GenAI4ELab/papercli-papers huggingface dataset:
CoGS: Controllable Generation and Search from Sketch and Style
Authors: Cusuh Ham; Gemma Canet Tarrés; Tu Bui; James Hays; Zhe Lin; John Collomosse
Venue: ECCV 2022 (main)

Abstract: We present CoGS, a novel method for the style-conditioned, sketch-driven synthesis of images. CoGS enables exploration of diverse appearance possibilities for a given sketched object, enabling decoupled control over the structure and the appearance of the output. Coarse-grained control over object structure and appearance are enabled via an input sketch and an exemplar "style" conditioning image to a transformer-based sketch and style encoder to generate a discrete codebook representation. We map the codebook representation into a metric space, enabling fine-grained control over selection and interpolation between multiple synthesis options before generating the image via a vector quantized GAN (VQGAN) decoder. Our framework thereby unifies search and synthesis tasks, in that a sketch and style pair may be used to run an initial synthesis which may be refined via combination with similar results in a search corpus to produce an image more closely matching the user’s intent. We show that our model, trained on the 125 object classes of our newly created Pseudosketches dataset, is capable of producing a diverse gamut of semantic content and appearance styles.
HairNet: Hairstyle Transfer with Pose Changes
Authors: Peihao Zhu; Rameen Abdal; John Femiani; Peter Wonka
Venue: ECCV 2022 (main)

Abstract: "We propose a novel algorithm for automatic hairstyle transfer, specifically targeting complicated inputs that do not match in pose. The input to our algorithm are two images, one for the hairstyle and one for the identity (face). We do not require any additional inputs such as segmentation masks. Our algorithm consists of multiple steps and we contribute three novel components. The first contribution is the idea to include baldification into hairstyle editing pipelines to simplify inpainting of background and face regions covered by hair. The second contribution is a novel embedding algorithm that can handle both pose changes and semantic image blending. The third contribution is the hairnet architecture that semantically blends the hairstyle and identity images, performing multiple tasks jointly, such as baldification of the identity image, transformation estimation between the two images, warping, and hairstyle copying. Our results show a clear improvement over current state of the art methods in both quantitative and qualitative results. Code and data will be released."
Unbiased Multi-Modality Guidance for Image Inpainting
Authors: Yongsheng Yu; Dawei Du; Libo Zhang; Tiejian Luo
Venue: ECCV 2022 (main)

Abstract: "Image inpainting is an ill-posed problem to recover missing or damaged image content based on incomplete images with masks. Previous works usually predict the auxiliary structures (\eg, edges, segmentation and contours) to help fill visually realistic patches in a multi-stage fashion. However, imprecise auxiliary priors may yield biased inpainted results. Besides, it is time-consuming for some methods to be implemented by multiple stages of complex neural networks. To solve this issue, we develop an end-to-end multi-modality guided transformer network, including one inpainting branch and two auxiliary branches for semantic segmentation and edge textures. Within each transformer block, the proposed multi-scale spatial-aware attention module can learn the multi-modal structural features efficiently via auxiliary denormalization. Different from previous methods relying on direct guidance from biased priors, our method enriches semantically consistent context in an image based on discriminative interplay information from multiple modalities. Comprehensive experiments on several challenging image inpainting datasets show that our method achieves state-of-the-art performance to deal with various regular/irregular masks efficiently."
Intelli-Paint: Towards Developing More Human-Intelligible Painting Agents
Authors: Jaskirat Singh; Cameron Smith; Jose Echevarria; Liang Zheng
Venue: ECCV 2022 (main)

