GenAI4ELab/papercli-papers huggingface dataset:
ColorFormer: Image Colorization via Color Memory Assisted Hybrid-Attention Transformer
Authors: Xiaozhong Ji; Boyuan Jiang; Donghao Luo; Guangpin Tao; Wenqing Chu; Zhifeng Xie; Chengjie Wang; Ying Tai
Venue: ECCV 2022 (main)

Abstract: "Automatic image colorization is a challenging task that attracts a lot of research interest. Previous methods employing deep neural networks have produced impressive results. However, these colorization images are still unsatisfactory and far from practical applications. The reason is that semantic consistency and color richness are two key elements ignored by existing methods. In this work, we propose an automatic image colorization method via color memory assisted hybrid-attention transformer, namely ColorFormer. Our network consists of a transformer-based encoder and a color memory decoder. The core module of the encoder is our proposed global-local hybrid attention operation, which improves the ability to capture global receptive field dependencies. With the strong power to model contextual semantic information of grayscale image in different scenes, our network can produce semantic-consistent colorization results. In decoder part, we design a color memory module which stores various semantic-color mapping for image-adaptive queries. The queried color priors are used as reference to help the decoder produce more vivid and diverse results. Experimental results show that our method can generate more realistic and semantically matched color images compared with state-of-the-art methods. Moreover, owing to the proposed end-to-end architecture, the inference speed reaches 40FPS on a V100 GPU, which meets the real-time requirement."
EAGAN: Efficient Two-Stage Evolutionary Architecture Search for GANs
Authors: Guohao Ying; Xin He; Bin Gao; Bo Han; Xiaowen Chu
Venue: ECCV 2022 (main)

Abstract: "Generative adversarial networks (GANs) have proven successful in image generation tasks. However, GAN training is inherently unstable. Although many works try to stabilize it by manually modifying GAN architecture, it requires much expertise. Neural architecture search (NAS) has become an attractive solution to search GANs automatically. The early NAS-GANs search only generators to reduce search complexity but lead to a sub-optimal GAN. Some recent works try to search both generator (G) and discriminator (D), but they suffer from the instability of GAN training. To alleviate the instability, we propose an efficient two-stage evolutionary algorithm-based NAS framework to search GANs, namely EAGAN. We decouple the search of G and D into two stages, where stage-1 searches G with a fixed D and adopts the many-to-one training strategy, and stage-2 searches D with the optimal G found in stage-1 and adopts the one-to-one training and weight-resetting strategies to enhance the stability of GAN training. Both stages use the non-dominated sorting method to produce Pareto-front architectures under multiple objectives (e.g., model size, Inception Score (IS), and Fréchet Inception Distance (FID)). EAGAN is applied to the unconditional image generation task and can efficiently finish the search on the CIFAR-10 dataset in 1.2 GPU days. Our searched GANs achieve competitive results (IS=8.81Â±0.10, FID=9.91) on the CIFAR-10 dataset and surpass prior NAS-GANs on the STL-10 dataset (IS=10.44Â±0.087, FID=22.18). Source code: https://github.com/marsggbo/EAGAN."
Weakly-Supervised Stitching Network for Real-World Panoramic Image Generation
Authors: Dae-Young Song; Geonsoo Lee; HeeKyung Lee; Gi-Mun Um; Donghyeon Cho
Venue: ECCV 2022 (main)

Abstract: "Recently, there has been growing attention on an end-to-end deep learning-based stitching model. However, the most challenging point in deep learning-based stitching is to obtain pairs of input images with a narrow field of view and ground truth images with a wide field of view captured from real-world scenes. To overcome this difficulty, we develop a weakly-supervised learning mechanism to train the stitching model without requiring genuine ground truth images. In addition, we propose a stitching model that takes multiple real-world fisheye images as inputs and creates a 360$^{\circ}$ output image in an equirectangular projection format. In particular, our model consists of color consistency corrections, warping, and blending, and is trained by perceptual and SSIM losses. The effectiveness of the proposed algorithm is verified on two real-world stitching datasets."
DynaST: Dynamic Sparse Transformer for Exemplar-Guided Image Generation
Authors: Songhua Liu; Jingwen Ye; Sucheng Ren; Xinchao Wang
Venue: ECCV 2022 (main)

