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

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