6,729 research outputs found
Learning to Dress {3D} People in Generative Clothing
Three-dimensional human body models are widely used in the analysis of human
pose and motion. Existing models, however, are learned from minimally-clothed
3D scans and thus do not generalize to the complexity of dressed people in
common images and videos. Additionally, current models lack the expressive
power needed to represent the complex non-linear geometry of pose-dependent
clothing shapes. To address this, we learn a generative 3D mesh model of
clothed people from 3D scans with varying pose and clothing. Specifically, we
train a conditional Mesh-VAE-GAN to learn the clothing deformation from the
SMPL body model, making clothing an additional term in SMPL. Our model is
conditioned on both pose and clothing type, giving the ability to draw samples
of clothing to dress different body shapes in a variety of styles and poses. To
preserve wrinkle detail, our Mesh-VAE-GAN extends patchwise discriminators to
3D meshes. Our model, named CAPE, represents global shape and fine local
structure, effectively extending the SMPL body model to clothing. To our
knowledge, this is the first generative model that directly dresses 3D human
body meshes and generalizes to different poses. The model, code and data are
available for research purposes at https://cape.is.tue.mpg.de.Comment: CVPR-2020 camera ready. Code and data are available at
https://cape.is.tue.mpg.d
Next3D: Generative Neural Texture Rasterization for 3D-Aware Head Avatars
3D-aware generative adversarial networks (GANs) synthesize high-fidelity and
multi-view-consistent facial images using only collections of single-view 2D
imagery. Towards fine-grained control over facial attributes, recent efforts
incorporate 3D Morphable Face Model (3DMM) to describe deformation in
generative radiance fields either explicitly or implicitly. Explicit methods
provide fine-grained expression control but cannot handle topological changes
caused by hair and accessories, while implicit ones can model varied topologies
but have limited generalization caused by the unconstrained deformation fields.
We propose a novel 3D GAN framework for unsupervised learning of generative,
high-quality and 3D-consistent facial avatars from unstructured 2D images. To
achieve both deformation accuracy and topological flexibility, we propose a 3D
representation called Generative Texture-Rasterized Tri-planes. The proposed
representation learns Generative Neural Textures on top of parametric mesh
templates and then projects them into three orthogonal-viewed feature planes
through rasterization, forming a tri-plane feature representation for volume
rendering. In this way, we combine both fine-grained expression control of
mesh-guided explicit deformation and the flexibility of implicit volumetric
representation. We further propose specific modules for modeling mouth interior
which is not taken into account by 3DMM. Our method demonstrates
state-of-the-art 3D-aware synthesis quality and animation ability through
extensive experiments. Furthermore, serving as 3D prior, our animatable 3D
representation boosts multiple applications including one-shot facial avatars
and 3D-aware stylization.Comment: Project page: https://mrtornado24.github.io/Next3D
Articulation-aware Canonical Surface Mapping
We tackle the tasks of: 1) predicting a Canonical Surface Mapping (CSM) that
indicates the mapping from 2D pixels to corresponding points on a canonical
template shape, and 2) inferring the articulation and pose of the template
corresponding to the input image. While previous approaches rely on keypoint
supervision for learning, we present an approach that can learn without such
annotations. Our key insight is that these tasks are geometrically related, and
we can obtain supervisory signal via enforcing consistency among the
predictions. We present results across a diverse set of animal object
categories, showing that our method can learn articulation and CSM prediction
from image collections using only foreground mask labels for training. We
empirically show that allowing articulation helps learn more accurate CSM
prediction, and that enforcing the consistency with predicted CSM is similarly
critical for learning meaningful articulation.Comment: To appear at CVPR 2020, project page
https://nileshkulkarni.github.io/acsm
Action in Mind: Neural Models for Action and Intention Perception
To notice, recognize, and ultimately perceive the others’ actions and to discern the intention behind those observed actions is an essential skill for social communications and improves markedly the chances of survival. Encountering dangerous behavior, for instance, from a person or an animal requires an immediate and suitable reaction. In addition, as social creatures, we need to perceive, interpret, and judge correctly the other individual’s actions as a fundamental skill for our social life. In other words, our survival and success in adaptive social behavior and nonverbal communication depends heavily on our ability to thrive in complex social situations. However, it has been shown that humans spontaneously can decode animacy and social interactions even from strongly impoverished stimuli and this is a fundamental part of human experience that develops early in infancy and is shared with other primates. In addition, it is well established that perceptual and motor representations of actions are tightly coupled and both share common mechanisms. This coupling between action perception and action execution plays a critical role in action understanding as postulated in various studies and they are potentially important for our social cognition. This interaction likely is mediated by action-selective neurons in the superior temporal sulcus (STS), premotor and parietal cortex. STS and TPJ have been identified also as coarse neural substrate for the processing of social interactions stimuli. Despite this localization, the underlying exact neural circuits of this processing remain unclear. The aim of this thesis is to understand the neural mechanisms behind the action perception coupling and to investigate further how human brain perceive different classes of social interactions. To achieve this goal, first we introduce a neural model that provides a unifying account for multiple experiments on the interaction between action execution and action perception. The model reproduces correctly the interactions between action observation and execution in several experiments and provides a link towards electrophysiological detailed models of relevant circuits. This model might thus provide a starting point for the detailed quantitative investigation how motor plans interact with perceptual action representations at the level of single-cell mechanisms. Second we present a simple neural model that reproduces some of the key observations in psychophysical experiments about the perception of animacy and social interactions from stimuli. Even in its simple form the model proves that animacy and social interaction judgments partly might be derived by very elementary operations in hierarchical neural vision systems, without a need of sophisticated or accurate probabilistic inference
TADA! Text to Animatable Digital Avatars
We introduce TADA, a simple-yet-effective approach that takes textual
descriptions and produces expressive 3D avatars with high-quality geometry and
lifelike textures, that can be animated and rendered with traditional graphics
pipelines. Existing text-based character generation methods are limited in
terms of geometry and texture quality, and cannot be realistically animated due
to inconsistent alignment between the geometry and the texture, particularly in
the face region. To overcome these limitations, TADA leverages the synergy of a
2D diffusion model and an animatable parametric body model. Specifically, we
derive an optimizable high-resolution body model from SMPL-X with 3D
displacements and a texture map, and use hierarchical rendering with score
distillation sampling (SDS) to create high-quality, detailed, holistic 3D
avatars from text. To ensure alignment between the geometry and texture, we
render normals and RGB images of the generated character and exploit their
latent embeddings in the SDS training process. We further introduce various
expression parameters to deform the generated character during training,
ensuring that the semantics of our generated character remain consistent with
the original SMPL-X model, resulting in an animatable character. Comprehensive
evaluations demonstrate that TADA significantly surpasses existing approaches
on both qualitative and quantitative measures. TADA enables creation of
large-scale digital character assets that are ready for animation and
rendering, while also being easily editable through natural language. The code
will be public for research purposes
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