mtybilly/Instruct2DS huggingface dataset:
Motion Transformer for Unsupervised Image Animation
Authors: Biao Wang, Tiezheng Ge, Yuning Jiang, Wen Li, Lixin Duan Jiale Tao
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

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: Jian Liang, Lei Ji, Fan Yang, Yuejian Fang, Daxin Jiang, Nan Duan Chenfei Wu
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

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: Wanrong He, Yingqing Xu, Yang Gao Chenyu Yang
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

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: Yong Du, Tianyi Xiang, Junyu Dong, Jing Qin, Shengfeng He Haorui Song
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

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: Juan C. Pérez, Anna Frühstück, Philip H. S. Torr, Peter Wonka, Bernard Ghanem Motasem Alfarra
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

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: Gyeongrok Oh, Wonmin Byeon, Chanyoung Kim, Won Jeong Ryoo, Sang Ho Yoon, Hyunjun Cho, Jihyun Bae, Jinkyu Kim, Sangpil Kim Seung Hyun Lee
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

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: Connelly Barnes, Kevin Wampler, Sohrab Amirghodsi, Eli Shechtman, Zhe Lin, Jianbo Shi Lingzhi Zhang
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

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: Paolo Favaro Aram Davtyan
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

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: Yong Zhang, Xiaodong Cun, Mingdeng Cao, Yanbo Fan, Xuan Wang, Qingyan Bai, Baoyuan Wu, Jue Wang, Yujiu Yang Fei Yin
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

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: Thomas Hayes, Harry Yang, Xi Yin, Guan Pang, David Jacobs, Jia-Bin Huang, Devi Parikh Songwei Ge
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

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: Itai Antebi, Shai Bagon, Michal Irani Eyal Naor
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

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: Max Reimann, Martin Büssemeyer, Amir Semmo, Jürgen Döllner, Matthias Trapp Winfried Lötzsch
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

