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Found 11 papers, 9 papers with code
NeRF-XL: Scaling NeRFs with Multiple GPUs
We present NeRF-XL, a principled method for distributing Neural Radiance Fields (NeRFs) across multiple GPUs, thus enabling the training and rendering of NeRFs with an arbitrarily large capacity.
NeRF-Det: Learning Geometry-Aware Volumetric Representation for Multi-View 3D Object Detection
We present NeRF-Det, a novel method for indoor 3D detection with posed RGB images as input.
NerfAcc: Efficient Sampling Accelerates NeRFs
Optimizing and rendering Neural Radiance Fields is computationally expensive due to the vast number of samples required by volume rendering.
Nerfstudio: A Modular Framework for Neural Radiance Field Development
Neural Radiance Fields (NeRF) are a rapidly growing area of research with wide-ranging applications in computer vision, graphics, robotics, and more.
Monocular Dynamic View Synthesis: A Reality Check
We study the recent progress on dynamic view synthesis (DVS) from monocular video.
NerfAcc: A General NeRF Acceleration Toolbox
We propose NerfAcc, a toolbox for efficient volumetric rendering of radiance fields.
TAVA: Template-free Animatable Volumetric Actors
Since TAVA does not require a body template, it is applicable to humans as well as other creatures such as animals.
PlenOctrees for Real-time Rendering of Neural Radiance Fields
We introduce a method to render Neural Radiance Fields (NeRFs) in real time using PlenOctrees, an octree-based 3D representation which supports view-dependent effects.
AI Choreographer: Music Conditioned 3D Dance Generation with AIST++
We present AIST++, a new multi-modal dataset of 3D dance motion and music, along with FACT, a Full-Attention Cross-modal Transformer network for generating 3D dance motion conditioned on music.
Ranked #2 on
Motion Synthesis
on BRACE
Monocular Real-Time Volumetric Performance Capture
We present the first approach to volumetric performance capture and novel-view rendering at real-time speed from monocular video, eliminating the need for expensive multi-view systems or cumbersome pre-acquisition of a personalized template model.
Learning Formation of Physically-Based Face Attributes
Based on a combined data set of 4000 high resolution facial scans, we introduce a non-linear morphable face model, capable of producing multifarious face geometry of pore-level resolution, coupled with material attributes for use in physically-based rendering.
