Real-Time Position-Aware View Synthesis from Single-View Input
Manu Gond, Emin Zerman, Sebastian Knorr, M{\aa}rten Sj\"ostr\"om

TL;DR
This paper introduces a lightweight, position-aware neural network capable of real-time view synthesis from a single image, enabling immersive, low-latency applications like telepresence with high visual fidelity.
Contribution
The authors propose a novel position-aware network architecture that efficiently synthesizes new views in real-time from a single image without explicit geometric warping.
Findings
Achieves real-time performance suitable for live applications.
Produces high-quality, realistic new views from a single input image.
Handles complex translational movements effectively.
Abstract
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,…
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Taxonomy
TopicsRobotics and Sensor-Based Localization · Advanced Vision and Imaging · Advanced Image and Video Retrieval Techniques
MethodsAttentive Walk-Aggregating Graph Neural Network
