74 research outputs found
MeMaHand: Exploiting Mesh-Mano Interaction for Single Image Two-Hand Reconstruction
Existing methods proposed for hand reconstruction tasks usually parameterize
a generic 3D hand model or predict hand mesh positions directly. The parametric
representations consisting of hand shapes and rotational poses are more stable,
while the non-parametric methods can predict more accurate mesh positions. In
this paper, we propose to reconstruct meshes and estimate MANO parameters of
two hands from a single RGB image simultaneously to utilize the merits of two
kinds of hand representations. To fulfill this target, we propose novel
Mesh-Mano interaction blocks (MMIBs), which take mesh vertices positions and
MANO parameters as two kinds of query tokens. MMIB consists of one graph
residual block to aggregate local information and two transformer encoders to
model long-range dependencies. The transformer encoders are equipped with
different asymmetric attention masks to model the intra-hand and inter-hand
attention, respectively. Moreover, we introduce the mesh alignment refinement
module to further enhance the mesh-image alignment. Extensive experiments on
the InterHand2.6M benchmark demonstrate promising results over the
state-of-the-art hand reconstruction methods
Dopamine Surface Modification of Trititanate Nanotubes: Proposed InâSitu Structure Models
Two models for selfâassembled dopamine on the surface of trititanate nanotubes are proposed: individual monomer units linked by ĎâĎ stacking of the aromatic regions and monoâattached units interacting through hydrogen bonds. This was investigated with solid state NMR spectroscopy studies and powder Xâray diffraction.Double bind: Two models for selfâassembled dopamine on the surface of trititanate nanotubes are proposed: individual trimer units linked by ĎâĎ stacking of the aromatic regions and monoâattached units interacting through hydrogen bonds. This was investigated by solid state NMR spectroscopy studies and powder Xâray diffraction.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137420/1/chem201600075.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137420/2/chem201600075_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137420/3/chem201600075-sup-0001-misc_information.pd
Batch-based Model Registration for Fast 3D Sherd Reconstruction
3D reconstruction techniques have widely been used for digital documentation
of archaeological fragments. However, efficient digital capture of fragments
remains as a challenge. In this work, we aim to develop a portable,
high-throughput, and accurate reconstruction system for efficient digitization
of fragments excavated in archaeological sites. To realize high-throughput
digitization of large numbers of objects, an effective strategy is to perform
scanning and reconstruction in batches. However, effective batch-based scanning
and reconstruction face two key challenges: 1) how to correlate partial scans
of the same object from multiple batch scans, and 2) how to register and
reconstruct complete models from partial scans that exhibit only small
overlaps. To tackle these two challenges, we develop a new batch-based matching
algorithm that pairs the front and back sides of the fragments, and a new
Bilateral Boundary ICP algorithm that can register partial scans sharing very
narrow overlapping regions. Extensive validation in labs and testing in
excavation sites demonstrate that these designs enable efficient batch-based
scanning for fragments. We show that such a batch-based scanning and
reconstruction pipeline can have immediate applications on digitizing sherds in
archaeological excavations. Our project page:
https://jiepengwang.github.io/FIRES/.Comment: Project page: https://jiepengwang.github.io/FIRES
An Implicit Parametric Morphable Dental Model
3D Morphable models of the human body capture variations among subjects and
are useful in reconstruction and editing applications. Current dental models
use an explicit mesh scene representation and model only the teeth, ignoring
the gum. In this work, we present the first parametric 3D morphable dental
model for both teeth and gum. Our model uses an implicit scene representation
and is learned from rigidly aligned scans. It is based on a component-wise
representation for each tooth and the gum, together with a learnable latent
code for each of such components. It also learns a template shape thus enabling
several applications such as segmentation, interpolation, and tooth
replacement. Our reconstruction quality is on par with the most advanced global
implicit representations while enabling novel applications. Project page:
https://vcai.mpi-inf.mpg.de/projects/DMM
Low-Power Redundant-Transition-Free TSPC Dual-Edge-Triggering Flip-Flop Using Single-Transistor-Clocked Buffer
