170 research outputs found
Variational Autoencoders for Deforming 3D Mesh Models
3D geometric contents are becoming increasingly popular. In this paper, we
study the problem of analyzing deforming 3D meshes using deep neural networks.
Deforming 3D meshes are flexible to represent 3D animation sequences as well as
collections of objects of the same category, allowing diverse shapes with
large-scale non-linear deformations. We propose a novel framework which we call
mesh variational autoencoders (mesh VAE), to explore the probabilistic latent
space of 3D surfaces. The framework is easy to train, and requires very few
training examples. We also propose an extended model which allows flexibly
adjusting the significance of different latent variables by altering the prior
distribution. Extensive experiments demonstrate that our general framework is
able to learn a reasonable representation for a collection of deformable
shapes, and produce competitive results for a variety of applications,
including shape generation, shape interpolation, shape space embedding and
shape exploration, outperforming state-of-the-art methods.Comment: CVPR 201
Reactive DC Magnetron Sputtering-Induced the Formation of Amorphous CuN Films Embedded Nanocrystalline WC Phase
A novel amorphous CuN/nanocrystal WC (nc-WC/a-CuN) film synthesized by reactive dc magnetron sputtering is reported in this paper. The nc-WC/a-CuN42 at.% film which is composed of many WC dendrite crystals of 5~10 nm with (001) orientation embedded in amorphous CuN possesses ~55 GPa hardness. The high-temperature wear analysis shows that this novel film possesses the comparable excellent friction performance with DLC film which is attributed to self-lubricant function of a-CuN; simultaneously the film was still maintaining the higher hardness at elevated temperature
Mesh-based Autoencoders for Localized Deformation Component Analysis
Spatially localized deformation components are very useful for shape analysis
and synthesis in 3D geometry processing. Several methods have recently been
developed, with an aim to extract intuitive and interpretable deformation
components. However, these techniques suffer from fundamental limitations
especially for meshes with noise or large-scale deformations, and may not
always be able to identify important deformation components. In this paper we
propose a novel mesh-based autoencoder architecture that is able to cope with
meshes with irregular topology. We introduce sparse regularization in this
framework, which along with convolutional operations, helps localize
deformations. Our framework is capable of extracting localized deformation
components from mesh data sets with large-scale deformations and is robust to
noise. It also provides a nonlinear approach to reconstruction of meshes using
the extracted basis, which is more effective than the current linear
combination approach. Extensive experiments show that our method outperforms
state-of-the-art methods in both qualitative and quantitative evaluations
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Direct contact membrane distillation with heat recovery: Thermodynamic insights from module scale modeling
Direct contact membrane distillation (DCMD) can desalinate saline waters using low-grade heat and is thus economically attractive when low-temperature thermal energy is readily available. Coupling DCMD with a heat exchanger (HX) can significantly enhance the energy efficiency of the process by recovering the latent heat accumulated in the permeate (distillate) stream. This study evaluates the mass recovery rate (i.e., fraction of feed water recovered), γ, and the specific heat duty (i.e., energy input per unit mass of product water), β, of DCMD desalination using low-grade heat coupled with HX. Mass and heat transfer in DCMD and HX were modeled at the module scale and thermodynamic analysis of the system was performed. The relative flow rate (between the permeate and feed streams), α, was found to be a critical operation parameter to optimize process performance, regardless of the mass and heat transfer kinetics. Both numerical evaluation and analytical analysis reveal a critical relative flow rate, α⁎, that demarcates DCMD operation between a permeate limiting regime (when αα⁎), when mass transfer kinetics are not limiting. Similarly, we identified mass-limited and temperature-limited heat recovery regimes in the HX that are dependent on α. Our analysis shows that the highest γ and lowest β achievable are solely determined by the thermodynamic properties of the system and always occur at the critical relative flow rate, α⁎. For example, the thermodynamic limits for γ and β are 6.4% and 27.6 kJ kg−1, respectively, for seawater desalination by single-pass DCMD at 60 °C with HX. However, in practical operation, as the DCMD membrane area and permeability cannot be infinitely large, the process is in a mass-transfer-limiting-regime and performance departs from the thermodynamic limits. Lastly, we demonstrate that heat transfer across a thermally-conductive DCMD membrane further reduces the recovery rate and energy efficiency of the process. The findings from this study have important implications for optimization of the DCMD process and for serving as criteria to assess process performance
Rigidity controllable as-rigid-as-possible shape deformations
Shape deformation is one of the fundamental techniques in geometric processing. One principle of deformation is to preserve the geometric details while distributing the necessary distortions uniformly. To achieve this, state-of-the-art techniques deform shapes in a locally as-rigid-as-possible (ARAP) manner. Existing ARAP deformation methods optimize rigid transformations in the 1-ring neighborhoods and maintain the consistency between adjacent pairs of rigid transformations by single overlapping edges. In this paper, we make one step further and propose to use larger local neighborhoods to enhance the consistency of adjacent rigid transformations. This is helpful to keep the geometric details better and distribute the distortions more uniformly. Moreover, the size of the expanded local neighborhoods provides an intuitive parameter to adjust physical stiffness. The larger the neighborhood is, the more rigid the material is. Based on these, we propose a novel rigidity controllable mesh deformation method where shape rigidity can be flexibly adjusted. The size of the local neighborhoods can be learned from datasets of deforming objects automatically or specified by the user, and may vary over the surface to simulate shapes composed of mixed materials. Various examples are provided to demonstrate the effectiveness of our method
Language Models as Black-Box Optimizers for Vision-Language Models
Vision-language models (VLMs) pre-trained on web-scale datasets have
demonstrated remarkable capabilities across a variety of vision and multimodal
tasks. Currently, fine-tuning methods for VLMs mainly operate in a white-box
setting, requiring access to model parameters for backpropagation. However,
many VLMs rely on proprietary data and are not open-source, which restricts the
use of white-box approaches for fine-tuning. Given that popular private large
language models (LLMs) like ChatGPT still offer a language-based user
interface, we aim to develop a novel fine-tuning approach for VLMs through
natural language prompts, thereby avoiding the need to access model parameters,
feature embeddings, or output logits. In this setup, we propose employing
chat-based LLMs as black-box optimizers to search for the best text prompt on
the illustrative task of few-shot image classification using CLIP.
Specifically, we adopt an automatic "hill-climbing" procedure that converges on
an effective prompt by evaluating the accuracy of current prompts and asking
LLMs to refine them based on textual feedback, all within a conversational
process without human-in-the-loop. In a challenging 1-shot learning setup, our
simple approach surpasses the white-box continuous prompting method (CoOp) by
an average of 1.5% across 11 datasets including ImageNet. Our approach also
outperforms OpenAI's manually crafted prompts. Additionally, we highlight the
advantage of conversational feedback that incorporates both positive and
negative prompts, suggesting that LLMs can utilize the implicit "gradient"
direction in textual feedback for a more efficient search. Lastly, we find that
the text prompts generated through our strategy are not only more interpretable
but also transfer well across different CLIP architectures in a black-box
manner
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