Abstract: "One key challenge of exemplar-guided image generation lies in establishing fine-grained correspondences between input and guided images. Prior approaches, despite the promising results, have relied on either estimating dense attention to compute per-point matching, which is limited to only coarse scales due to the quadratic memory cost, or fixing the number of correspondences to achieve linear complexity, which lacks flexibility. In this paper, we propose a dynamic sparse attention based Transformer model, termed Dynamic Sparse Transformer (DynaST), to achieve fine-level matching with favorable efficiency. The heart of our approach is a novel dynamic-attention unit, dedicated to covering the variation on the optimal number of tokens one position should focus on. Specifically, DynaST leverages the multi-layer nature of Transformer structure, and performs the dynamic attention scheme in a cascaded manner to refine matching results and synthesize visually-pleasing outputs. In addition, we introduce a unified training objective for DynaST, making it a versatile reference-based image translation framework for both supervised and unsupervised scenarios. Extensive experiments on three applications, pose-guided person image generation, edge-based face synthesis, and undistorted image style transfer, demonstrate that DynaST achieves superior performance in local details, outperforming the state of the art while reducing the computational cost significantly. Our code is available \href{https://github.com/Huage001/DynaST}{here}."
Multimodal Conditional Image Synthesis with Product-of-Experts GANs
Authors: Xun Huang; Arun Mallya; Ting-Chun Wang; Ming-Yu Liu
Venue: ECCV 2022 (main)

Abstract: "Existing conditional image synthesis frameworks generate images based on user inputs in a single modality, such as text, segmentation, or sketch. They do not allow users to simultaneously use inputs in multiple modalities to control the image synthesis output. This reduces their practicality as multimodal inputs are more expressive and complement each other. To address this limitation, we propose the Product-of-Experts Generative Adversarial Networks (PoE-GAN) framework, which can synthesize images conditioned on multiple input modalities or any subset of them, even the empty set. We achieve this capability with a single trained model. PoE-GAN consists of a product-of-experts generator and a multimodal multiscale projection discriminator. Through our carefully designed training scheme, PoE-GAN learns to synthesize images with high quality and diversity. Besides advancing the state of the art in multimodal conditional image synthesis, PoE-GAN also outperforms the best existing unimodal conditional image synthesis approaches when tested in the unimodal setting. The project website is available at https://deepimagination.github.io/PoE-GAN/."
Auto-Regressive Image Synthesis with Integrated Quantization
Authors: Fangneng Zhan; Yingchen Yu; Rongliang Wu; Jiahui Zhang; Kaiwen Cui; Changgong Zhang; Shijian Lu
Venue: ECCV 2022 (main)

Abstract: "Deep generative models have achieved conspicuous progress in realistic image synthesis with multifarious conditional inputs, while generating diverse yet high-fidelity images remains a grand challenge in conditional image generation. This paper presents a versatile framework for conditional image generation which incorporates the inductive bias of CNNs and powerful sequence modeling of auto-regression that naturally leads to diverse image generation. Instead of independently quantizing the features of multiple domains as in prior research, we design an integrated quantization scheme with a variational regularizer that mingles the feature discretization in multiple domains, and markedly boosts the auto-regressive modeling performance. Notably, the variational regularizer enables to regularize feature distributions in incomparable latent spaces by penalizing the intra-domain variations of distributions. In addition, we design a reliable Gumbel sampling strategy that allows to incorporate distribution uncertainty into the auto-regressive training procedure. The reliable Gumbel sampling substantially mitigates the exposure bias that often incurs misalignment between the training and inference stages and severely impairs the inference performance. Extensive experiments over multiple conditional image generation tasks show that our method achieves superior diverse image generation performance qualitatively and quantitatively as compared with the state-of-the-art."
JoJoGAN: One Shot Face Stylization
Authors: Min Jin Chong; David Forsyth
Venue: ECCV 2022 (main)

