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scalable view synthesis

Scalable view synthesis is a computer vision and graphics technique that generates new, realistic visual perspectives of a three-dimensional scene or object from existing images while flexibly adapting to arbitrary numbers of input and output viewpoints. Unlike traditional view synthesis methods that depend on rigid camera configurations, scene-specific optimization, or fixed input-to-output ratios, scalable view synthesis accepts flexible quantities of reference views and can produce numerous spatially consistent target views across diverse camera trajectories. By employing generalized representations, continuous relative camera parameter controls, and generative models, this approach maintains visual coherence and computational efficiency across varying hardware capacities, effectively bridging single-image viewpoint generation, multi-image view synthesis, and dense three-dimensional reconstruction within a unified framework.

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EscherNet: A Generative Model for Scalable View Synthesis

EscherNet: A Generative Model for Scalable View Synthesis

Xin Kong, Shikun Liu, Xiaoyang Lyu, Marwan Taher, Xiaojuan Qi, Andrew J. Davison

Why you should read this

Introduces a multi-view conditioned diffusion model with relative camera positional encoding that synthesizes over 100 consistent target views simultaneously on a single GPU from arbitrary reference angles, unifying novel view synthesis and 3D reconstruction without expensive 3D convolutions or volumetric rendering.

We introduce EscherNet, a multi-view conditioned diffusion model for view synthesis. EscherNet learns implicit and generative 3D representations coupled with a specialised camera positional encoding, allowing precise and continuous relative control of the camera transformation between an arbitrary number of reference and target views. EscherNet offers exceptional generality, flexibility, and scalability in view synthesis — it can generate more than 100 consistent target views simultaneously on a single consumer-grade GPU, despite being trained with a fixed number of 3 reference views to 3 target views. As a result, EscherNet not only addresses zero-shot novel view synthesis, but also naturally unifies single- and multi-image 3D reconstruction, combining these diverse tasks into a single, cohesive framework. Our extensive experiments demonstrate that EscherNet achieves state-of-the-art performance in multiple benchmarks, even when compared to methods specifically tailored for each individual problem. This remarkable versatility opens up new directions for designing scalable neural architectures for 3D vision. Project page: https://kxhit.github.io/EscherNet.

Added

2026-09-26