los movimientos humanos dinámicos.

Objetivo del estudio:

  • Proponer un marco impulsado por aprendizaje contrastivo para la generación de movimiento humano en 3D estilizado, adaptativo dinámico espaciotemporal.
  • Mejorar la capacidad de capturar variaciones estilísticas locales y mejorar el detalle expresivo en los movimientos humanos en 3D generados.

Principales métodos:

  • Se introdujo la normalización de instancia de atención espacial (SAIN) y la normalización de instancia de atención temporal (TAIN) para extraer características de estilo de movimiento locales y globales.
  • Se empleó una estructura de doble vía para aislar el contenido del movimiento y un inyector de estilo (SADA, TADA) para la integración de estilo de precisión.
  • Se utilizaron pérdidas contrastivas de estilo y contenido durante el entrenamiento para mejorar la agrupación y separación de características.

Principales resultados:

  • El método propuesto logró un rendimiento superior en el conjunto de datos Xia, con una FID de 0.06, una precisión del 96.70%, una diversidad del 5.67 y una multimodalidad del 0.97, coincidiendo estrechamente con datos reales.
  • En tareas de transferencia de estilo de movimiento, el modelo alcanzó un 94.11 CRA y un 89.41 SRA, superando los métodos existentes de vanguardia.

Conclusiones:

  • El marco desarrollado desvincula eficazmente el estilo y el contenido del movimiento, lo que permite una generación de movimiento humano en 3D estilizado, adaptativo dinámico y de precisión.
  • El enfoque de aprendizaje contrastivo mejora la diversidad estilística y la fidelidad del contenido, produciendo movimientos humanos más expresivos y realistas.