1,765 research outputs found
Tex2Shape: Detailed Full Human Body Geometry From a Single Image
We present a simple yet effective method to infer detailed full human body shape from only a single photograph. Our model can infer full-body shape including face, hair, and clothing including wrinkles at interactive frame-rates. Results feature details even on parts that are occluded in the input image. Our main idea is to turn shape regression into an aligned image-to-image translation problem. The input to our method is a partial texture map of the visible region obtained from off-the-shelf methods. 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. Despite being trained purely with synthetic data, our model generalizes well to real-world photographs. Numerous results demonstrate the versatility and robustness of our method
Tex2Shape: Detailed Full Human Body Geometry From a Single Image
We present a simple yet effective method to infer detailed full human body
shape from only a single photograph. Our model can infer full-body shape
including face, hair, and clothing including wrinkles at interactive
frame-rates. Results feature details even on parts that are occluded in the
input image. Our main idea is to turn shape regression into an aligned
image-to-image translation problem. The input to our method is a partial
texture map of the visible region obtained from off-the-shelf methods. 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. Despite being trained purely with synthetic data, our model
generalizes well to real-world photographs. Numerous results demonstrate the
versatility and robustness of our method
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. We first automatically compute training pairs of images
with aligned 3D garments using a custom non-rigid 3D to 2D registration method,
which is accurate but slow. Using these pairs, we learn a mapping from pixels
to the 3D garment surface. Our idea is to learn dense correspondences from
garment image silhouettes to a 2D-UV map of a 3D garment surface using shape
information alone, completely ignoring texture, which allows us to generalize
to the wide range of web images. Several experiments demonstrate that our model
is more accurate than widely used baselines such as thin-plate-spline warping
and image-to-image translation networks while being orders of magnitude faster.
Our model opens the door for applications such as virtual try-on, and allows
for generation of 3D humans with varied textures which is necessary for
learning.Comment: IEEE Conference on Computer Vision and Pattern Recognitio
Rõivaste tekstureerimine kasutades Kinect V2.0
This thesis describes three new garment retexturing methods for FitsMe virtual fitting room applications
using data from Microsoft Kinect II RGB-D camera.
The first method, which is introduced, is an automatic technique for garment retexturing using
a single RGB-D image and infrared information obtained from Kinect II. First, the garment
is segmented out from the image using GrabCut or depth segmentation. Then texture domain
coordinates are computed for each pixel belonging to the garment using normalized 3D information.
Afterwards, shading is applied to the new colors from the texture image.
The second method proposed in this work is about 2D to 3D garment retexturing where a segmented
garment of a manikin or person is matched to a new source garment and retextured,
resulting in augmented images in which the new source garment is transferred to the manikin
or person. The problem is divided into garment boundary matching based on point set registration
which uses Gaussian mixture models and then interpolate inner points using surface
topology extracted through geodesic paths, which leads to a more realistic result than standard
approaches.
The final contribution of this thesis is by introducing another novel method which is used for
increasing the texture quality of a 3D model of a garment, by using the same Kinect frame
sequence which was used in the model creation. Firstly, a structured mesh must be created
from the 3D model, therefore the 3D model is wrapped to a base model with defined seams and
texture map. Afterwards frames are matched to the newly created model and by process of ray
casting the color values of the Kinect frames are mapped to the UV map of the 3D model
HeadOn: Real-time Reenactment of Human Portrait Videos
We propose HeadOn, the first real-time source-to-target reenactment approach
for complete human portrait videos that enables transfer of torso and head
motion, face expression, and eye gaze. Given a short RGB-D video of the target
actor, we automatically construct a personalized geometry proxy that embeds a
parametric head, eye, and kinematic torso model. A novel real-time reenactment
algorithm employs this proxy to photo-realistically map the captured motion
from the source actor to the target actor. On top of the coarse geometric
proxy, we propose a video-based rendering technique that composites the
modified target portrait video via view- and pose-dependent texturing, and
creates photo-realistic imagery of the target actor under novel torso and head
poses, facial expressions, and gaze directions. To this end, we propose a
robust tracking of the face and torso of the source actor. We extensively
evaluate our approach and show significant improvements in enabling much
greater flexibility in creating realistic reenacted output videos.Comment: Video: https://www.youtube.com/watch?v=7Dg49wv2c_g Presented at
Siggraph'1
Text-Guided Generation and Editing of Compositional 3D Avatars
Our goal is to create a realistic 3D facial avatar with hair and accessories
using only a text description. While this challenge has attracted significant
recent interest, existing methods either lack realism, produce unrealistic
shapes, or do not support editing, such as modifications to the hairstyle. We
argue that existing methods are limited because they employ a monolithic
modeling approach, using a single representation for the head, face, hair, and
accessories. Our observation is that the hair and face, for example, have very
different structural qualities that benefit from different representations.
Building on this insight, we generate avatars with a compositional model, in
which the head, face, and upper body are represented with traditional 3D
meshes, and the hair, clothing, and accessories with neural radiance fields
(NeRF). The model-based mesh representation provides a strong geometric prior
for the face region, improving realism while enabling editing of the person's
appearance. By using NeRFs to represent the remaining components, our method is
able to model and synthesize parts with complex geometry and appearance, such
as curly hair and fluffy scarves. Our novel system synthesizes these
high-quality compositional avatars from text descriptions. The experimental
results demonstrate that our method, Text-guided generation and Editing of
Compositional Avatars (TECA), produces avatars that are more realistic than
those of recent methods while being editable because of their compositional
nature. For example, our TECA enables the seamless transfer of compositional
features like hairstyles, scarves, and other accessories between avatars. This
capability supports applications such as virtual try-on.Comment: Home page: https://yfeng95.github.io/tec
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