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."
Neural Radiance Transfer Fields for Relightable Novel-View Synthesis with Global Illumination
Authors: Ayush Tewari, Thomas Leimkühler, Marc Habermann, Christian Theobalt Linjie Lyu
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Given a set of images of a scene, the re-rendering of this scene from novel views and lighting conditions is an important and challenging problem in Computer Vision and Graphics. On the one hand, most existing works in Computer Vision usually impose many assumptions regarding the image formation process, e.g. direct illumination and predefined materials, to make scene parameter estimation tractable. On the other hand, mature Computer Graphics tools allow modeling of complex photo-realistic light transport given all the scene parameters. Combining these approaches, we propose a method for scene relighting under novel views by learning a neural precomputed radiance transfer function, which implicitly handles global illumination effects using novel environment maps. Our method can be solely supervised on a set of real images of the scene under a single unknown lighting condition. To disambiguate the task during training, we tightly integrate a differentiable path tracer in the training process and propose a combination of a synthesized OLAT and a real image loss. Results show that the recovered disentanglement of scene parameters improves significantly over the current state of the art and, thus, also our re-rendering results are more realistic and accurate."
Transformers As Meta-Learners for Implicit Neural Representations
Authors: Xiaolong Wang Yinbo Chen
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Implicit Neural Representations (INRs) have emerged and shown their benefits over discrete representations in recent years. However, fitting an INR to the given observations usually requires optimization with gradient descent from scratch, which is inefficient and does not generalize well with sparse observations. To address this problem, most of the prior works train a hypernetwork that generates a single vector to modulate the INR weights, where the single vector becomes an information bottleneck that limits the reconstruction precision of the output INR. Recent work shows that the whole set of weights in INR can be precisely inferred without the single-vector bottleneck by gradient-based meta-learning. Motivated by a generalized formulation of gradient-based meta-learning, we propose a formulation that uses Transformers as hypernetworks for INRs, where it can directly build the whole set of INR weights with Transformers specialized as set-to-set mapping. We demonstrate the effectiveness of our method for building INRs in different tasks and domains, including 2D image regression and view synthesis for 3D objects. Our work draws connections between the Transformer hypernetworks and gradient-based meta-learning algorithms and we provide further analysis for understanding the generated INRs."
Style Your Hair: Latent Optimization for Pose-Invariant Hairstyle Transfer via Local-Style-Aware Hair Alignment
Authors: Chaeyeon Chung, Yoonseo Kim, Sunghyun Park, Kangyeol Kim, Jaegul Choo Taewoo Kim
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Editing hairstyle is unique and challenging due to the complexity and delicacy of hairstyle. Although recent approaches significantly improved the hair details, these models often produce undesirable outputs when a pose of a source image is considerably different from that of a target hair image, limiting their real-world applications. HairFIT, a pose-invariant hairstyle transfer model, alleviates this limitation yet still shows unsatisfactory quality in preserving delicate hair textures. To solve these limitations, we propose a high-performing pose-invariant hairstyle transfer model equipped with latent optimization and a newly presented local-style-matching loss. In the StyleGAN2 latent space, we first explore a pose-aligned latent code of a target hair with the detailed textures preserved based on local style matching. Then, our model inpaints the occlusions of the source considering the aligned target hair and blends both images to produce a final output. The experimental results demonstrate that our model has strengths in transferring a hairstyle under larger pose differences and preserving local hairstyle textures. The codes are available at https://github.com/Taeu/Style-Your-Hair."
High-Resolution Virtual Try-On with Misalignment and Occlusion-Handled Conditions
Authors: Gyojung Gu, Sunghyun Park, Seunghwan Choi, Jaegul Choo Sangyun Lee
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Image-based virtual try-on aims to synthesize an image of a person wearing a given clothing item. To solve the task, the existing methods warp the clothing item to fit the person’s body and generate the segmentation map of the person wearing the item before fusing the item with the person. However, when the warping and the segmentation generation stages operate individually without information exchange, the misalignment between the warped clothes and the segmentation map occurs, which leads to the artifacts in the final image. The information disconnection also causes excessive warping near the clothing regions occluded by the body parts, so-called pixel-squeezing artifacts. To settle the issues, we propose a novel try-on condition generator as a unified module of the two stages (i.e., warping and segmentation generation stages). A newly proposed feature fusion block in the condition generator implements the information exchange, and the condition generator does not create any misalignment or pixel-squeezing artifacts. We also introduce discriminator rejection that filters out the incorrect segmentation map predictions and assures the performance of virtual try-on frameworks. Experiments on a high-resolution dataset demonstrate that our model successfully handles the misalignment and occlusion, and significantly outperforms the baselines. Code is available at https://github.com/sangyun884/HR-VITON"
A Codec Information Assisted Framework for Efficient Compressed Video Super-Resolution
Authors: Xueyi Zou, Jiaming Guo, Youliang Yan, Rong Xie, Li Song Hengsheng Zhang
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Online processing of compressed videos to increase their resolutions attracts increasing and broad attention. Video Super-Resolution (VSR) using recurrent neural network architecture is a promising solution due to its efficient modeling of long-range temporal dependencies. However, state-of-the-art recurrent VSR models still require significant computation to obtain a good performance, mainly because of the complicated motion estimation for frame/feature alignment and the redundant processing of consecutive video frames. In this paper, considering the characteristics of compressed videos, we propose a Codec Information Assisted Framework (CIAF) to boost and accelerate recurrent VSR models for compressed videos. Firstly, the framework reuses the coded video information of Motion Vectors to model the temporal relationships between adjacent frames. Experiments demonstrate that the models with Motion Vector based alignment can significantly boost the performance with negligible additional computation, even comparable to those using more complex optical flow based alignment. Secondly, by further making use of the coded video information of Residuals, the framework can be informed to skip the computation on redundant pixels. Experiments demonstrate that the proposed framework can save up to 70% of the computation without performance drop on the REDS4 test videos encoded by H.264 when CRF is 23."
Injecting 3D Perception of Controllable NeRF-GAN into StyleGAN for Editable Portrait Image Synthesis
Authors: Yuanming Li, Dongsik Yoon, Donghyeon Kim, David Han, Hanseok Ko Jeong-gi Kwak