Abstract: "A style mapper applies some fixed style to its input images (so, for example, taking faces to cartoons). This paper describes a simple procedure -- JoJoGAN -- to learn a style mapper from a single example of the style. JoJoGAN uses a GAN inversion procedure and StyleGAN’s style-mixing property to produce a substantial paired dataset from a single example style. The paired dataset is then used to fine-tune a StyleGAN. An image can then be style mapped by GAN-inversion followed by the fine-tuned StyleGAN. JoJoGAN needs just one reference and as little as 30 seconds of training time. JoJoGAN can use extreme style references (say, animal faces) successfully. Furthermore, one can control what aspects of the style are used and how much of the style is applied. Qualitative and quantitative evaluation show that JoJoGAN produces high quality high resolution images that vastly outperform the current state-of-the-art."
VecGAN: Image-to-Image Translation with Interpretable Latent Directions
Authors: Yusuf Dalva; Said Fahri Altındiş; Aysegul Dundar
Venue: ECCV 2022 (main)

Abstract: "We propose VecGAN, an image-to-image translation framework for facial attribute editing with interpretable latent directions. Facial attribute editing task faces the challenges of precise attribute editing with controllable strength and preservation of the other attributes of an image. For this goal, we design the attribute editing by latent space factorization and for each attribute, we learn a linear direction that is orthogonal to the others. The other component is the controllable strength of the change, a scalar value. In our framework, this scalar can be either sampled or encoded from a reference image by projection. Our work is inspired by the latent space factorization works of fixed pretrained GANs. However, while those models cannot be trained end-to-end and struggle to edit encoded images precisely, VecGAN is end-to-end trained for image translation task and successful at editing an attribute while preserving the others. Our extensive experiments show that VecGAN achieves significant improvements over state-of-the-arts for both local and global edits."
Any-Resolution Training for High-Resolution Image Synthesis
Authors: Lucy Chai; Michaël Gharbi; Eli Shechtman; Phillip Isola; Richard Zhang
Venue: ECCV 2022 (main)

Abstract: "Generative models operate at fixed resolution, even though natural images come in a variety of sizes. As high-resolution details are downsampled away and low-resolution images are discarded altogether, precious supervision is lost. We argue that every pixel matters and create datasets with variable-size images, collected at their native resolutions. To take advantage of varied-size data, we introduce continuous-scale training, a process that samples patches at random scales to train a new generator with variable output resolutions. First, conditioning the generator on a target scale allows us to generate higher resolution images than previously possible, without adding layers to the model. Second, by conditioning on continuous coordinates, we can sample patches that still obey a consistent global layout, which also allows for scalable training at higher resolutions. Controlled FFHQ experiments show that our method can take advantage of multi-resolution training data better than discrete multi-scale approaches, achieving better FID scores and cleaner high-frequency details. We also train on other natural image domains including churches, mountains, and birds, and demonstrate arbitrary scale synthesis with both coherent global layouts and realistic local details, going beyond 2K resolution in our experiments. Our project page is available at: https://chail.github.io/anyres-gan/."
CCPL: Contrastive Coherence Preserving Loss for Versatile Style Transfer
Authors: Zijie Wu; Zhen Zhu; Junping Du; Xiang Bai
Venue: ECCV 2022 (main)

Abstract: "In this paper, we aim to devise a universally versatile style transfer method capable of performing artistic, photo-realistic, and video style transfer jointly, without seeing videos during training. Previous single-frame methods assume a strong constraint on the whole image to maintain temporal consistency, which could be violated in many cases. Instead, we make a mild and reasonable assumption that global inconsistency is dominated by local inconsistencies and devise a generic Contrastive Coherence Preserving Loss (CCPL) applied to local patches. CCPL can preserve the coherence of the content source during style transfer without degrading stylization. Moreover, it owns a neighbor-regulating mechanism, resulting in a vast reduction of local distortions and considerable visual quality improvement. Aside from its superior performance on versatile style transfer, it can be easily extended to other tasks, such as image-to-image translation. Besides, to better fuse content and style features, we propose Simple Covariance Transformation (SCT) to effectively align second-order statistics of the content feature with the style feature. Experiments demonstrate the effectiveness of the resulting model for versatile style transfer, when armed with CCPL."
CANF-VC: Conditional Augmented Normalizing Flows for Video Compression
Authors: Yung-Han Ho; Chih-Peng Chang; Peng-Yu Chen; Alessandro Gnutti; Wen-Hsiao Peng
Venue: ECCV 2022 (main)

