A 3D avatar typically has one of six cardinal facial expressions. To simulate realistic emotional variation, we should be able to render a facial transition between two arbitrary expressions. This study presents a new framework for instruction-driven facial expression generation that produces a 3D face and, starting from an image of the face, transforms the facial expression from one designated facial expression to another. The Instruction-driven Facial Expression Decomposer (IFED) module is introduced to facilitate multimodal data learning and capture the correlation between textual descriptions and facial expression features. Subsequently, we propose the Instruction to Facial Expression Transition (I2FET) method, which leverages IFED and a vertex reconstruction loss function to refine the semantic comprehension of latent vectors, thus generating a facial expression sequence according to the given instruction. Lastly, we present the Facial Expression Transition model to generate smooth transitions between facial expressions. Extensive evaluation suggests that the proposed model outperforms state-of-the-art methods on the CK+ and CelebV-HQ datasets. The results show that our framework can generate facial expression trajectories according to text instruction. Considering that text prompts allow us to make diverse descriptions of human emotional states, the repertoire of facial expressions and the transitions between them can be expanded greatly. We expect our framework to find various practical applications. More information about our project can be found athttps://vohoanganh.github.io/tg3dfet/

In this paper, we propose a new metric that is applicable both to routing and rate adaptation in multi-rate wireless mesh networks. Unlike many previous efforts, our metric is comprehensive; it considers several factors that affect end-to-end performance such as the effect of the relative positions of the links on a path when choosing the rates of operation and the importance of avoiding congested areas. We call our metric ETM (for Expected Transmission cost in Multi-rate wireless networks). We analytically derive the ETM metric. We show that the ETM metric can be used (a) to determine the best end-to-end path with a greedy routing approach and (b) it can be used to dynamically select the best transmission rate for each link on the path via a dynamic programming approach. Based on ETM, we design and implement the ETM-framework on an indoor wireless mesh network and compare its performance with that of the popular ETT and the recently proposed ETOP metrics. Our experiments show that the ETM-framework yields throughput improvements of up to 253% and 368% as compared with the ETT and ETOP frameworks.