Search Results for author:
Found 15 papers, 4 papers with code
SpotlessSplats: Ignoring Distractors in 3D Gaussian Splatting
3D Gaussian Splatting (3DGS) is a promising technique for 3D reconstruction, offering efficient training and rendering speeds, making it suitable for real-time applications. However, current methods require highly controlled environments (no moving people or wind-blown elements, and consistent lighting) to meet the inter-view consistency assumption of 3DGS.
PhysAvatar: Learning the Physics of Dressed 3D Avatars from Visual Observations
This marks a significant advancement towards modeling photorealistic digital humans using physically based inverse rendering with physics in the loop.
Alchemist: Parametric Control of Material Properties with Diffusion Models
We propose a method to control material attributes of objects like roughness, metallic, albedo, and transparency in real images.
ZeroNVS: Zero-Shot 360-Degree View Synthesis from a Single Image
Further, we observe that Score Distillation Sampling (SDS) tends to truncate the distribution of complex backgrounds during distillation of 360-degree scenes, and propose "SDS anchoring" to improve the diversity of synthesized novel views.
Preface: A Data-driven Volumetric Prior for Few-shot Ultra High-resolution Face Synthesis
NeRFs have enabled highly realistic synthesis of human faces including complex appearance and reflectance effects of hair and skin.
ITI-GEN: Inclusive Text-to-Image Generation
We show that, for some attributes, images can represent concepts more expressively than text.
TEGLO: High Fidelity Canonical Texture Mapping from Single-View Images
Our key insight is that by mapping the input image pixels onto the texture space we can achieve near perfect reconstruction (>= 74 dB PSNR at 1024^2 resolution).
MELON: NeRF with Unposed Images in SO(3)
Neural radiance fields enable novel-view synthesis and scene reconstruction with photorealistic quality from a few images, but require known and accurate camera poses.
Attention Beats Concatenation for Conditioning Neural Fields
Neural fields model signals by mapping coordinate inputs to sampled values.
Kubric: A scalable dataset generator
Data is the driving force of machine learning, with the amount and quality of training data often being more important for the performance of a system than architecture and training details.
Solving Inverse Problems with NerfGANs
We introduce a novel framework for solving inverse problems using NeRF-style generative models.
LOLNeRF: Learn from One Look
We present a method for learning a generative 3D model based on neural radiance fields, trained solely from data with only single views of each object.
GazeGAN - Unpaired Adversarial Image Generation for Gaze Estimation
Recent research has demonstrated the ability to estimate gaze on mobile devices by performing inference on the image from the phone's front-facing camera, and without requiring specialized hardware.
