High-resolution images come with rich color information and texture details. Due to the rapid upgrading of display devices and rendering technologies, high-resolution real-time rendering faces the computational overhead challenge. To address this, the current mainstream solution is to render at a lower resolution and then upsample to the target resolution by supersampling techniques. However, while many prior supersampling approaches have attempted to exploit rich rendered data such as color, depth, motion vectors at low resolution, there is little discussion on how to harness high-frequency information that is readily available in the high-resolution (HR) G-buffers of modern renders. In this article, we seek to investigate how to fully leverage information from HR G-buffers to maximize the visual quality of supersampling results. We propose a neural network for real-time supersampling of rendered content, which is based on several core designs, including gated G-buffers encoder, G-buffers attended encoder and reflection-aware loss. These designs are especially made for the sake of effectively using HR G-buffers, enabling faithful recovery of a variety of high-frequency scene details from low-resolution, highly aliased inputs. Furthermore, a simple occlusion-aware blender is proposed to efficiently rectify dis-occluded features in the warped previous frame, allowing us to better exploit history information to improve temporal stability. The experiments show that our method, equipped with strong ability to harness HR G-buffer information, significantly improves the visual fidelity of high-resolution reconstructions upon previous state-of-the-art methods, even for challenging $4 \times 4$4×4 upsampling, while still being compute-efficient.

Kinship verification aims to infer whether there is a kin relation between different individuals from facial images . However, popular kinship datasets are often small and suffer from data imbalance. Most existing methods build complex networks to extract features but ignore some implicit information, like family information. They use balanced datasets with fixed negative samples for training, which overlooks valuable information from multiple negative samples, leading to poor performance and robustness. To address these issues, we propose a novel end-to-end framework for kinship verification called Online Re-weighting Relation Network (OR 2 Net) based on an online re-weighting strategy of meta-learning and relation network. Our novel relation network aims to extract fine-grained features and reduce differences between generations by using multi-scale features and mining family information from kinship datasets. Additionally, we design a lightweight meta re-weighting network that uses a small, clean meta-set to guide the adaptive weighing of training examples. This is done using one-step stochastic gradient descent (SGD) based on an online re-weighting strategy from meta-learning. This helps find effective hard negative samples and reduces the imbalance problem. Extensive experiments on three public kinship verification datasets show that our proposed method is more effective compared to state-of-the-art methods. The code is publicly available on https://github.com/XinZhao-dlnu/OR2N .

Abstract In this paper, we propose a few-shot method for pose transfer of anime characters—given a source image of an anime character and a target pose, we transfer the pose of the target to the source character. Despite recent advances in pose transfer on real people images, these methods typically require large numbers of training images of different person under different poses to achieve reasonable results. However, anime character images are expensive to obtain they are created with a lot of artistic authoring. To address this, we propose a meta-learning framework for few-shot pose transfer, which can well generalize to an unseen character given just a few examples of the character. Further, we propose fusion residual blocks to align the features of the source and target so that the appearance of the source character can be well transferred to the target pose. Experiments show that our method outperforms leading pose transfer methods, especially when the source characters are not in the training set.