Neural Radiance Fields (NeRF) is a neural network-based technique for capturing 3D scenes and objects from 2D images or sparse 3D data. It consists of two main components, “NeRF in” and “NeRF out” network. NeRF-based human representations have applications in gaming, virtual reality, animation, film production, and medical imaging. ActorsNeRF is a category-level human actor NeRF model that can synthesize high-quality views of novel actors with unseen poses. It utilizes a 2-level canonical space and fine-tuning to achieve generalization across different individuals. ActorsNeRF outperforms the HumanNeRF approach in terms of maintaining valid shape and synthesizing unobserved body parts.
UCSD and ByteDance Researchers Present ActorsNeRF: A Novel Animatable Human Actor NeRF Model that Generalizes to Unseen Actors in a Few-Shot Setting
Neural Radiance Fields (NeRF) is a powerful AI technique that captures 3D scenes and objects from 2D images or sparse 3D data. It uses neural networks to predict the 3D position and color information of a point based on a pixel’s 2D coordinates and camera pose.
NeRF-based human representations have many practical applications, such as creating virtual avatars for games and virtual reality, 3D modeling for animation and film production, and medical imaging for diagnosis and treatment planning.
However, implementing NeRF can be computationally intensive and require substantial training data. To address this, researchers have developed a new method called ActorsNeRF, which is a category-level human actor NeRF model that can generate high-quality views of novel actors with unseen poses using only a few images.
ActorsNeRF leverages a 2-level canonical space and a skinning weight network to achieve realistic character movements and deformations. It is trained on a diverse set of subjects to achieve generalization across different individuals.
ActorsNeRF outperforms previous approaches and maintains valid shapes for unobserved body parts. It has been tested on multiple benchmarks and consistently performs well.
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