AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
PaMG is introduced, a framework that leverages the local features of body part motions, achieving better performance in both global and local text-driven human motion generation and editing and validating the effectiveness of the approach.
Object and subjective experiments demonstrate that the PyraMotion outperforms state-of-the-art methods in terms of generating natural and expressive full-body human gestures and the self-adaptiveness integration through attention maps contributes to performance.
This work proposes POP-TMR which pretrains body part representations for fine-grained text-motion retrieval and introduces HumanML3D+, an enhanced benchmark that provides accurate positive annotations for text queries and includes text descriptions at varying levels of detail, enabling more systematic performance asses...
It is demonstrated that MoSAIC improves the response--preservation trade-off required for selective and controllable part-local motion editing, and is presented as a latent diffusion framework for part-local reference-conditioned motion style transfer.
This work explores the motion transfer from one video to another, which is crucial in animation for diverse characters. Previously, video motion transfer has been largely explored between human and human-like characters, enabling a lot of applications in digital creation. However, these approaches encounter a main limi...
This work investigates a simple yet effective conditional generative framework for text-to-motion generation and proposes T2M-GIT+, which employs a non-autoregressive method to generate discrete motion representations in parallel, and is therefore more efficient than T2M-GPT+ while achieving comparable results.
This work proposes Change and Invariance Motion Editing (CIME), a unified framework that comprehensively decouples change and invariance into spatial pose and temporal rhythm dimensions and introduces the Riemannian Non-uniform Integral Manifold Mapping module.
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