Real-Time Human Frontal View Synthesis from a Single Image

TL;DR

PrismMirror is a real-time, geometry-guided framework that synthesizes photorealistic human frontal views from a single image, improving visual quality and structural accuracy for immersive telepresence.

Contribution

It introduces a novel cascade learning strategy and a lightweight model for real-time, geometry-aware frontal view synthesis without external geometric modeling.

Findings

Achieves 24 FPS real-time inference.

Outperforms previous methods in visual authenticity.

Maintains structural accuracy in synthesized views.

Abstract

Photorealistic human novel view synthesis from a single image is crucial for democratizing immersive 3D telepresence, eliminating the need for complex multi-camera setups. However, current rendering-centric methods prioritize visual fidelity over explicit geometric understanding and struggle with intricate regions like faces and hands, leading to temporal instability. Meanwhile, human-centric frameworks suffer from memory bottlenecks since they typically rely on an auxiliary model to provide informative structural priors for geometric modeling, which limits real-time performance. To address these challenges, we propose PrismMirror, a geometry-guided framework for instant frontal view synthesis from a single image. By avoiding external geometric modeling and focusing on frontal view synthesis, our model optimizes visual integrity for telepresence. Specifically, PrismMirror introduces a novel cascade learning strategy that enables coarse-to-fine geometric feature learning. It first directly learns coarse geometric features, such as SMPL-X meshes and point clouds, and then refines textures through rendering supervision. To achieve real-time efficiency, we distill this unified framework into a lightweight linear attention model. Notably, PrismMirror is the first monocular human frontal view synthesis model that achieves real-time inference at 24 FPS, significantly outperforming previous methods in both visual authenticity and structural accuracy.