Reference-based Super-Resolution (Ref-SR) has recently emerged as a promising paradigm to enhance a low-resolution (LR) input image or video by introducing an additional high-resolution (HR) reference image. Existing Ref-SR methods mostly rely on implicit correspondence matching to borrow HR textures from reference images to compensate for the information loss in input images. However, performing local transfer is difficult because of two gaps between input and reference images: the transformation gap (e.g., scale and rotation) and the resolution gap (e.g., HR and LR). To tackle these challenges, we propose C2-Matching in this work, which performs explicit robust matching crossing transformation and resolution. 1) To bridge the transformation gap, we propose a contrastive correspondence network, which learns transformation-robust correspondences using augmented views of the input image. 2) To address the resolution gap, we adopt teacher-student correlation distillation, which distills knowledge from the easier HR-HR matching to guide the more ambiguous LR-HR matching. 3) Finally, we design a dynamic aggregation module to address the potential misalignment issue between input images and reference images. In addition, to faithfully evaluate the performance of Reference-based Image Super-Resolution under a realistic setting, we contribute the Webly-Referenced SR (WR-SR) dataset, mimicking the practical usage scenario. We also extend C2-Matching to Reference-based Video Super-Resolution task, where an image taken in a similar scene serves as the HR reference image. Extensive experiments demonstrate that our proposed C2-Matching significantly outperforms state of the arts on the standard CUFED5 benchmark and also boosts the performance of video SR by incorporating the C2-Matching component into Video SR pipelines.

Previous studies have explored generating accurately lip-synced talking faces for arbitrary targets given audio conditions. However, most of them deform or generate the whole facial area, leading to non-realistic results. In this work, we delve into the formulation of altering only the mouth shapes of the target person. This requires masking a large percentage of the original image and seamlessly inpainting it with the aid of audio and reference frames. To this end, we propose the Audio-Visual Context-Aware Transformer (AV-CAT) framework, which produces accurate lip-sync with photo-realistic quality by predicting the masked mouth shapes. Our key insight is to exploit desired contextual information provided in audio and visual modalities thoroughly with delicately designed Transformers. Specifically, we propose a convolution-Transformer hybrid backbone and design an attention-based fusion strategy for filling the masked parts. It uniformly attends to the textural information on the unmasked regions and the reference frame. Then the semantic audio information is involved in enhancing the self-attention computation. Additionally, a refinement network with audio injection improves both image and lip-sync quality. Extensive experiments validate that our model can generate high-fidelity lip-synced results for arbitrary subjects.

Co-speech gesture is crucial for human-machine interaction and digital entertainment. While previous works mostly map speech audio to human skeletons (e.g., 2D keypoints), directly generating speakers' gestures in the image domain remains unsolved. In this work, we formally define and study this challenging problem of audio-driven co-speech gesture video generation, i.e., using a unified framework to generate speaker image sequence driven by speech audio. Our key insight is that the co-speech gestures can be decomposed into common motion patterns and subtle rhythmic dynamics. To this end, we propose a novel framework, Audio-driveN Gesture vIdeo gEneration (ANGIE), to effectively capture the reusable co-speech gesture patterns as well as fine-grained rhythmic movements. To achieve high-fidelity image sequence generation, we leverage an unsupervised motion representation instead of a structural human body prior (e.g., 2D skeletons). Specifically, 1) we propose a vector quantized motion extractor (VQ-Motion Extractor) to summarize common co-speech gesture patterns from implicit motion representation to codebooks. 2) Moreover, a co-speech gesture GPT with motion refinement (Co-Speech GPT) is devised to complement the subtle prosodic motion details. Extensive experiments demonstrate that our framework renders realistic and vivid co-speech gesture video. Demo video and more resources can be found in: https://alvinliu0.github.io/projects/ANGIE

Dialogue systems can leverage large pre-trained language models and knowledge to generate fluent and informative responses. However, these models are still prone to produce hallucinated responses not supported by the input source, which greatly hinders their application. The heterogeneity between external knowledge and dialogue context challenges representation learning and source integration, and further contributes to unfaithfulness. To handle this challenge and generate more faithful responses, this paper presents RHO ($\rho$) utilizing the representations of linked entities and relation predicates from a knowledge graph (KG). We propose (1) local knowledge grounding to combine textual embeddings with the corresponding KG embeddings; and (2) global knowledge grounding to equip RHO with multi-hop reasoning abilities via the attention mechanism. In addition, we devise a response re-ranking technique based on walks over KG sub-graphs for better conversational reasoning. Experimental results on OpenDialKG show that our approach significantly outperforms state-of-the-art methods on both automatic and human evaluation by a large margin, especially in hallucination reduction (17.54% in FeQA).

Existing correspondence datasets for two-dimensional (2D) cartoon suffer from simple frame composition and monotonic movements, making them insufficient to simulate real animations. In this work, we present a new 2D animation visual correspondence dataset, AnimeRun, by converting open source three-dimensional (3D) movies to full scenes in 2D style, including simultaneous moving background and interactions of multiple subjects. Our analyses show that the proposed dataset not only resembles real anime more in image composition, but also possesses richer and more complex motion patterns compared to existing datasets. With this dataset, we establish a comprehensive benchmark by evaluating several existing optical flow and segment matching methods, and analyze shortcomings of these methods on animation data. Data, code and other supplementary materials are available at https://lisiyao21.github.io/projects/AnimeRun.

