Salvato in:
| Autori principali: | , , , , , , |
|---|---|
| Natura: | Artículo Open Access |
| Pubblicazione: |
Wiley
2026
|
| Soggetti: | |
| Accesso online: | https://onlinelibrary.wiley.com/doi/10.1002/cav.70091 |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
Sommario:
- SinMDGan: A Hybrid Deep Learning Framework for Single Motion Synthesis Using Diffusion‐GAN Models Qiang Chen Binsong Zuo Tingsong Lu Yuming Fang Xiaolu Mu Chao Cai Xiaogang Jin Computer Animation and Virtual Worlds ABSTRACT Generating diverse and realistic movements has long been a central challenge in computer graphics. Generative Adversarial Networks (GANs) remain a compelling solution due to their ability to perform well even with limited training data. However, traditional GANs generate samples directly, which can lead to the omission of certain data patterns. To address this limitation, we introduce SinMDGan , a hybrid deep learning framework for single‐motion synthesis that leverages a Diffusion‐GAN model. Our approach integrates the strengths of GANs, which capture global motion characteristics, with diffusion techniques, which refine local details, ensuring both authenticity and diversity in generated movements. Unlike conventional cascaded GANs, our framework employs a single generator‐discriminator pair, utilizing different diffusion time steps to synthesize novel and diverse motions from a single short sequence. Experimental evaluations demonstrate the effectiveness of our model in achieving stable data distribution coverage and enhancing output diversity. Additionally, we showcase various applications, including motion composition and long‐sequence generation, highlighting the versatility of our approach. 10.1002/cav.70091 http://onlinelibrary.wiley.com/termsAndConditions#vor