Abstract: "This paper presents an end-to-end learning-based video compression system, termed CANF-VC, based on conditional augmented normalizing flows (CANF). Most learned video compression systems adopt the same hybrid-based coding architecture as the traditional codecs. Recent research on conditional coding has shown the sub-optimality of the hybrid-based coding and opens up opportunities for deep generative models to take a key role in creating new coding frameworks. CANF-VC represents a new attempt that leverages the conditional ANF to learn a video generative model for conditional inter-frame coding. We choose ANF because it is a special type of generative model, which includes variational autoencoder as a special case and is able to achieve better expressiveness. CANF-VC also extends the idea of conditional coding to motion coding, forming a purely conditional coding framework. Extensive experimental results on commonly used datasets confirm the superiority of CANF-VC to the state-of-the-art methods. The source code of CANF-VC is available at https://github.com/NYCU-MAPL/CANF-VC."
Bi-Level Feature Alignment for Versatile Image Translation and Manipulation
Authors: Fangneng Zhan; Yingchen Yu; Rongliang Wu; Jiahui Zhang; Kaiwen Cui; Aoran Xiao; Shijian Lu; Chunyan Miao
Venue: ECCV 2022 (main)

Abstract: "Generative adversarial networks (GANs) have achieved great success in image translation and manipulation. However, high-fidelity image generation with faithful style control remains a grand challenge in computer vision. This paper presents a versatile image translation and manipulation framework that achieves accurate semantic and style guidance in image generation by explicitly building a correspondence. To handle the quadratic complexity incurred by building the dense correspondences, we introduce a bi-level feature alignment strategy that adopts a top-$k$ operation to rank block-wise features followed by dense attention between block features which reduces memory cost substantially. As the top-$k$ operation involves index swapping which precludes the gradient propagation, we approximate the non-differentiable top-$k$ operation with a regularized earth mover’s problem so that its gradient can be effectively back-propagated. In addition, we design a novel semantic position encoding mechanism that builds up coordinate for each individual semantic region to preserve texture structures while building correspondences. Further, we design a novel confidence feature injection module which mitigates mismatch problem by fusing features adaptively according to the reliability of built correspondences. Extensive experiments show that our method achieves superior performance qualitatively and quantitatively as compared with the state-of-the-art."
High-Fidelity Image Inpainting with GAN Inversion
Authors: Yongsheng Yu; Libo Zhang; Heng Fan; Tiejian Luo
Venue: ECCV 2022 (main)

Abstract: "Image inpainting seeks a semantically consistent way to recover the corrupted image in the light of its unmasked content. Previous approaches usually reuse the well-trained GAN as effective prior to generate realistic patches for missing holes with GAN inversion. Nevertheless, the ignorance of hard constraint in these algorithms may yield the gap between GAN inversion and image inpainting. Addressing this problem, in this paper we devise a novel GAN inversion model for image inpainting, dubbed {\it InvertFill}, mainly consisting of an encoder with a pre-modulation module and a GAN generator with F&W+ latent space. Within the encoder, the pre-modulation network leverages multi-scale structures to encode more discriminative semantic into style vectors. In order to bridge the gap between GAN inversion and image inpainting, F&W+ latent space is proposed to eliminate glaring color discrepancy and semantic inconsistency. To reconstruct faithful and photorealistic images, a simple yet effective Soft-update Mean Latent module is designed to capture more diverse in-domain patterns that synthesize high-fidelity textures for large corruptions. Comprehensive experiments on four challenging dataset, including Places2, CelebA-HQ, MetFaces, and Scenery, demonstrate that our InvertFill outperforms the advanced approaches qualitatively and quantitatively and supports the completion of out-of-domain images well. All codes, models and results will be made available upon the acceptance."
DeltaGAN: Towards Diverse Few-Shot Image Generation with Sample-Specific Delta
Authors: Yan Hong; Li Niu; Jianfu Zhang; Liqing Zhang
Venue: ECCV 2022 (main)