Image super-resolution is a common task on mobile and IoT devices, where one often needs to upscale and enhance low-resolution images and video frames. While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints. In this Mobile AI challenge, we address this problem and propose the participants to design an efficient quantized image super-resolution solution that can demonstrate a real-time performance on mobile NPUs. The participants were provided with the DIV2K dataset and trained INT8 models to do a high-quality 3X image upscaling. The runtime of all models was evaluated on the Synaptics VS680 Smart Home board with a dedicated edge NPU capable of accelerating quantized neural networks. All proposed solutions are fully compatible with the above NPU, demonstrating an up to 60 FPS rate when reconstructing Full HD resolution images. A detailed description of all models developed in the challenge is provided in this paper.

鉴于其广泛的应用，已经对人面部交换的任务进行了许多尝试。尽管现有的方法主要依赖于乏味的网络和损失设计，但它们仍然在源和目标面之间的信息平衡中挣扎，并倾向于产生可见的人工制品。在这项工作中，我们引入了一个名为StylesWap的简洁有效的框架。我们的核心想法是利用基于样式的生成器来增强高保真性和稳健的面部交换，因此可以采用发电机的优势来优化身份相似性。我们仅通过最小的修改来确定，StyleGAN2体系结构可以成功地处理来自源和目标的所需信息。此外，受到TORGB层的启发，进一步设计了交换驱动的面具分支以改善信息的融合。此外，可以采用stylegan倒置的优势。特别是，提出了交换引导的ID反转策略来优化身份相似性。广泛的实验验证了我们的框架会产生高质量的面部交换结果，从而超过了最先进的方法，既有定性和定量。

生成高质量的艺术肖像视频是计算机图形和愿景中的一项重要且理想的任务。尽管已经提出了一系列成功的肖像图像图像模型模型，但这些面向图像的方法在应用于视频（例如固定框架尺寸，面部对齐的要求，缺失的非种族细节和缺失的非种族细节和缺失的要求）时，具有明显的限制。时间不一致。在这项工作中，我们通过引入一个新颖的Vtoonify框架来研究具有挑战性的可控高分辨率肖像视频风格转移。具体而言，Vtoonify利用了Stylegan的中高分辨率层，以基于编码器提取的多尺度内容功能来渲染高质量的艺术肖像，以更好地保留框架细节。由此产生的完全卷积体系结构接受可变大小的视频中的非对齐面孔作为输入，从而有助于完整的面部区域，并在输出中自然动作。我们的框架与现有的基于Stylegan的图像图像模型兼容，以将其扩展到视频化，并继承了这些模型的吸引力，以进行柔性风格控制颜色和强度。这项工作分别为基于收藏和基于示例的肖像视频风格转移而建立在Toonify和DualStylegan的基于Toonify和Dualstylegan的Vtoonify的两个实例化。广泛的实验结果证明了我们提出的VTOONIFY框架对现有方法的有效性在生成具有灵活风格控件的高质量和临时艺术肖像视频方面的有效性。

3D人类的姿势和形状估计（又称“人网恢复”）取得了实质性进展。研究人员主要关注新算法的发展，而对涉及的其他关键因素的关注较少。这可能会导致最佳基线，从而阻碍对新设计方法的公平和忠实的评估。为了解决这个问题，这项工作从算法以外的三个探索性观点中提出了首次全面的基准测试研究。 1）数据集。对31个数据集的分析揭示了数据样本的不同影响：具有关键属性的数据集（即多样化的姿势，形状，相机特征，骨干特征）更有效。高质量数据集的战略选择和组合可以显着提高模型性能。 2）骨干。从CNN到变压器的10个骨干的实验表明，从接近任务中学到的知识很容易转移到人网状恢复中。 3）培训策略。正确的增强技术和损失设计至关重要。通过上述发现，我们在具有相对简单的模型的3DPW测试集上实现了47.3 mm的PA-MPJPE。更重要的是，我们为算法的公平比较提供了强大的基准，以及将来建立有效培训配置的建议。代码库可在http://github.com/smplbody/hmr-benchmarks上获得

高质量的HDRI（高动态范围图像），通常是HDR Panoramas，是创建图形中3D场景的3D场景的最受欢迎的方法之一。考虑到捕获HDRI的困难，高度需要一种多功能和可控的生成模型，外行用户可以直观地控制生成过程。但是，现有的最新方法仍然难以合成复杂场景的高质量全景。在这项工作中，我们提出了一个零击文本驱动的框架Text2Light，以生成4K+分辨率HDRIS，而无需配对培训数据。给定一个自由形式的文本作为场景的描述，我们通过两个专用步骤合成相应的HDRI：1）在低动态范围（LDR）（LDR）和低分辨率的文本驱动全景生成，以及2）超分辨率逆音映射在分辨率和动态范围内扩大LDR Panorama。具体来说，为了获得零击文本驱动的全景生成，我们首先将双代码簿作为不同环境纹理的离散表示形式。然后，在预先训练的剪辑模型的驱动下，一个文本条件的全局采样器学会了根据输入文本从全局代码簿中采样整体语义。此外，一个结构感知的本地采样器学会了以整体语义为指导的LDR Panoramas逐个贴片。为了获得超分辨率的逆音映射，我们从LDR Panorama得出了360度成像的连续表示，作为一组固定在球体上的结构性潜在代码。这种连续表示可以使多功能模块同时提高分辨率和动态范围。广泛的实验证明了Text2light在产生高质量HDR全景方面具有卓越的能力。此外，我们还展示了我们在现实渲染和沉浸式VR中工作的可行性。