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Found 13 papers, 3 papers with code
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.
Diffusion Shape Prior for Wrinkle-Accurate Cloth Registration
Registering clothes from 4D scans with vertex-accurate correspondence is challenging, yet important for dynamic appearance modeling and physics parameter estimation from real-world data.
Drivable Avatar Clothing: Faithful Full-Body Telepresence with Dynamic Clothing Driven by Sparse RGB-D Input
Clothing is an important part of human appearance but challenging to model in photorealistic avatars.
Dressing Avatars: Deep Photorealistic Appearance for Physically Simulated Clothing
The key idea is to introduce a neural clothing appearance model that operates on top of explicit geometry: at training time we use high-fidelity tracking, whereas at animation time we rely on physically simulated geometry.
Garment Avatars: Realistic Cloth Driving using Pattern Registration
Here, we propose an end-to-end pipeline for building drivable representations for clothing.
Modeling Clothing as a Separate Layer for an Animatable Human Avatar
To address these difficulties, we propose a method to build an animatable clothed body avatar with an explicit representation of the clothing on the upper body from multi-view captured videos.
Revitalizing Optimization for 3D Human Pose and Shape Estimation: A Sparse Constrained Formulation
We propose a novel sparse constrained formulation and from it derive a real-time optimization method for 3D human pose and shape estimation.
MonoClothCap: Towards Temporally Coherent Clothing Capture from Monocular RGB Video
A differentiable renderer is utilized to align our captured shapes to the input frames by minimizing the difference in both silhouette, segmentation, and texture.
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.
You2Me: Inferring Body Pose in Egocentric Video via First and Second Person Interactions
The body pose of a person wearing a camera is of great interest for applications in augmented reality, healthcare, and robotics, yet much of the person's body is out of view for a typical wearable camera.
Monocular Total Capture: Posing Face, Body, and Hands in the Wild
We present the first method to capture the 3D total motion of a target person from a monocular view input.
Ranked #28 on
Monocular 3D Human Pose Estimation
on Human3.6M
Surface Normals in the Wild
We study the problem of single-image depth estimation for images in the wild.
Semantic Object Parsing with Local-Global Long Short-Term Memory
The long chains of sequential computation by stacked LG-LSTM layers also enable each pixel to sense a much larger region for inference benefiting from the memorization of previous dependencies in all positions along all dimensions.