In the modern graphics processing unit (GPU)/artificial intelligence (AI) era, flip-flop (FF) has become one of the most power-hungry blocks in processors. To address this issue, a novel single-phase-clock dual-edge-triggering (DET) FF using a single-transistor-clocked (STC) buffer (STCB) is proposed. The STCB uses a single-clocked transistor in the data sampling path, which completely removes clock redundant transitions (RTs) and internal RTs that exist in other DET designs. Verified by post-layout simulations in 22 nm fully depleted silicon on insulator (FD-SOI) CMOS, when operating at 10% switching activity, the proposed STC-DET outperforms prior state-of-the-art low-power DET in power consumption by 14% and 9.5%, at 0.4 and 0.8 V, respectively. It also achieves the lowest power-delay-product (PDP) among the DETs
Giant Enhancement of Magnonic Frequency Combs by Exceptional Points
With their incomparable time-frequency accuracy, frequency combs have
significantly advanced precision spectroscopy, ultra-sensitive detection, and
atomic clocks. Traditional methods to create photonic, phononic, and magnonic
frequency combs hinge on material nonlinearities which are often weak,
necessitating high power densities to surpass their initiation thresholds,
which subsequently limits their applications. Here, we introduce a novel
nonlinear process to efficiently generate magnonic frequency combs (MFCs) by
exploiting exceptional points (EPs) in a coupled system comprising a
pump-induced magnon mode and a Kittel mode. Even without any cavity, our method
greatly improves the efficiency of nonlinear frequency conversion and achieves
optimal MFCs at low pump power. Additionally, our novel nonlinear process
enables excellent tunability of EPs using the polarization and power of the
pump, simplifying MFC generation and manipulation. Our work establishes a
synergistic relationship between non-Hermitian physics and MFCs, which is
advantages for coherent/quantum information processing and ultra-sensitive
detection.Comment: 7 pages, 4 figure
HandPainter â 3D sketching in VR with hand-based physical proxy
3D sketching in virtual reality (VR) enables users to create 3D virtual objects intuitively and immersively. However, previous studies showed that mid-air drawing may lead to inaccurate sketches. To address this issue, we propose to use one hand as a canvas proxy and the index finger of the other hand as a 3D pen. To this end, we first perform a formative study to compare two-handed interaction with tablet-pen interaction for VR sketching. Based on the findings of this study, we design HandPainter, a VR sketching system which focuses on the direct use of two hands for 3D sketching without requesting any tablet, pen, or VR controller. Our implementation is based on a pair of VR gloves, which provide hand tracking and gesture capture. We devise a set of intuitive gestures to control various functionalities required during 3D sketching, such as canvas panning and drawing positioning. We show the effectiveness of HandPainter by presenting a number of sketching results and discussing the outcomes of a user study-based comparison with mid-air drawing and tablet-based sketching tools
Surface Extraction from Neural Unsigned Distance Fields
We propose a method, named DualMesh-UDF, to extract a surface from unsigned
distance functions (UDFs), encoded by neural networks, or neural UDFs. Neural
UDFs are becoming increasingly popular for surface representation because of
their versatility in presenting surfaces with arbitrary topologies, as opposed
to the signed distance function that is limited to representing a closed
surface. However, the applications of neural UDFs are hindered by the notorious
difficulty in extracting the target surfaces they represent. Recent methods for
surface extraction from a neural UDF suffer from significant geometric errors
or topological artifacts due to two main difficulties: (1) A UDF does not
exhibit sign changes; and (2) A neural UDF typically has substantial
approximation errors. DualMesh-UDF addresses these two difficulties.
Specifically, given a neural UDF encoding a target surface to be
recovered, we first estimate the tangent planes of at a set of sample
points close to . Next, we organize these sample points into local
clusters, and for each local cluster, solve a linear least squares problem to
determine a final surface point. These surface points are then connected to
create the output mesh surface, which approximates the target surface. The
robust estimation of the tangent planes of the target surface and the
subsequent minimization problem constitute our core strategy, which contributes
to the favorable performance of DualMesh-UDF over other competing methods. To
efficiently implement this strategy, we employ an adaptive Octree. Within this
framework, we estimate the location of a surface point in each of the octree
cells identified as containing part of the target surface. Extensive
experiments show that our method outperforms existing methods in terms of
surface reconstruction quality while maintaining comparable computational
efficiency.Comment: ICCV 202
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