轻量级网络实现单图实时新视角生成，支持动态位置感知。
Real-Time Position-Aware View Synthesis from Single-View Input
- 通过位置感知嵌入将目标相机位姿映射为高维特征图
- 双编码器融合生成高质量新视角，复杂平移运动表现优异
- 无需显式几何变换，适合低延迟交互场景如远程会议
视图合成技术显著提升了沉浸式体验，广泛应用于远程呈现和娱乐等领域。通过从单张输入图像生成新视角，用户能更真实地感知与互动环境。然而，许多前沿方法虽视觉质量高，却难以实现实时性能，限制了其在低延迟关键场景的应用。本文提出一种轻量级、位置感知的网络，可基于单张输入图像与目标相机姿态实现实时视图合成。框架包含位置感知嵌入模块，高效将目标位姿的位置信息映射为高维特征图；该特征图与输入图像共同输入渲染网络，通过双编码器分支融合高低层特征，生成逼真新视图。实验表明，相比现有方法，本方案在效率与视觉质量上均有优势，尤其在处理复杂平移运动时无需显式几何操作（如扭曲），表现出色。本工作推动了实时交互式远程呈现应用的发展。
原文摘要 · Abstract (English)
Recent advancements in view synthesis have significantly enhanced immersive experiences across various computer graphics and multimedia applications, including telepresence and entertainment. By enabling the generation of new perspectives from a single input view, view synthesis allows users to better perceive and interact with their environment. However, many state-of-the-art methods, while achieving high visual quality, face limitations in real-time performance, which makes them less suitable for live applications where low latency is critical. In this paper, we present a lightweight, position-aware network designed for real-time view synthesis from a single input image and a target camera pose. The proposed framework consists of a Position Aware Embedding, which efficiently maps positional information from the target pose to generate high dimensional feature maps. These feature maps, along with the input image, are fed into a Rendering Network that merges features from dual encoder branches to resolve both high and low level details, producing a realistic new view of the scene. Experimental results demonstrate that our method achieves superior efficiency and visual quality compared to existing approaches, particularly in handling complex translational movements without explicit geometric operations like warping. This work marks a step toward enabling real-time live and interactive telepresence applications.
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