The Impact of Point-Cloud Rendering on User Perceptions of Human- and AI-Controlled Avatar Embodiment

Avatars for social VR are increasingly driven by imperfect tracking or AI-generated motion, where mismatches between appearance fidelity and behavioral realism can harm user perception. We investigate whether a simulated point-cloud rendering (PCR) can improve social perception and increase tolerance to motion artifacts compared to standard shaded mesh rendering. We present a real-time VR avatar system that supports (i) a streaming conversational loop, (ii) human-controlled embodiment via hybrid tracking using a head-mounted display for upper body and an external camera for lower body and root motion, and (iii) AI-controlled embodiment via context-aware selection from an offline-generated animation library. We evaluate a 2×2×2 within-subjects study crossing rendering style (PCR vs. mesh) and control source (human vs. AI) across two sequential scenarios: a conversation-focused desk interaction and a movement-focused guided exercise. We measure social presence, human-likeness and affinity, and appearance-behavior plausibility, and collect post-study rendering-style preference rankings. Our results show that PCR significantly improves perceived human-likeness and plausibility, with particularly strong benefits for AI-controlled avatars. Crucially, PCR successfully closed the plausibility gap between human and AI avatars during high-movement interactions, acting as a perceptual buffer against motion artifacts. While social presence was driven by the interaction scenario rather than rendering style, post-study rankings revealed a trade-off between perceptual coherence and visual comfort.