In the evolving and increasingly complex landscape of computer graphics and visualization, the integration of perceptual modeling with structural representation has become paramount for significantly advancing visual communication capabilities. Traditional methodologies, while offering foundational insights, often fall short in preserving critical spatial integrity while simultaneously enhancing semantic discrimination, resulting in persistent challenges for key applications such as human pose estimation, face frontalization, and other geometry-intensive tasks. To address these longstanding limitations, we introduce a comprehensive and systematic framework composed of three tightly interconnected components including a rigorous and formalized definition of visual communication tasks, the innovative Perceptual Relational Projection Network (PRPN), and the advanced Context-Aware Geometric Alignment Strategy (CAGAS). The PRPN utilizes a dual-stream network architecture designed to encode both global perceptual structures and fine-grained local relational cues, ensuring spatial transformation consistency, robustness to structural variations, and multiscale contextual integration across diverse scenarios. Complementing this, CAGAS proposes a novel spatially informed alignment mechanism, dynamically optimizing inter-frame or inter-image consistency, while effectively mitigating the effects of occlusion, pose variability, and non-rigid deformations. Together, these components form a unified and highly adaptable methodology that redefines the way visual communication tasks are modeled and solved. This approach not only improves fidelity and interpretability but also aligns closely with the cutting-edge objectives of the Computer Graphics and Visualization field, thereby opening new avenues for developing innovative, precise, and intelligent visualization techniques.
Jing Luo (Tue,) studied this question.