FaceRefiner: High-Fidelity Facial Texture Refinement with Differentiable Rendering-based Style Transfer

Recent facial texture generation methods prefer to use deep networks to synthesize image content and then fill in the UV map, thus generating a compelling full texture from a single image. Nevertheless, the synthesized texture UV map usually comes from a space constructed by the training data or the 2D face generator, which limits the methods' generalization ability for in-the-wild input images. Consequently, their facial details, structures and identity may not be consistent with the input. In this paper, we address this issue by proposing a style transfer-based facial texture refinement method named FaceRefiner. FaceRefiner treats the 3D sampled texture as style and the output of a texture generation method as content. The photo-realistic style is then expected to be transferred from the style image to the content image. Different from current style transfer methods that only transfer high and middle level information to the result, our style transfer method integrates differentiable rendering to also transfer low level (or pixel level) information in the visible face regions. The main benefit of such multi-level information transfer is that, the details, structures and semantics in the input can thus be well preserved. The extensive experiments on Multi-PIE, CelebA and FFHQ datasets demonstrate that our refinement method can improve the texture quality and the face identity preserving ability, compared with state-of-the-arts.

Fast Causal Discovery by Approximate Kernel-based Generalized Score Functions with Linear Computational Complexity

Score-based causal discovery methods can effectively identify causal relationships by evaluating candidate graphs and selecting the one with the highest score. One popular class of scores is kernel-based generalized score functions, which can adapt to a wide range of scenarios and work well in practice because they circumvent assumptions about causal mechanisms and data distributions. Despite these advantages, kernel-based generalized score functions pose serious computational challenges in time and space, with a time complexity of $\mathcal{O}(n^3)$ and a memory complexity of $\mathcal{O}(n^2)$, where $n$ is the sample size. In this paper, we propose an approximate kernel-based generalized score function with $\mathcal{O}(n)$ time and space complexities by using low-rank technique and designing a set of rules to handle the complex composite matrix operations required to calculate the score, as well as developing sampling algorithms for different data types to benefit the handling of diverse data types efficiently. Our extensive causal discovery experiments on both synthetic and real-world data demonstrate that compared to the state-of-the-art method, our method can not only significantly reduce computational costs, but also achieve comparable accuracy, especially for large datasets.

DNPM: A Neural Parametric Model for the Synthesis of Facial Geometric Details

Parametric 3D models have enabled a wide variety of computer vision and graphics tasks, such as modeling human faces, bodies and hands. In 3D face modeling, 3DMM is the most widely used parametric model, but can't generate fine geometric details solely from identity and expression inputs. To tackle this limitation, we propose a neural parametric model named DNPM for the facial geometric details, which utilizes deep neural network to extract latent codes from facial displacement maps encoding details and wrinkles. Built upon DNPM, a novel 3DMM named Detailed3DMM is proposed, which augments traditional 3DMMs by including the synthesis of facial details only from the identity and expression inputs. Moreover, we show that DNPM and Detailed3DMM can facilitate two downstream applications: speech-driven detailed 3D facial animation and 3D face reconstruction from a degraded image. Extensive experiments have shown the usefulness of DNPM and Detailed3DMM, and the progressiveness of two proposed applications.

Joint Sensing and Communication Optimization in Target-Mounted STARS-Assisted Vehicular Networks: A MADRL Approach

The utilization of integrated sensing and communication (ISAC) technology has the potential to enhance the communication performance of road side units (RSUs) through the active sensing of target vehicles. Furthermore, installing a simultaneous transmitting and reflecting surface (STARS) on the target vehicle can provide an extra boost to the reflection of the echo signal, thereby improving the communication quality for in-vehicle users. However, the design of this target-mounted STARS system exhibits significant challenges, such as limited information sharing and distributed STARS control. In this paper, we propose an end-to-end multi-agent deep reinforcement learning (MADRL) framework to tackle the challenges of joint sensing and communication optimization in the considered target-mounted STARS assisted vehicle networks. By deploying agents on both RSU and vehicle, the MADRL framework enables RSU and vehicle to perform beam prediction and STARS pre-configuration using their respective local information. To ensure efficient and stable learning for continuous decision-making, we employ the multi-agent soft actor critic (MASAC) algorithm and the multi-agent proximal policy optimization (MAPPO) algorithm on the proposed MADRL framework. Extensive experimental results confirm the effectiveness of our proposed MADRL framework in improving both sensing and communication performance through the utilization of target-mounted STARS. Finally, we conduct a comparative analysis and comparison of the two proposed algorithms under various environmental conditions.