Abstract: "Stroke based rendering methods have recently become a popular solution for the generation of stylized paintings. However, the current research in this direction is focused mainly on the improvement of final canvas quality, and thus often fails to consider the intelligibility of the generated painting sequences to actual human users. In this work, we motivate the need to learn more human-intelligible painting sequences in order to facilitate the use of autonomous painting systems in a more interactive context (e.g. as a painting assistant tool for human users or for robotic painting applications). To this end, we propose a novel painting approach which learns to generate output canvases while exhibiting a painting style which is more relatable to human users. The proposed painting pipeline Intelli-Paint consists of 1) a progressive layering strategy which allows the agent to first paint a natural background scene before adding in each of the foreground objects in a progressive fashion. 2) We also introduce a novel sequential brushstroke guidance strategy which helps the painting agent to shift its attention between different image regions in a semantic-aware manner. 3) Finally, we propose a brushstroke regularization strategy which allows for 60-80% reduction in the total number of required brushstrokes without any perceivable differences in the quality of generated canvases. Through both quantitative and qualitative results, we show that the resulting agents not only show enhanced efficiency in output canvas generation but also exhibit a more natural-looking painting style which would better assist human users express their ideas through digital artwork."
Motion Transformer for Unsupervised Image Animation
Authors: Jiale Tao; Biao Wang; Tiezheng Ge; Yuning Jiang; Wen Li; Lixin Duan
Venue: ECCV 2022 (main)

Abstract: "Image animation aims to animate a source image by using motion learned from a driving video. Current state-of-the-art methods typically use convolutional neural networks (CNNs) to predict motion information, such as motion keypoints and corresponding local transformations. However, these CNN based methods do not explicitly model the interactions between motions; as a result, the important underlying motion relationship may be neglected, which can potentially lead to noticeable artifacts being produced in the generated animation video. To this end, we propose a new method, the motion transformer, which is the first attempt to build a motion estimator based on a vision transformer. More specifically, we introduce two types of tokens in our proposed method: i) image tokens formed from patch features and corresponding position encoding; and ii) motion tokens encoded with motion information. Both types of tokens are sent into vision transformers to promote underlying interactions between them through multi-head self attention blocks. By adopting this process, the motion information can be better learned to boost the model performance. The final embedded motion tokens are then used to predict the corresponding motion keypoints and local transformations. Extensive experiments on benchmark datasets show that our proposed method achieves promising results to the state-of-the-art baselines. Our source code will be public available."
NÜWA: Visual Synthesis Pre-training for Neural visUal World creAtion
Authors: Chenfei Wu; Jian Liang; Lei Ji; Fan Yang; Yuejian Fang; Daxin Jiang; Nan Duan
Venue: ECCV 2022 (main)

Abstract: "This paper presents a unified multimodal pre-trained model called NÃœWA that can generate new or manipulate existing visual data (i.e., image and video) for various visual synthesis tasks. To cover language, image, and video at the same time for different scenarios, a 3D transformer encoder-decoder framework is designed, which can not only deal with videos as 3D data but also adapt to texts and images as 1D and 2D data, respectively. A 3D Nearby Attention (3DNA) mechanism is also proposed to consider the nature of the visual data and reduce the computational complexity. We evaluate NÃœWA on 8 downstream tasks and 10 datasets. Compared to several strong baselines, NÃœWA achieves state-of-the-art results on text-to-image generation, text-to-video generation, video predictions, etc. It also shows surprisingly good zero-shot capabilities on text-guided image and video manipulation tasks."
EleGANt: Exquisite and Locally Editable GAN for Makeup Transfer
Authors: Chenyu Yang; Wanrong He; Yingqing Xu; Yang Gao
Venue: ECCV 2022 (main)

Abstract: "Most existing methods view makeup transfer as transferring color distributions of different facial regions and ignore details such as eye shadows and blushes. Besides, they only achieve controllable transfer within predefined fixed regions. This paper emphasizes the transfer of makeup details and steps towards more flexible controls. To this end, we propose Exquisite and locally editable GAN for makeup transfer (EleGANt). It encodes facial attributes into pyramidal feature maps to preserves high-frequency information. It uses attention to extract makeup features from the reference and adapt them to the source face, and we introduce a novel Sow-Attention Module that applies attention within shifted overlapped windows to reduce the computational cost. Moreover, EleGANt is the first to achieve customized local editing within arbitrary areas by corresponding editing on the feature maps. Extensive experiments demonstrate that EleGANt generates realistic makeup faces with exquisite details and achieves state-of-the-art performance. The code is available at https://github.com/Chenyu-Yang-2000/EleGANt."
Editing Out-of-Domain GAN Inversion via Differential Activations
Authors: Haorui Song; Yong Du; Tianyi Xiang; Junyu Dong; Jing Qin; Shengfeng He
Venue: ECCV 2022 (main)

