1,103 research outputs found
From 3D scan to body pressure of compression garments
Human bodies come under loads in sports. For safety or other purposes, athletes wear compression garments to help avoid wrong postures or movement. We assessed anthropometrics of elite rowers, and found significant differences with the general population, indicating compression garments would behave differently for the athletes. By combining 3D scanning technique and FEM modelling software, we were able to predict compression garment performance on part of the athlete bodies . Abaqus Explicit solver was applied to simulate movement of athletes actually putting on a compression garment, and to track stress distribution during the process
High-Fidelity 3D Head Avatars Reconstruction through Spatially-Varying Expression Conditioned Neural Radiance Field
One crucial aspect of 3D head avatar reconstruction lies in the details of
facial expressions. Although recent NeRF-based photo-realistic 3D head avatar
methods achieve high-quality avatar rendering, they still encounter challenges
retaining intricate facial expression details because they overlook the
potential of specific expression variations at different spatial positions when
conditioning the radiance field. Motivated by this observation, we introduce a
novel Spatially-Varying Expression (SVE) conditioning. The SVE can be obtained
by a simple MLP-based generation network, encompassing both spatial positional
features and global expression information. Benefiting from rich and diverse
information of the SVE at different positions, the proposed SVE-conditioned
neural radiance field can deal with intricate facial expressions and achieve
realistic rendering and geometry details of high-fidelity 3D head avatars.
Additionally, to further elevate the geometric and rendering quality, we
introduce a new coarse-to-fine training strategy, including a geometry
initialization strategy at the coarse stage and an adaptive importance sampling
strategy at the fine stage. Extensive experiments indicate that our method
outperforms other state-of-the-art (SOTA) methods in rendering and geometry
quality on mobile phone-collected and public datasets.Comment: 9 pages, 5 figure
Computer-based tracking, analysis, and visualization of linguistically significant nonmanual events in American Sign Language (ASL)
Our linguistically annotated American Sign Language (ASL) corpora have formed a basis for research to automate detection by
computer of essential linguistic information conveyed through facial expressions and head movements. We have tracked head position
and facial deformations, and used computational learning to discern specific grammatical markings. Our ability to detect, identify, and
temporally localize the occurrence of such markings in ASL videos has recently been improved by incorporation of (1) new techniques
for deformable model-based 3D tracking of head position and facial expressions, which provide significantly better tracking accuracy
and recover quickly from temporary loss of track due to occlusion; and (2) a computational learning approach incorporating 2-level
Conditional Random Fields (CRFs), suited to the multi-scale spatio-temporal characteristics of the data, which analyses not only
low-level appearance characteristics, but also the patterns that enable identification of significant gestural components, such as
periodic head movements and raised or lowered eyebrows. Here we summarize our linguistically motivated computational approach
and the results for detection and recognition of nonmanual grammatical markings; demonstrate our data visualizations, and discuss the
relevance for linguistic research; and describe work underway to enable such visualizations to be produced over large corpora and
shared publicly on the Web
BakedAvatar: Baking Neural Fields for Real-Time Head Avatar Synthesis
Synthesizing photorealistic 4D human head avatars from videos is essential
for VR/AR, telepresence, and video game applications. Although existing Neural
Radiance Fields (NeRF)-based methods achieve high-fidelity results, the
computational expense limits their use in real-time applications. To overcome
this limitation, we introduce BakedAvatar, a novel representation for real-time
neural head avatar synthesis, deployable in a standard polygon rasterization
pipeline. Our approach extracts deformable multi-layer meshes from learned
isosurfaces of the head and computes expression-, pose-, and view-dependent
appearances that can be baked into static textures for efficient rasterization.
We thus propose a three-stage pipeline for neural head avatar synthesis, which
includes learning continuous deformation, manifold, and radiance fields,
extracting layered meshes and textures, and fine-tuning texture details with
differential rasterization. Experimental results demonstrate that our
representation generates synthesis results of comparable quality to other
state-of-the-art methods while significantly reducing the inference time
required. We further showcase various head avatar synthesis results from
monocular videos, including view synthesis, face reenactment, expression
editing, and pose editing, all at interactive frame rates.Comment: ACM Transactions on Graphics (SIGGRAPH Asia 2023). Project Page:
https://buaavrcg.github.io/BakedAvata
A survey of real-time crowd rendering
In this survey we review, classify and compare existing approaches for real-time crowd rendering. We first overview character animation techniques, as they are highly tied to crowd rendering performance, and then we analyze the state of the art in crowd rendering. We discuss different representations for level-of-detail (LoD) rendering of animated characters, including polygon-based, point-based, and image-based techniques, and review different criteria for runtime LoD selection. Besides LoD approaches, we review classic acceleration schemes, such as frustum culling and occlusion culling, and describe how they can be adapted to handle crowds of animated characters. We also discuss specific acceleration techniques for crowd rendering, such as primitive pseudo-instancing, palette skinning, and dynamic key-pose caching, which benefit from current graphics hardware. We also address other factors affecting performance and realism of crowds such as lighting, shadowing, clothing and variability. Finally we provide an exhaustive comparison of the most relevant approaches in the field.Peer ReviewedPostprint (author's final draft
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. Our model learns to predict the parameters of a statistical body model and instance displacements that add clothing and hair to the shape. The model achieves fast and accurate predictions based on two key design choices. First, by predicting shape in a canonical T-pose space, the network learns to encode the images of the person into pose-invariant latent codes, where the information is fused. Second, based on the observation that feed-forward predictions are fast but do not always align with the input images, we predict using both, bottom-up and top-down streams (one per view) allowing information to flow in both directions. Learning relies only on synthetic 3D data. Once learned, the model can take a variable number of frames as input, and is able to reconstruct shapes even from a single image with an accuracy of 6mm. Results on 3 different datasets demonstrate the efficacy and accuracy of our approach
Instant Volumetric Head Avatars
We present Instant Volumetric Head Avatars (INSTA), a novel approach for
reconstructing photo-realistic digital avatars instantaneously. INSTA models a
dynamic neural radiance field based on neural graphics primitives embedded
around a parametric face model. Our pipeline is trained on a single monocular
RGB portrait video that observes the subject under different expressions and
views. While state-of-the-art methods take up to several days to train an
avatar, our method can reconstruct a digital avatar in less than 10 minutes on
modern GPU hardware, which is orders of magnitude faster than previous
solutions. In addition, it allows for the interactive rendering of novel poses
and expressions. By leveraging the geometry prior of the underlying parametric
face model, we demonstrate that INSTA extrapolates to unseen poses. In
quantitative and qualitative studies on various subjects, INSTA outperforms
state-of-the-art methods regarding rendering quality and training time.Comment: Website: https://zielon.github.io/insta/ Video:
https://youtu.be/HOgaeWTih7
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