Real-Time Human Frontal View Synthesis from a Single Image

• URL: http://arxiv.org/abs/2603.15433v1
• Date: Mon, 16 Mar 2026 15:39:48 GMT
• Title: Real-Time Human Frontal View Synthesis from a Single Image
• Abstract summary: Photorealistic human view synthesis from a single image is crucial for democratizing immersive 3D telepresence.<n>We propose PrismMirror, a geometry-guided framework for instant frontal view synthesis from a single image.<n> PrismMirror is the first monocular human frontal view synthesis model that achieves real-time inference at 24 FPS.
• Score: 15.31754211648905
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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