Search Results for author:
Found 9 papers, 8 papers with code
Morphable Diffusion: 3D-Consistent Diffusion for Single-image Avatar Creation
To the best of our knowledge, our proposed framework is the first diffusion model to enable the creation of fully 3D-consistent, animatable, and photorealistic human avatars from a single image of an unseen subject; extensive quantitative and qualitative evaluations demonstrate the advantages of our approach over existing state-of-the-art avatar creation models on both novel view and novel expression synthesis tasks.
3DGS-Avatar: Animatable Avatars via Deformable 3D Gaussian Splatting
In this paper, we use 3D Gaussian Splatting and learn a non-rigid deformation network to reconstruct animatable clothed human avatars that can be trained within 30 minutes and rendered at real-time frame rates (50+ FPS).
ResFields: Residual Neural Fields for Spatiotemporal Signals
Neural fields, a category of neural networks trained to represent high-frequency signals, have gained significant attention in recent years due to their impressive performance in modeling complex 3D data, such as signed distance (SDFs) or radiance fields (NeRFs), via a single multi-layer perceptron (MLP).
KeypointNeRF: Generalizing Image-based Volumetric Avatars using Relative Spatial Encoding of Keypoints
In this work, we investigate common issues with existing spatial encodings and propose a simple yet highly effective approach to modeling high-fidelity volumetric humans from sparse views.
Ranked #2 on
Generalizable Novel View Synthesis
on ZJU-MoCap
COAP: Compositional Articulated Occupancy of People
We present a novel neural implicit representation for articulated human bodies.
MetaAvatar: Learning Animatable Clothed Human Models from Few Depth Images
In contrast, we propose an approach that can quickly generate realistic clothed human avatars, represented as controllable neural SDFs, given only monocular depth images.
LEAP: Learning Articulated Occupancy of People
Substantial progress has been made on modeling rigid 3D objects using deep implicit representations.
DeepSurfels: Learning Online Appearance Fusion
We present DeepSurfels, a novel hybrid scene representation for geometry and appearance information.
Finding the most similar textual documents using Case-Based Reasoning
In 2016, the new similarity TS-SS metric is proposed, which showed state-of-the-art results in the field of textual mining for unsupervised learning.
