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Found 15 papers, 8 papers with code
LightAvatar: Efficient Head Avatar as Dynamic Neural Light Field
Recent works have shown that neural radiance fields (NeRFs) on top of parametric models have reached SOTA quality to build photorealistic head avatars from a monocular video.
Gaussian3Diff: 3D Gaussian Diffusion for 3D Full Head Synthesis and Editing
We present a novel framework for generating photorealistic 3D human head and subsequently manipulating and reposing them with remarkable flexibility.
Learning Personalized High Quality Volumetric Head Avatars from Monocular RGB Videos
The learnt avatar is driven by a parametric face model to achieve user-controlled facial expressions and head poses.
Cross-Modal 3D Shape Generation and Manipulation
In this paper, we propose a generic multi-modal generative model that couples the 2D modalities and implicit 3D representations through shared latent spaces.
R2L: Distilling Neural Radiance Field to Neural Light Field for Efficient Novel View Synthesis
On the other hand, Neural Light Field (NeLF) presents a more straightforward representation over NeRF in novel view synthesis -- the rendering of a pixel amounts to one single forward pass without ray-marching.
NeROIC: Neural Rendering of Objects from Online Image Collections
We present a novel method to acquire object representations from online image collections, capturing high-quality geometry and material properties of arbitrary objects from photographs with varying cameras, illumination, and backgrounds.
S3: Neural Shape, Skeleton, and Skinning Fields for 3D Human Modeling
Constructing and animating humans is an important component for building virtual worlds in a wide variety of applications such as virtual reality or robotics testing in simulation.
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.
ARCH: Animatable Reconstruction of Clothed Humans
In this paper, we propose ARCH (Animatable Reconstruction of Clothed Humans), a novel end-to-end framework for accurate reconstruction of animation-ready 3D clothed humans from a monocular image.
Ranked #3 on
3D Object Reconstruction From A Single Image
on BUFF
3D Object Reconstruction From A Single Image
3D Reconstruction
Learning Perspective Undistortion of Portraits
In contrast to the previous state-of-the-art approach, our method handles even portraits with extreme perspective distortion, as we avoid the inaccurate and error-prone step of first fitting a 3D face model.
PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization
We introduce Pixel-aligned Implicit Function (PIFu), a highly effective implicit representation that locally aligns pixels of 2D images with the global context of their corresponding 3D object.
Ranked #1 on
3D Object Reconstruction
on RenderPeople
SiCloPe: Silhouette-Based Clothed People
The synthesized silhouettes which are the most consistent with the input segmentation are fed into a deep visual hull algorithm for robust 3D shape prediction.
Deep Volumetric Video From Very Sparse Multi-View Performance Capture
We present a deep learning-based volumetric capture approach for performance capture using a passive and highly sparse multi-view capture system.
Realistic Dynamic Facial Textures From a Single Image Using GANs
By retargeting the PCA expression geometry from the source, as well as using the newly inferred texture, we can both animate the face and perform video face replacement on the source video using the target appearance.
Auto-Conditioned Recurrent Networks for Extended Complex Human Motion Synthesis
We present a real-time method for synthesizing highly complex human motions using a novel training regime we call the auto-conditioned Recurrent Neural Network (acRNN).
