628 research outputs found
Low-latency compression of mocap data using learned spatial decorrelation transform
Due to the growing needs of human motion capture (mocap) in movie, video
games, sports, etc., it is highly desired to compress mocap data for efficient
storage and transmission. This paper presents two efficient frameworks for
compressing human mocap data with low latency. The first framework processes
the data in a frame-by-frame manner so that it is ideal for mocap data
streaming and time critical applications. The second one is clip-based and
provides a flexible tradeoff between latency and compression performance. Since
mocap data exhibits some unique spatial characteristics, we propose a very
effective transform, namely learned orthogonal transform (LOT), for reducing
the spatial redundancy. The LOT problem is formulated as minimizing square
error regularized by orthogonality and sparsity and solved via alternating
iteration. We also adopt a predictive coding and temporal DCT for temporal
decorrelation in the frame- and clip-based frameworks, respectively.
Experimental results show that the proposed frameworks can produce higher
compression performance at lower computational cost and latency than the
state-of-the-art methods.Comment: 15 pages, 9 figure
Human Motion Capture Data Tailored Transform Coding
Human motion capture (mocap) is a widely used technique for digitalizing
human movements. With growing usage, compressing mocap data has received
increasing attention, since compact data size enables efficient storage and
transmission. Our analysis shows that mocap data have some unique
characteristics that distinguish themselves from images and videos. Therefore,
directly borrowing image or video compression techniques, such as discrete
cosine transform, does not work well. In this paper, we propose a novel
mocap-tailored transform coding algorithm that takes advantage of these
features. Our algorithm segments the input mocap sequences into clips, which
are represented in 2D matrices. Then it computes a set of data-dependent
orthogonal bases to transform the matrices to frequency domain, in which the
transform coefficients have significantly less dependency. Finally, the
compression is obtained by entropy coding of the quantized coefficients and the
bases. Our method has low computational cost and can be easily extended to
compress mocap databases. It also requires neither training nor complicated
parameter setting. Experimental results demonstrate that the proposed scheme
significantly outperforms state-of-the-art algorithms in terms of compression
performance and speed
GeoUDF: Surface Reconstruction from 3D Point Clouds via Geometry-guided Distance Representation
We present a learning-based method, namely GeoUDF,to tackle the long-standing
and challenging problem of reconstructing a discrete surface from a sparse
point cloud.To be specific, we propose a geometry-guided learning method for
UDF and its gradient estimation that explicitly formulates the unsigned
distance of a query point as the learnable affine averaging of its distances to
the tangent planes of neighboring points on the surface. Besides,we model the
local geometric structure of the input point clouds by explicitly learning a
quadratic polynomial for each point. This not only facilitates upsampling the
input sparse point cloud but also naturally induces unoriented normal, which
further augments UDF estimation. Finally, to extract triangle meshes from the
predicted UDF we propose a customized edge-based marching cube module. We
conduct extensive experiments and ablation studies to demonstrate the
significant advantages of our method over state-of-the-art methods in terms of
reconstruction accuracy, efficiency, and generality. The source code is
publicly available at https://github.com/rsy6318/GeoUDF
NeuroGF: A Neural Representation for Fast Geodesic Distance and Path Queries
Geodesics are essential in many geometry processing applications. However,
traditional algorithms for computing geodesic distances and paths on 3D mesh
models are often inefficient and slow. This makes them impractical for
scenarios that require extensive querying of arbitrary point-to-point
geodesics. Although neural implicit representations have emerged as a popular
way of representing 3D shape geometries, there is still no research on
representing geodesics with deep implicit functions. To bridge this gap, this
paper presents the first attempt to represent geodesics on 3D mesh models using
neural implicit functions. Specifically, we introduce neural geodesic fields
(NeuroGFs), which are learned to represent the all-pairs geodesics of a given
mesh. By using NeuroGFs, we can efficiently and accurately answer queries of
arbitrary point-to-point geodesic distances and paths, overcoming the
limitations of traditional algorithms. Evaluations on common 3D models show
that NeuroGFs exhibit exceptional performance in solving the single-source
all-destination (SSAD) and point-to-point geodesics, and achieve high accuracy
consistently. Moreover, NeuroGFs offer the unique advantage of encoding both 3D
geometry and geodesics in a unified representation. Code is made available at
https://github.com/keeganhk/NeuroGF/tree/master
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