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arXiv · 2603.15433

Real-Time Human Frontal View Synthesis from a Single Image

Fangyu Lin·Yingdong Hu·Lunjie Zhu·Zhening Liu·Yushi Huang·Zehong Lin·Jun Zhang
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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.

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Fangyu Lin, Yingdong Hu, Lunjie Zhu, Zhening Liu, Yushi Huang, Zehong Lin, Jun Zhang. 2026-03-16. Real-Time Human Frontal View Synthesis from a Single Image. https://arxiv.org/abs/2603.15433

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