Abstract: "Despite the demonstrated editing capacity in the latent space of a pretrained GAN model, inverting real-world images is stuck in a dilemma that the reconstruction cannot be faithful to the original input. The main reason for this is that the distributions between training and real-world data are misaligned, and because of that, it is unstable of GAN inversion for real image editing. In this paper, we propose a novel GAN prior based editing framework to tackle the out-of-domain inversion problem with a composition-decomposition paradigm. In particular, during the phase of composition, we introduce a differential activation module for detecting semantic changes from a global perspective, \ie, the relative gap between the features of edited and unedited images. With the aid of the generated Diff-CAM mask, a coarse reconstruction can intuitively be composited by the paired original and edited images. In this way, the attribute-irrelevant regions can be survived in almost whole, while the quality of such an intermediate result is still limited by an unavoidable ghosting effect. Consequently, in the decomposition phase, we further present a GAN prior based deghosting network for separating the final fine edited image from the coarse reconstruction. Extensive experiments exhibit superiorities over the state-of-the-art methods, in terms of qualitative and quantitative evaluations. The robustness and flexibility of our method is also validated on both scenarios of single attribute and multi-attribute manipulations. Code is available at https://github.com/HaoruiSong622/Editing-Out-of-Domain."
On the Robustness of Quality Measures for GANs
Authors: Motasem Alfarra; Juan C. Pérez; Anna Frühstück; Philip H. S. Torr; Peter Wonka; Bernard Ghanem
Venue: ECCV 2022 (main)

Abstract: "This work evaluates the robustness of quality measures of generative models such as Inception Score (IS) and Fréchet Inception Distance (FID). Analogous to the vulnerability of deep models against a variety of adversarial attacks, we show that such metrics can also be manipulated by additive pixel perturbations. Our experiments indicate that one can generate a distribution of images with very high scores but low perceptual quality. Conversely, one can optimize for small imperceptible perturbations that, when added to real world images, deteriorate their scores. We further extend our evaluation to generative models themselves, including the state of the art network StyleGANv2. We show the vulnerability of both the generative model and the FID against additive perturbations in the latent space. Finally, we show that the FID can be robustified by simply replacing the standard Inception with a robust Inception. We validate the effectiveness of the robustified metric through extensive experiments, showing it is more robust against manipulation."
Sound-Guided Semantic Video Generation
Authors: Seung Hyun Lee; Gyeongrok Oh; Wonmin Byeon; Chanyoung Kim; Won Jeong Ryoo; Sang Ho Yoon; Hyunjun Cho; Jihyun Bae; Jinkyu Kim; Sangpil Kim
Venue: ECCV 2022 (main)

Abstract: "The recent success in StyleGAN demonstrates that pre-trained StyleGAN latent space is useful for realistic video generation. However, the generated motion in the video is usually not semantically meaningful due to the difficulty of determining the direction and magnitude in the StyleGAN latent space. In this paper, we propose a framework to generate realistic videos by leveraging multimodal (sound-image-text) embedding space. As sound provides the temporal contexts of the scene, our framework learns to generate a video that is semantically consistent with sound. First, our sound inversion module maps the audio directly into the StyleGAN latent space. We then incorporate the CLIP-based multimodal embedding space to further provide the audio-visual relationships. Finally, the proposed frame generator learns to find the trajectory in the latent space which is coherent with the corresponding sound and generates a video in a hierarchical manner. We provide the new high-resolution landscape video dataset (audio-visual pair) for the sound-guided video generation task. The experiments show that our model outperforms the state-of-the-art methods in terms of video quality. We further show several applications including image and video editing to verify the effectiveness of our method. We provide diverse examples in the following anonymized project website: \url{https://anonymous5584.github.io}"
Inpainting at Modern Camera Resolution by Guided PatchMatch with Auto-Curation
Authors: Lingzhi Zhang; Connelly Barnes; Kevin Wampler; Sohrab Amirghodsi; Eli Shechtman; Zhe Lin; Jianbo Shi
Venue: ECCV 2022 (main)

