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This paper aims to tackle the challenge of efficiently producing interactivefree-viewpoint videos. Some recent works equip neural radiance fields withimage encoders, enabling them to generalize across scenes. When processingdynamic scenes, they can simply treat each video frame as an individual sceneand perform novel view synthesis to generate free-viewpoint videos. However,their rendering process is slow and cannot support interactive applications. Amajor factor is that they sample lots of points in empty space when inferringradiance fields. We propose a novel scene representation, called ENeRF, for thefast creation of interactive free-viewpoint videos. Specifically, givenmulti-view images at one frame, we first build the cascade cost volume topredict the coarse geometry of the scene. The coarse geometry allows us tosample few points near the scene surface, thereby significantly improving therendering speed. This process is fully differentiable, enabling us to jointlylearn the depth prediction and radiance field networks from RGB images.Experiments on multiple benchmarks show that our approach exhibits competitiveperformance while being at least 60 times faster than previous generalizableradiance field methods.

Efficient Neural Radiance Fields for Interactive Free-viewpoint Video