Abstract: "Learning to generate new images for a novel category based on only a few images, named as few-shot image generation, has attracted increasing research interest. Several state-of-the-art works have yielded impressive results, but the diversity is still limited. In this work, we propose a novel Delta Generative Adversarial Network (DeltaGAN), which consists of a reconstruction subnetwork and a generation subnetwork. The reconstruction subnetwork captures intra-category transformation, i.e., delta, between same-category pairs. The generation subnetwork generates sample-specific “delta” for an input image, which is combined with this input image to generate a new image within the same category. Besides, an adversarial delta matching loss is designed to link the above two subnetworks together. Extensive experiments on six benchmark datasets demonstrate the effectiveness of our proposed method."
Image Inpainting with Cascaded Modulation GAN and Object-Aware Training
Authors: Haitian Zheng; Zhe Lin; Jingwan Lu; Scott Cohen; Eli Shechtman; Connelly Barnes; Jianming Zhang; Ning Xu; Sohrab Amirghodsi; Jiebo Luo
Venue: ECCV 2022 (main)

Abstract: "Recent image inpainting methods have made great progress but often struggle to generate plausible image structures when dealing with large holes in complex images. This is partially due to the lack of effective network structures that can capture both the long-range dependency and high-level semantics of an image. We propose cascaded modulation GAN (CM-GAN), a new network design consisting of an encoder with Fourier convolution blocks that extract multi-scale feature representations from the input image with holes and a dual-stream decoder with a novel cascaded global-spatial modulation block at each scale level. In each decoder block, global modulation is first applied to perform coarse and semantic-aware structure synthesis, followed by spatial modulation to further adjust the feature map in a spatially adaptive fashion. In addition, we design an object-aware training scheme to prevent the network from hallucinating new objects inside holes, fulfilling the needs of object removal tasks in real-world scenarios. Extensive experiments are conducted to show that our method significantly outperforms existing methods in both quantitative and qualitative evaluation. Please refer to the project page: https://github.com/htzheng/CM-GAN-Inpainting."
StyleFace: Towards Identity-Disentangled Face Generation on Megapixels
Authors: Yuchen Luo; Junwei Zhu; Keke He; Wenqing Chu; Ying Tai; Chengjie Wang; Junchi Yan
Venue: ECCV 2022 (main)

Abstract: "Identity swapping and de-identification are two essential applications of identity-disentangled face image generation. Although sharing a similar problem definition, the two tasks have been long studied separately, and identity-disentangled face generation on megapixels is still under exploration. In this work, we propose StyleFace, a unified framework for 1024^2 resolution high-fidelity identity swapping and de-identification. To encode real identity while supporting virtual identity generation, we represent identity as a latent variable and further utilize contrastive learning for latent space regularization. Besides, we utilize StyleGAN2 to improve the generation quality on megapixels and devise an Adaptive Attribute Extractor, which adaptively preserves the identity-irrelevant attributes in a simple yet effective way. Extensive experiments demonstrate the state-of-the-art performance of StyleFace in high-resolution identity swapping and de-identification, respectively."
Video Extrapolation in Space and Time
Authors: Yunzhi Zhang; Jiajun Wu
Venue: ECCV 2022 (main)

Abstract: "Novel view synthesis (NVS) and video prediction (VP) are typically considered disjoint tasks in computer vision. However, they can both be seen as ways to observe the spatial-temporal world: NVS aims to synthesize a scene from a new point of view, while VP aims to see a scene from a new point of time. These two tasks provide complementary signals to obtain a scene representation, as viewpoint changes from spatial observations inform depth, and temporal observations inform the motion of cameras and individual objects. Inspired by these observations, we propose to study the problem of Video Extrapolation in Space and Time (VEST). We propose a model that tackles this problem and leverages the self-supervision from both tasks, while existing methods are designed to solve one of them. Experiments show that our method achieves performance better than or comparable to several state-of-the-art NVS and VP methods on indoor and outdoor real-world datasets."