Abstract: "Recently, deep models have established SOTA performance for low-resolution image inpainting, but they lack fidelity at resolutions associated with modern cameras such as 4K or more, and for large holes. We contribute an inpainting benchmark dataset of photos at 4K and above representative of modern sensors. We demonstrate a novel framework that combines deep learning and traditional methods. We use an existing deep inpainting model LaMa [28] to fill the hole plausibly, es- tablish three guide images consisting of structure, segmentation, depth, and apply a multiply-guided PatchMatch [1] to produce eight candidate upsampled inpainted images. Next, we feed all candidate inpaintings through a novel curation module that chooses a good inpainting by column summation on an 8x8 antisymmetric pairwise preference matrix. Our framework’s results are overwhelmingly preferred by users over 8 strong baselines, with improvements of quantitative metrics up to 7.4 times over the best baseline LaMa, and our technique when paired with 4 different SOTA inpainting backbones improves each such that ours is overwhelmingly preferred by users over a strong super-res baseline."
Controllable Video Generation through Global and Local Motion Dynamics
Authors: Aram Davtyan; Paolo Favaro
Venue: ECCV 2022 (main)

Abstract: "We present GLASS, a method for Global and Local Action-driven Sequence Synthesis. GLASS is a generative model that is trained on video sequences in an unsupervised manner and that can animate an input image at test time. The method learns to segment frames into foreground-background layers and to generate transitions of the foregrounds over time through a global and local action representation. Global actions are explicitly related to 2D shifts, while local actions are instead related to (both geometric and photometric) local deformations. GLASS uses a recurrent neural network to transition between frames and is trained through a reconstruction loss. We also introduce W-Sprites (Walking Sprites), a novel synthetic dataset with a predefined action space. We evaluate our method on both W-Sprites and real datasets, and find that GLASS is able to generate realistic video sequences from a single input image and to successfully learn a more advanced action space than in prior work. Further details, the code and example videos are available at https://araachie.github.io/glass/."
StyleHEAT: One-Shot High-Resolution Editable Talking Face Generation via Pre-trained StyleGAN
Authors: Fei Yin; Yong Zhang; Xiaodong Cun; Mingdeng Cao; Yanbo Fan; Xuan Wang; Qingyan Bai; Baoyuan Wu; Jue Wang; Yujiu Yang
Venue: ECCV 2022 (main)

Abstract: "One-shot talking face generation aims at synthesizing a high-quality talking face video from an arbitrary portrait image, driven by a video or an audio segment. In this work, we provide a solution from a novel perspective that differs from existing frameworks. We first investigate the latent feature space of a pre-trained StyleGAN and discover some excellent spatial transformation properties. Upon the observation, we propose a novel unified framework based on a pre-trained StyleGAN that enables a set of powerful functionalities, i.e., high-resolution video generation, disentangled control by driving video or audio, and flexible face editing. Our framework elevates the resolution of the synthesized talking face to 1024×1024 for the first time, even though the training dataset has a lower resolution. Moreover, our framework allows two types of facial editing, i.e., global editing via GAN inversion and intuitive editing via 3D morphable models. Comprehensive experiments show superior video quality and flexible controllability over state-of-the-art methods. Code is available at https://github.com/FeiiYin/StyleHEAT."
Long Video Generation with Time-Agnostic VQGAN and Time-Sensitive Transformer
Authors: Songwei Ge; Thomas Hayes; Harry Yang; Xi Yin; Guan Pang; David Jacobs; Jia-Bin Huang; Devi Parikh
Venue: ECCV 2022 (main)

