Search Results for author:
Found 38 papers, 9 papers with code
Approaching human 3D shape perception with neurally mappable models
Finally, we find that while the models trained with multi-view learning objectives are able to partially generalize to new object categories, they fall short of human alignment.
Diffusion with Forward Models: Solving Stochastic Inverse Problems Without Direct Supervision
Denoising diffusion models are a powerful type of generative models used to capture complex distributions of real-world signals.
AvatarStudio: Text-driven Editing of 3D Dynamic Human Head Avatars
Our method edits the full head in a canonical space, and then propagates these edits to remaining time steps via a pretrained deformation network.
FlowCam: Training Generalizable 3D Radiance Fields without Camera Poses via Pixel-Aligned Scene Flow
SE(3) camera pose estimation is then performed via a weighted least-squares fit to the scene flow field.
Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold
Synthesizing visual content that meets users' needs often requires flexible and precise controllability of the pose, shape, expression, and layout of the generated objects.
Learning to Render Novel Views from Wide-Baseline Stereo Pairs
We conduct extensive comparisons on held-out test scenes across two real-world datasets, significantly outperforming prior work on novel view synthesis from sparse image observations and achieving multi-view-consistent novel view synthesis.
ConceptFusion: Open-set Multimodal 3D Mapping
ConceptFusion leverages the open-set capabilities of today's foundation models pre-trained on internet-scale data to reason about concepts across modalities such as natural language, images, and audio.
gCoRF: Generative Compositional Radiance Fields
We start with a global generative model (GAN) and learn to decompose it into different semantic parts using supervision from 2D segmentation masks.
Neural Radiance Transfer Fields for Relightable Novel-view Synthesis with Global Illumination
Given a set of images of a scene, the re-rendering of this scene from novel views and lighting conditions is an important and challenging problem in Computer Vision and Graphics.
Neural Groundplans: Persistent Neural Scene Representations from a Single Image
We present a method to map 2D image observations of a scene to a persistent 3D scene representation, enabling novel view synthesis and disentangled representation of the movable and immovable components of the scene.
GAN2X: Non-Lambertian Inverse Rendering of Image GANs
2D images are observations of the 3D physical world depicted with the geometry, material, and illumination components.
FIRe: Fast Inverse Rendering using Directional and Signed Distance Functions
To this end, we learn a signed distance function (SDF) along with our DDF model to represent a class of shapes.
Disentangled3D: Learning a 3D Generative Model with Disentangled Geometry and Appearance from Monocular Images
Our model can disentangle the geometry and appearance variations in the scene, i. e., we can independently sample from the geometry and appearance spaces of the generative model.
Advances in Neural Rendering
The reconstruction of such a scene representation from observations using differentiable rendering losses is known as inverse graphics or inverse rendering.
StyleVideoGAN: A Temporal Generative Model using a Pretrained StyleGAN
We demonstrate that it suffices to train our temporal architecture on only 10 minutes of footage of 1 subject for about 6 hours.
Egocentric Videoconferencing
Even holding a mobile phone camera in the front of the face while sitting for a long duration is not convenient.
Efficient and Differentiable Shadow Computation for Inverse Problems
Differentiable rendering has received increasing interest for image-based inverse problems.
PhotoApp: Photorealistic Appearance Editing of Head Portraits
We present an approach for high-quality intuitive editing of the camera viewpoint and scene illumination in a portrait image.
Non-Rigid Neural Radiance Fields: Reconstruction and Novel View Synthesis of a Dynamic Scene From Monocular Video
We show that a single handheld consumer-grade camera is sufficient to synthesize sophisticated renderings of a dynamic scene from novel virtual camera views, e. g. a `bullet-time' video effect.
Monocular Real-time Full Body Capture with Inter-part Correlations
We present the first method for real-time full body capture that estimates shape and motion of body and hands together with a dynamic 3D face model from a single color image.
Ranked #11 on
3D Hand Pose Estimation
on FreiHAND
i3DMM: Deep Implicit 3D Morphable Model of Human Heads
Our approach has the following favorable properties: (i) It is the first full head morphable model that includes hair.
Learning Complete 3D Morphable Face Models from Images and Videos
Our network design and loss functions ensure a disentangled parameterization of not only identity and albedo, but also, for the first time, an expression basis.
PIE: Portrait Image Embedding for Semantic Control
We present the first approach for embedding real portrait images in the latent space of StyleGAN, which allows for intuitive editing of the head pose, facial expression, and scene illumination in the image.
Monocular Reconstruction of Neural Face Reflectance Fields
The reflectance field of a face describes the reflectance properties responsible for complex lighting effects including diffuse, specular, inter-reflection and self shadowing.
PatchNets: Patch-Based Generalizable Deep Implicit 3D Shape Representations
At the level of patches, objects across different categories share similarities, which leads to more generalizable models.
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.
StyleRig: Rigging StyleGAN for 3D Control over Portrait Images
StyleGAN generates photorealistic portrait images of faces with eyes, teeth, hair and context (neck, shoulders, background), but lacks a rig-like control over semantic face parameters that are interpretable in 3D, such as face pose, expressions, and scene illumination.
Neural Voice Puppetry: Audio-driven Facial Reenactment
Neural Voice Puppetry has a variety of use-cases, including audio-driven video avatars, video dubbing, and text-driven video synthesis of a talking head.
3D Morphable Face Models -- Past, Present and Future
In this paper, we provide a detailed survey of 3D Morphable Face Models over the 20 years since they were first proposed.
Text-based Editing of Talking-head Video
To edit a video, the user has to only edit the transcript, and an optimization strategy then chooses segments of the input corpus as base material.
EgoFace: Egocentric Face Performance Capture and Videorealistic Reenactment
Our lightweight setup allows operations in uncontrolled environments, and lends itself to telepresence applications such as video-conferencing from dynamic environments.
DEMEA: Deep Mesh Autoencoders for Non-Rigidly Deforming Objects
Mesh autoencoders are commonly used for dimensionality reduction, sampling and mesh modeling.
FML: Face Model Learning from Videos
In contrast, we propose multi-frame video-based self-supervised training of a deep network that (i) learns a face identity model both in shape and appearance while (ii) jointly learning to reconstruct 3D faces.
Deep Video Portraits
In order to enable source-to-target video re-animation, we render a synthetic target video with the reconstructed head animation parameters from a source video, and feed it into the trained network -- thus taking full control of the target.
A Hybrid Model for Identity Obfuscation by Face Replacement
As more and more personal photos are shared and tagged in social media, avoiding privacy risks such as unintended recognition becomes increasingly challenging.
Self-supervised Multi-level Face Model Learning for Monocular Reconstruction at over 250 Hz
To alleviate this problem, we present the first approach that jointly learns 1) a regressor for face shape, expression, reflectance and illumination on the basis of 2) a concurrently learned parametric face model.
InverseFaceNet: Deep Monocular Inverse Face Rendering
In contrast, we propose to recover high-quality facial pose, shape, expression, reflectance and illumination using a deep neural network that is trained using a large, synthetically created training corpus.
MoFA: Model-based Deep Convolutional Face Autoencoder for Unsupervised Monocular Reconstruction
In this work we propose a novel model-based deep convolutional autoencoder that addresses the highly challenging problem of reconstructing a 3D human face from a single in-the-wild color image.
