GHNeRF: Learning Generalizable Human Features with Efficient Neural Radiance Fields

no code implementations • 9 Apr 2024 • Arnab Dey, Di Yang, Rohith Agaram, Antitza Dantcheva, Andrew I. Comport, Srinath Sridhar, Jean Martinet

In this paper, we introduce a novel approach, termed GHNeRF, designed to address these limitations by learning 2D/3D joint locations of human subjects with NeRF representation.

HyP-NeRF: Learning Improved NeRF Priors using a HyperNetwork

no code implementations • NeurIPS 2023 • Bipasha Sen, Gaurav Singh, Aditya Agarwal, Rohith Agaram, K Madhava Krishna, Srinath Sridhar

Neural Radiance Fields (NeRF) have become an increasingly popular representation to capture high-quality appearance and shape of scenes and objects.

Retrieval

Canonical Fields: Self-Supervised Learning of Pose-Canonicalized Neural Fields

1 code implementation • CVPR 2023 • Rohith Agaram, Shaurya Dewan, Rahul Sajnani, Adrien Poulenard, Madhava Krishna, Srinath Sridhar

We present Canonical Field Network (CaFi-Net), a self-supervised method to canonicalize the 3D pose of instances from an object category represented as neural fields, specifically neural radiance fields (NeRFs).

Object Self-Supervised Learning