Yanze Wu

Chong Mou, Yanze Wu, Xintao Wang et al.

Interactive image restoration aims to restore images by adjusting several controlling coefficients, which determine the restoration strength. Existing methods are restricted in learning the controllable functions under the supervision of known degradation types and levels. They usually suffer from a severe performance drop when the real degradation is different from their assumptions. Such a limitation is due to the complexity of real-world degradations, which can not provide explicit supervision to the interactive modulation during training. However, how to realize the interactive modulation in real-world super-resolution has not yet been studied. In this work, we present a Metric Learning based Interactive Modulation for Real-World Super-Resolution (MM-RealSR). Specifically, we propose an unsupervised degradation estimation strategy to estimate the degradation level in real-world scenarios. Instead of using known degradation levels as explicit supervision to the interactive mechanism, we propose a metric learning strategy to map the unquantifiable degradation levels in real-world scenarios to a metric space, which is trained in an unsupervised manner. Moreover, we introduce an anchor point strategy in the metric learning process to normalize the distribution of metric space. Extensive experiments demonstrate that the proposed MM-RealSR achieves excellent modulation and restoration performance in real-world super-resolution. Codes are available at https://github.com/TencentARC/MM-RealSR.

Yanze Wu, Xintao Wang, Gen Li et al.

This paper studies the problem of real-world video super-resolution (VSR) for animation videos, and reveals three key improvements for practical animation VSR. First, recent real-world super-resolution approaches typically rely on degradation simulation using basic operators without any learning capability, such as blur, noise, and compression. In this work, we propose to learn such basic operators from real low-quality animation videos, and incorporate the learned ones into the degradation generation pipeline. Such neural-network-based basic operators could help to better capture the distribution of real degradations. Second, a large-scale high-quality animation video dataset, AVC, is built to facilitate comprehensive training and evaluations for animation VSR. Third, we further investigate an efficient multi-scale network structure. It takes advantage of the efficiency of unidirectional recurrent networks and the effectiveness of sliding-window-based methods. Thanks to the above delicate designs, our method, AnimeSR, is capable of restoring real-world low-quality animation videos effectively and efficiently, achieving superior performance to previous state-of-the-art methods. Codes and models are available at https://github.com/TencentARC/AnimeSR.

Pengze Zhang, Yanze Wu, Mengtian Li et al.

Videos convey richer information than images or text, capturing both spatial and temporal dynamics. However, most existing video customization methods rely on reference images or task-specific temporal priors, failing to fully exploit the rich spatio-temporal information inherent in videos, thereby limiting flexibility and generalization in video generation. To address these limitations, we propose OmniTransfer, a unified framework for spatio-temporal video transfer. It leverages multi-view information across frames to enhance appearance consistency and exploits temporal cues to enable fine-grained temporal control. To unify various video transfer tasks, OmniTransfer incorporates three key designs: Task-aware Positional Bias that adaptively leverages reference video information to improve temporal alignment or appearance consistency; Reference-decoupled Causal Learning separating reference and target branches to enable precise reference transfer while improving efficiency; and Task-adaptive Multimodal Alignment using multimodal semantic guidance to dynamically distinguish and tackle different tasks. Extensive experiments show that OmniTransfer outperforms existing methods in appearance (ID and style) and temporal transfer (camera movement and video effects), while matching pose-guided methods in motion transfer without using pose, establishing a new paradigm for flexible, high-fidelity video generation.

Yanze Wu, Xintao Wang, Yu Li et al.

Colorization has attracted increasing interest in recent years. Classic reference-based methods usually rely on external color images for plausible results. A large image database or online search engine is inevitably required for retrieving such exemplars. Recent deep-learning-based methods could automatically colorize images at a low cost. However, unsatisfactory artifacts and incoherent colors are always accompanied. In this work, we propose GCP-Colorization that leverages the rich and diverse color priors encapsulated in a pretrained Generative Adversarial Networks (GAN) for automatic colorization. Specifically, we first "retrieve" matched features (similar to exemplars) via a GAN encoder and then incorporate these features into the colorization process with feature modulations. Thanks to the powerful generative color prior (GCP) and delicate designs, our GCP-Colorization could produce vivid colors with a single forward pass. Moreover, it is highly convenient to obtain diverse results by modifying GAN latent codes. GCP-Colorization also inherits the merit of interpretable controls of GANs and could attain controllable and smooth transitions by walking through GAN latent space. Extensive experiments and user studies demonstrate that GCP-Colorization achieves superior performance than previous works. Codes are available at https://github.com/ToTheBeginning/GCP-Colorization.

Yanze Wu, Qiang Sun, Jianqi Ma et al.

This paper studies the task of Visual Question Answering (VQA), which is topical in Multimedia community recently. Particularly, we explore two critical research problems existed in VQA: (1) efficiently fusing the visual and textual modalities; (2) enabling the visual reasoning ability of VQA models in answering complex questions. To address these challenging problems, a novel Question Guided Modular Routing Networks (QGMRN) has been proposed in this paper. Particularly, The QGMRN is composed of visual, textual and routing network. The visual and textual network serve as the backbones for the generic feature extractors of visual and textual modalities. QGMRN can fuse the visual and textual modalities at multiple semantic levels. Typically, the visual reasoning is facilitated by the routing network in a discrete and stochastic way by using Gumbel-Softmax trick for module selection. When the input reaches a certain modular layer, routing network newly proposed in this paper, dynamically selects a portion of modules from that layer to process the input depending on the question features generated by the textual network. It can also learn to reason by routing between the generic modules without additional supervision information or expert knowledge. Benefiting from the dynamic routing mechanism, QGMRN can outperform the previous classical VQA methods by a large margin and achieve the competitive results against the state-of-the-art methods. Furthermore, attention mechanism is integrated into our QGMRN model and thus can further boost the model performance. Empirically, extensive experiments on the CLEVR and CLEVR-Humans datasets validate the effectiveness of our proposed model, and the state-of-the-art performance has been achieved.