57 research outputs found
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Mechanics and kinetics of dynamic instability.
During dynamic instability, self-assembling microtubules (MTs) stochastically alternate between phases of growth and shrinkage. This process is driven by the presence of two distinct states of MT subunits, GTP- and GDP-bound tubulin dimers, that have different structural properties. Here, we use a combination of analysis and computer simulations to study the mechanical and kinetic regulation of dynamic instability in three-dimensional (3D) self-assembling MTs. Our model quantifies how the 3D structure and kinetics of the distinct states of tubulin dimers determine the mechanical stability of MTs. We further show that dynamic instability is influenced by the presence of quenched disorder in the state of the tubulin subunit as reflected in the fraction of non-hydrolysed tubulin. Our results connect the 3D geometry, kinetics and statistical mechanics of these tubular assemblies within a single framework, and may be applicable to other self-assembled systems where these same processes are at play
SHERF: Generalizable Human NeRF from a Single Image
Existing Human NeRF methods for reconstructing 3D humans typically rely on
multiple 2D images from multi-view cameras or monocular videos captured from
fixed camera views. However, in real-world scenarios, human images are often
captured from random camera angles, presenting challenges for high-quality 3D
human reconstruction. In this paper, we propose SHERF, the first generalizable
Human NeRF model for recovering animatable 3D humans from a single input image.
SHERF extracts and encodes 3D human representations in canonical space,
enabling rendering and animation from free views and poses. To achieve
high-fidelity novel view and pose synthesis, the encoded 3D human
representations should capture both global appearance and local fine-grained
textures. To this end, we propose a bank of 3D-aware hierarchical features,
including global, point-level, and pixel-aligned features, to facilitate
informative encoding. Global features enhance the information extracted from
the single input image and complement the information missing from the partial
2D observation. Point-level features provide strong clues of 3D human
structure, while pixel-aligned features preserve more fine-grained details. To
effectively integrate the 3D-aware hierarchical feature bank, we design a
feature fusion transformer. Extensive experiments on THuman, RenderPeople,
ZJU_MoCap, and HuMMan datasets demonstrate that SHERF achieves state-of-the-art
performance, with better generalizability for novel view and pose synthesis.Comment: Accepted by ICCV2023. Project webpage:
https://skhu101.github.io/SHERF
LLMs as Workers in Human-Computational Algorithms? Replicating Crowdsourcing Pipelines with LLMs
LLMs have shown promise in replicating human-like behavior in crowdsourcing
tasks that were previously thought to be exclusive to human abilities. However,
current efforts focus mainly on simple atomic tasks. We explore whether LLMs
can replicate more complex crowdsourcing pipelines. We find that modern LLMs
can simulate some of crowdworkers' abilities in these "human computation
algorithms," but the level of success is variable and influenced by requesters'
understanding of LLM capabilities, the specific skills required for sub-tasks,
and the optimal interaction modality for performing these sub-tasks. We reflect
on human and LLMs' different sensitivities to instructions, stress the
importance of enabling human-facing safeguards for LLMs, and discuss the
potential of training humans and LLMs with complementary skill sets. Crucially,
we show that replicating crowdsourcing pipelines offers a valuable platform to
investigate (1) the relative strengths of LLMs on different tasks (by
cross-comparing their performances on sub-tasks) and (2) LLMs' potential in
complex tasks, where they can complete part of the tasks while leaving others
to humans
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