Abstract: "Videos are created to express emotion, exchange information, and share experiences. Video synthesis has intrigued researchers for a long time. Despite the rapid progress driven by advances in visual synthesis, most existing studies focus on improving the frames’ quality and the transitions between them, while little progress has been made in generating longer videos. In this paper, we present a method that builds on 3D-VQGAN and transformers to generate videos with thousands of frames. Our evaluation shows that our model trained on 16-frame video clips from standard benchmarks such as UCF-101, Sky Time-lapse, and Taichi-HD datasets can generate diverse, coherent, and high-quality long videos. We also showcase conditional extensions of our approach for generating meaningful long videos by incorporating temporal information with text and audio. Videos and code can be found at https://songweige.github.io/projects/tats."
Combining Internal and External Constraints for Unrolling Shutter in Videos
Authors: Eyal Naor; Itai Antebi; Shai Bagon; Michal Irani
Venue: ECCV 2022 (main)

Abstract: "Videos obtained by rolling-shutter (RS) cameras result in spatially-distorted frames. These distortions become significant under fast camera/scene motions. Undoing effects of RS is sometimes addressed as a spatial problem, where objects need to be rectified/displaced in order to generate their correct global shutter (GS) frame. However, the cause of the RS effect is inherently temporal, not spatial. In this paper we propose a space-time solution to the RS problem. We observe that despite the severe differences between their xy-frames, a RS video and its corresponding GS video tend to share the exact same xt-slices - up to a known sub-frame temporal shift. Moreover, they share the same distribution of small 2D xt-patches, despite the strong temporal aliasing within each video. This allows to constrain the GS output video using video-specific constraints imposed by the RS input video. Our algorithm is composed of 3 main components: (i) Dense temporal upsampling between consecutive RS frames using an off-the-shelf method, (which was trained on regular video sequences), from which we extract GS “proposals”. (ii) Learning to correctly merge an ensemble of such GS “proposals” using a dedicated MergeNet. (iii) A video-specific zero-shot optimization which imposes the similarity of xt-patches between the GS output video and the RS input video. Our method obtains state-of-the-art results on benchmark datasets, both numerically and visually, despite being trained on a small synthetic RS/GS dataset. Moreover, it generalizes well to new complex RS videos with motion types outside the distribution of the training set (e.g., complex non-rigid motions) - videos which competing methods trained on much more data cannot handle well. We attribute these generalization capabilities to the combination of external and internal constraints."
WISE: Whitebox Image Stylization by Example-Based Learning
Authors: Winfried Lötzsch; Max Reimann; Martin Büssemeyer; Amir Semmo; Jürgen Döllner; Matthias Trapp
Venue: ECCV 2022 (main)

Abstract: "Image-based artistic rendering can synthesize a variety of expressive styles using algorithmic image filtering. In contrast to deep learning-based methods, these heuristics-based filtering techniques can operate on high-resolution images, are interpretable, and can be parameterized according to various design aspects. However, adapting or extending these techniques to produce new styles is often a tedious and error-prone task that requires expert knowledge. We propose a new paradigm to alleviate this problem: implementing algorithmic image filtering techniques as differentiable operations that can learn parametrizations aligned to certain reference styles. To this end, we present WISE, an example-based image-processing system that can handle a multitude of stylization techniques, such as watercolor, oil, or cartoon stylization, within a common framework. By training parameter prediction networks for global and local filter parameterizations, we can simultaneously adapt effects to reference styles and image content, e.g., to enhance facial features. Our method can be optimized in a style-transfer framework or learned in a generative-adversarial setting for image-to-image translation. We demonstrate that jointly training an xDoG filter and a CNN for postprocessing can achieve comparable results to a state-of-the-art GAN-based method."