Search Results for author:
Found 27 papers, 14 papers with code
Photogeometric Scene Flow for High-Detail Dynamic 3D Reconstruction
This paper proposes photogeometric scene flow (PGSF) for high-quality dynamic 3D reconstruction.
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
We present an approach to efficiently detect the 2D pose of multiple people in an image.
Ranked #6 on
Keypoint Detection
on MPII Multi-Person
Panoptic Studio: A Massively Multiview System for Social Interaction Capture
The core challenges in capturing social interactions are: (1) occlusion is functional and frequent; (2) subtle motion needs to be measured over a space large enough to host a social group; (3) human appearance and configuration variation is immense; and (4) attaching markers to the body may prime the nature of interactions.
Hand Keypoint Detection in Single Images using Multiview Bootstrapping
The method is used to train a hand keypoint detector for single images.
Total Capture: A 3D Deformation Model for Tracking Faces, Hands, and Bodies
We present a unified deformation model for the markerless capture of multiple scales of human movement, including facial expressions, body motion, and hand gestures.
Deep Appearance Models for Face Rendering
At inference time, we condition the decoding network on the viewpoint of the camera in order to generate the appropriate texture for rendering.
OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation
Ranked #4 on
Pose Estimation
on MPII Single Person
LBS Autoencoder: Self-supervised Fitting of Articulated Meshes to Point Clouds
As input, we take a sequence of point clouds to be registered as well as an artist-rigged mesh, i. e. a template mesh equipped with a linear-blend skinning (LBS) deformation space parameterized by a skeleton hierarchy.
Towards Social Artificial Intelligence: Nonverbal Social Signal Prediction in A Triadic Interaction
We present a new research task and a dataset to understand human social interactions via computational methods, to ultimately endow machines with the ability to encode and decode a broad channel of social signals humans use.
Neural Volumes: Learning Dynamic Renderable Volumes from Images
Modeling and rendering of dynamic scenes is challenging, as natural scenes often contain complex phenomena such as thin structures, evolving topology, translucency, scattering, occlusion, and biological motion.
Single-Network Whole-Body Pose Estimation
We present the first single-network approach for 2D~whole-body pose estimation, which entails simultaneous localization of body, face, hands, and feet keypoints.
PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization
Although current approaches have demonstrated the potential in real world settings, they still fail to produce reconstructions with the level of detail often present in the input images.
Ranked #1 on
3D Object Reconstruction From A Single Image
on BUFF
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.
Learning Compositional Radiance Fields of Dynamic Human Heads
In addition, we show that the learned dynamic radiance field can be used to synthesize novel unseen expressions based on a global animation code.
PVA: Pixel-aligned Volumetric Avatars
Volumetric models typically employ a global code to represent facial expressions, such that they can be driven by a small set of animation parameters.
Mixture of Volumetric Primitives for Efficient Neural Rendering
Real-time rendering and animation of humans is a core function in games, movies, and telepresence applications.
SimPoE: Simulated Character Control for 3D Human Pose Estimation
Based on this refined kinematic pose, the policy learns to compute dynamics-based control (e. g., joint torques) of the character to advance the current-frame pose estimate to the pose estimate of the next frame.
Ranked #229 on
3D Human Pose Estimation
on Human3.6M
Pixel Codec Avatars
Telecommunication with photorealistic avatars in virtual or augmented reality is a promising path for achieving authentic face-to-face communication in 3D over remote physical distances.
Driving-Signal Aware Full-Body Avatars
The core intuition behind our method is that better drivability and generalization can be achieved by disentangling the driving signals and remaining generative factors, which are not available during animation.
Pixel-Aligned Volumetric Avatars
Volumetric models typically employ a global code to represent facial expressions, such that they can be driven by a small set of animation parameters.
Ranked #5 on
Generalizable Novel View Synthesis
on ZJU-MoCap
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.
Drivable Volumetric Avatars using Texel-Aligned Features
To circumvent this, we propose a novel volumetric avatar representation by extending mixtures of volumetric primitives to articulated objects.
Multiface: A Dataset for Neural Face Rendering
Along with the release of the dataset, we conduct ablation studies on the influence of different model architectures toward the model's interpolation capacity of novel viewpoint and expressions.
MEGANE: Morphable Eyeglass and Avatar Network
However, modeling the geometric and appearance interactions of glasses and the face of virtual representations of humans is challenging.
RelightableHands: Efficient Neural Relighting of Articulated Hand Models
To achieve generalization, we condition the student model with physics-inspired illumination features such as visibility, diffuse shading, and specular reflections computed on a coarse proxy geometry, maintaining a small computational overhead.
Relightable Gaussian Codec Avatars
The fidelity of relighting is bounded by both geometry and appearance representations.
URHand: Universal Relightable Hands
To simplify the personalization process while retaining photorealism, we build a powerful universal relightable prior based on neural relighting from multi-view images of hands captured in a light stage with hundreds of identities.
