ActorsNeRF: Animatable Few-shot Human Rendering with Generalizable NeRFs

Understand

While NeRF-based human representations have shown impressive novel view synthesis results, most methods still rely on a large number of images / views for training.

• In this work, we propose a novel animatable NeRF called ActorsNeRF.
• It is first pre-trained on diverse human subjects, and then adapted with few-shot monocular video frames for a new actor with unseen poses.
• Building on previous generalizable NeRFs with parameter sharing using a ConvNet encoder, ActorsNeRF further adopts two human priors to capture the large human appearance, shape, and pose variations.