From Pre-Rendered to Autonomous: A Systematic Review of AI-Driven Character Animation and Embodiment in Virtual Reality

In recent years, the generation and animation of avatars in virtual reality (VR) have undergone a definitive paradigm shift, transitioning from pre-rendered, manually rigged meshes to autonomous, AI-driven digital entities. While individual algorithms have been extensively studied, there is a critical lack of comprehensive synthesis regarding how these generative models impact the broader sociotechnical ecosystem of Spatial Computing. To address this gap, this systematic literature review, conducted in accordance with PRISMA guidelines, analyzed 48 primary studies to evaluate the intersection of Generative AI, hardware architecture, human psychology, and digital ethics. The synthesis reveals a deeply interdependent ecosystem. While advanced neural rendering and diffusion models (RQ1) successfully bypass traditional 3D authoring bottlenecks, their pursuit of absolute visual fidelity severely antagonizes the thermal and latency constraints of standalone mobile hardware (RQ2). The literature demonstrates that failing to mitigate these bottlenecks through hardware–software co-design (e.g., specialized ASICs, gaze-contingent foveation) inevitably shatters the user’s sensorimotor loop, collapsing the sense of agency and triggering the Kinematic Uncanny Valley (RQ3). Furthermore, as these hyper-realistic avatars achieve kinematic autonomy, they introduce unprecedented sociotechnical vulnerabilities regarding spatial privacy, dataset bias, and post-mortem digital identity (RQ4). Ultimately, this review concludes that realizing a compelling and inclusive AI-driven Metaverse is no longer an isolated computer graphics challenge; it demands a rigorous, interdisciplinary paradigm shift where algorithms, silicon architectures, and cognitive psychology are inextricably co-designed under a foundational framework of digital ethics.