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Found 25 papers, 6 papers with code
Du²Net: Learning Depth Estimation from Dual-Cameras and Dual-Pixels
Computational stereo has reached a high level of accuracy, but degrades in the presence of occlusions, repeated textures, and correspondence errors along edges.
Efficient 3D Implicit Head Avatar with Mesh-anchored Hash Table Blendshapes
To address these challenges, we propose a novel fast 3D neural implicit head avatar model that achieves real-time rendering while maintaining fine-grained controllability and high rendering quality.
CHOSEN: Contrastive Hypothesis Selection for Multi-View Depth Refinement
We propose CHOSEN, a simple yet flexible, robust and effective multi-view depth refinement framework.
One2Avatar: Generative Implicit Head Avatar For Few-shot User Adaptation
Traditional methods for constructing high-quality, personalized head avatars from monocular videos demand extensive face captures and training time, posing a significant challenge for scalability.
Sandwiched Compression: Repurposing Standard Codecs with Neural Network Wrappers
Through a variety of examples, we apply the sandwich architecture to sources with different numbers of channels, higher resolution, higher dynamic range, and perceptual distortion measures.
MVDD: Multi-View Depth Diffusion Models
State-of-the-art results from extensive experiments demonstrate MVDD's excellent ability in 3D shape generation, depth completion, and its potential as a 3D prior for downstream tasks.
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.
Controllable Light Diffusion for Portraits
We introduce light diffusion, a novel method to improve lighting in portraits, softening harsh shadows and specular highlights while preserving overall scene illumination.
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.
Multi-Camera Lighting Estimation for Photorealistic Front-Facing Mobile Augmented Reality
This lack of support can be attributed to the unique challenges of obtaining 360$^\circ$ HDR environment maps, an ideal format of lighting representation, from the front-facing camera and existing techniques.
Neural Rendering in a Room: Amodal 3D Understanding and Free-Viewpoint Rendering for the Closed Scene Composed of Pre-Captured Objects
We, as human beings, can understand and picture a familiar scene from arbitrary viewpoints given a single image, whereas this is still a grand challenge for computers.
VoLux-GAN: A Generative Model for 3D Face Synthesis with HDRI Relighting
We propose VoLux-GAN, a generative framework to synthesize 3D-aware faces with convincing relighting.
Multiresolution Deep Implicit Functions for 3D Shape Representation
To the best of our knowledge, MDIF is the first deep implicit function model that can at the same time (1) represent different levels of detail and allow progressive decoding; (2) support both encoder-decoder inference and decoder-only latent optimization, and fulfill multiple applications; (3) perform detailed decoder-only shape completion.
HumanGPS: Geodesic PreServing Feature for Dense Human Correspondences
In this paper, we address the problem of building dense correspondences between human images under arbitrary camera viewpoints and body poses.
Light Stage Super-Resolution: Continuous High-Frequency Relighting
The light stage has been widely used in computer graphics for the past two decades, primarily to enable the relighting of human faces.
Neural Light Transport for Relighting and View Synthesis
In particular, we show how to fuse previously seen observations of illuminants and views to synthesize a new image of the same scene under a desired lighting condition from a chosen viewpoint.
Learning Illumination from Diverse Portraits
We present a learning-based technique for estimating high dynamic range (HDR), omnidirectional illumination from a single low dynamic range (LDR) portrait image captured under arbitrary indoor or outdoor lighting conditions.
HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching
Contrary to many recent neural network approaches that operate on a full cost volume and rely on 3D convolutions, our approach does not explicitly build a volume and instead relies on a fast multi-resolution initialization step, differentiable 2D geometric propagation and warping mechanisms to infer disparity hypotheses.
Ranked #1 on
Stereo Depth Estimation
on KITTI2015
Deep Implicit Volume Compression
We describe a novel approach for compressing truncated signed distance fields (TSDF) stored in 3D voxel grids, and their corresponding textures.
State of the Art on Neural Rendering
Neural rendering is a new and rapidly emerging field that combines generative machine learning techniques with physical knowledge from computer graphics, e. g., by the integration of differentiable rendering into network training.
Du$^2$Net: Learning Depth Estimation from Dual-Cameras and Dual-Pixels
Computational stereo has reached a high level of accuracy, but degrades in the presence of occlusions, repeated textures, and correspondence errors along edges.
Volumetric Capture of Humans with a Single RGBD Camera via Semi-Parametric Learning
The key insight is to leverage previously seen "calibration" images of a given user to extrapolate what should be rendered in a novel viewpoint from the data available in the sensor.
LookinGood: Enhancing Performance Capture with Real-time Neural Re-Rendering
We take the novel approach to augment such real-time performance capture systems with a deep architecture that takes a rendering from an arbitrary viewpoint, and jointly performs completion, super resolution, and denoising of the imagery in real-time.
StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth Prediction
A first estimate of the disparity is computed in a very low resolution cost volume, then hierarchically the model re-introduces high-frequency details through a learned upsampling function that uses compact pixel-to-pixel refinement networks.
Ranked #2 on
Stereo Depth Estimation
on sceneflow
ActiveStereoNet: End-to-End Self-Supervised Learning for Active Stereo Systems
In this paper we present ActiveStereoNet, the first deep learning solution for active stereo systems.
