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Found 16 papers, 8 papers with code
imGHUM: Implicit Generative Models of 3D Human Shape and Articulated Pose
We present imGHUM, the first holistic generative model of 3D human shape and articulated pose, represented as a signed distance function.
Video Based Reconstruction of 3D People Models
This paper describes how to obtain accurate 3D body models and texture of arbitrary people from a single, monocular video in which a person is moving.
Learning to Transfer Texture from Clothing Images to 3D Humans
In this paper, we present a simple yet effective method to automatically transfer textures of clothing images (front and back) to 3D garments worn on top SMPL, in real time.
Implicit Functions in Feature Space for 3D Shape Reconstruction and Completion
To solve this, we propose Implicit Feature Networks (IF-Nets), which deliver continuous outputs, can handle multiple topologies, and complete shapes for missing or sparse input data retaining the nice properties of recent learned implicit functions, but critically they can also retain detail when it is present in the input data, and can reconstruct articulated humans.
Tex2Shape: Detailed Full Human Body Geometry From a Single Image
From a partial texture, we estimate detailed normal and vector displacement maps, which can be applied to a low-resolution smooth body model to add detail and clothing.
Learning to Reconstruct People in Clothing from a Single RGB Camera
We present a learning-based model to infer the personalized 3D shape of people from a few frames (1-8) of a monocular video in which the person is moving, in less than 10 seconds with a reconstruction accuracy of 5mm.
Detailed Human Avatars from Monocular Video
We present a novel method for high detail-preserving human avatar creation from monocular video.
SelfPose: 3D Egocentric Pose Estimation from a Headset Mounted Camera
The quantitative evaluation, on synthetic and real-world datasets, shows that our strategy leads to substantial improvements in accuracy over state of the art egocentric approaches.
Optical Flow-based 3D Human Motion Estimation from Monocular Video
Under the assumption that starting from an initial pose optical flow constrains subsequent human motion, we exploit flow to find temporally coherent human poses of a motion sequence.
H-NeRF: Neural Radiance Fields for Rendering and Temporal Reconstruction of Humans in Motion
This allows us to robustly fuse information from sparse views and generalize well beyond the poses or views observed in training.
Photorealistic Monocular 3D Reconstruction of Humans Wearing Clothing
We present PHORHUM, a novel, end-to-end trainable, deep neural network methodology for photorealistic 3D human reconstruction given just a monocular RGB image.
Structured 3D Features for Reconstructing Controllable Avatars
We introduce Structured 3D Features, a model based on a novel implicit 3D representation that pools pixel-aligned image features onto dense 3D points sampled from a parametric, statistical human mesh surface.
PhoMoH: Implicit Photorealistic 3D Models of Human Heads
We present PhoMoH, a neural network methodology to construct generative models of photo-realistic 3D geometry and appearance of human heads including hair, beards, an oral cavity, and clothing.
SPHEAR: Spherical Head Registration for Complete Statistical 3D Modeling
We present \emph{SPHEAR}, an accurate, differentiable parametric statistical 3D human head model, enabled by a novel 3D registration method based on spherical embeddings.
Score Distillation Sampling with Learned Manifold Corrective
Score Distillation Sampling (SDS) is a recent but already widely popular method that relies on an image diffusion model to control optimization problems using text prompts.
VLOGGER: Multimodal Diffusion for Embodied Avatar Synthesis
We propose VLOGGER, a method for audio-driven human video generation from a single input image of a person, which builds on the success of recent generative diffusion models.