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Over the years, 2D GANs have achieved great successes in photorealistic portrait generation. However, they lack 3D understanding in the generation process, thus they suffer from multi-view inconsistency problem. To alleviate the issue, many 3D-aware GANs have been proposed and shown notable results, but 3D GANs struggle with editing semantic attributes. The controllability and interpretability of 3D GANs have not been much explored. In this work, we propose two solutions to overcome these weaknesses of 2D GANs and 3D-aware GANs. We first introduce a novel 3D-aware GAN, SURF-GAN, which is capable of discovering semantic attributes during training and controlling them in an unsupervised manner. After that, we inject the prior of SURF-GAN into StyleGAN to obtain a high-fidelity 3D-controllable generator. Unlike existing latent-based methods allowing implicit pose control, the proposed 3D-controllable StyleGAN enables explicit pose control over portrait generation. This distillation allows direct compatibility between 3D control and many StyleGAN-based techniques (e.g., inversion and stylization), and also brings an advantage in terms of computational resources. Our codes are available at https://github.com/jgkwak95/SURF-GAN."
AdaNeRF: Adaptive Sampling for Real-Time Rendering of Neural Radiance Fields
Authors: Thomas Neff, Zhaoyang Lv, Michael Zollhöfer, Markus Steinberger Andreas Kurz
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Novel view synthesis has recently been revolutionized by learning neural radiance fields directly from sparse observations. However, rendering images with this new paradigm is slow due to the fact that an accurate quadrature of the volume rendering equation requires a large number of samples for each ray. Previous work has mainly focused on speeding up the network evaluations that are associated with each sample point, e.g., via caching of radiance values into explicit spatial data structures, but this comes at the expense of model compactness. In this paper, we propose a novel dual-network architecture that takes an orthogonal direction by learning how to best reduce the number of required sample points. To this end, we split our network into a sampling and shading network that are jointly trained. Our training scheme employs fixed sample positions along each ray, and incrementally introduces sparsity throughout training to achieve high quality even at low sample counts. After fine-tuning with the target number of samples, the resulting compact neural representation can be rendered in real-time. Our experiments demonstrate that our approach outperforms concurrent compact neural representations in terms of quality and frame rate and performs on par with highly efficient hybrid representations. Code and supplementary material is available at https://thomasneff.github.io/adanerf."
Improving the Perceptual Quality of 2D Animation Interpolation
Authors: Matthias Zwicker Shuhong Chen
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Traditional 2D animation is labor-intensive, often requiring animators to manually draw twelve illustrations per second of movement. While automatic frame interpolation may ease this burden, 2D animation poses additional difficulties compared to photorealistic video. In this work, we address challenges unexplored in previous animation interpolation systems, with a focus on improving perceptual quality. Firstly, we propose SoftsplatLite (SSL), a forward-warping interpolation architecture with fewer trainable parameters and better perceptual performance. Secondly, we design a Distance Transform Module (DTM) that leverages line proximity cues to correct aberrations in difficult solid-color regions. Thirdly, we define a Restricted Relative Linear Discrepancy metric (RRLD) to automate the previously manual training data collection process. Lastly, we explore evaluation of 2D animation generation through a user study, and establish that the LPIPS perceptual metric and chamfer line distance (CD) are more appropriate measures of quality than PSNR and SSIM used in prior art."
Selective TransHDR: Transformer-Based Selective HDR Imaging Using Ghost Region Mask
Authors: Ye-In Park, Kyeongbo Kong, Jaeho Kwak, Suk-Ju Kang Jou Won Song
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "The primary issue in high dynamic range (HDR) imaging is the removal of ghost artifacts afforded when merging multi-exposure low dynamic range images. In the weakly misaligned region, ghost artifacts can be suppressed using convolutional neural network (CNN)-based methods. However, in highly misaligned regions, it is necessary to extract features from the global region because the necessary information does not exist in the local region. Therefore, the CNN-based methods specialized for local features extraction cannot obtain satisfactory results. To address this issue, we propose a transformer-based selective HDR image reconstruction network that uses a ghost region mask. The proposed method separates a given image into ghost and non-ghost regions, and then, selectively applies either the CNN or the transformer. The proposed selective transformer module divides an entire image into several regions to effectively extract the features of each region for HDR image reconstruction, thereby extracting the whole information required for HDR reconstruction in the ghost regions from the entire image. Extensive experiments conducted on several benchmark datasets demonstrate the superiority of the proposed method over existing state-of-the-art methods in terms of the mitigation of ghost artifacts."
Learning Series-Parallel Lookup Tables for Efficient Image Super-Resolution
Authors: Jingyi Zhang, Jie Zhou, Jiwen Lu Cheng Ma
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Lookup table (LUT) has shown its efficacy in low-level vision tasks due to the valuable characteristics of low computational cost and hardware independence. However, recent attempts to address the problem of single image super-resolution (SISR) with lookup tables are highly constrained by the small receptive field size. Besides, their frameworks of single-layer lookup tables limit the extension and generalization capacities of the model. In this paper, we propose a framework of series-parallel lookup tables (SPLUT) to alleviate the above issues and achieve efficient image super-resolution. On the one hand, we cascade multiple lookup tables to enlarge the receptive field of each extracted feature vector. On the other hand, we propose a parallel network which includes two branches of cascaded lookup tables which process different components of the input low-resolution images. By doing so, the two branches collaborate with each other and compensate for the precision loss of discretizing input pixels when establishing lookup tables. Compared to previous lookup table based methods, our framework has stronger representation abilities with more flexible architectures. Furthermore, we no longer need interpolation methods which introduce redundant computations so that our method can achieve faster inference speed. Extensive experimental results on five popular benchmark datasets show that our method obtains superior SISR performance in a more efficient way. The code is available at https://github.com/zhjy2016/SPLUT."
GeoAug: Data Augmentation for Few-Shot NeRF with Geometry Constraints
Authors: Yu Liu, Lianghua Huang, Bin Wang, Pan Pan Di Chen
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Neural Radiance Fields (NeRF) show remarkable ability to render novel views of a certain scene by learning an implicit volumetric representation with only posed RGB images. Despite its impressiveness and simplicity, NeRF usually converges to sub-optimal solutions with incorrect geometries given few training images. We hereby present GeoAug: a data augmentation method for NeRF, which enriches training data based on multi-view geometric constraint. GeoAug provides random artificial (novel pose, RGB image) pairs for training, where the RGB image is from a nearby training view. The rendering of a novel pose is warped to the nearby training view with depth map and relative pose to match the RGB image supervision. Our method reduces the risk of over-fitting by introducing more data during training, while also provides additional implicit supervision for depth maps. In experiments, our method significantly boosts the performance of neural radiance fields conditioned on few training views."
DoodleFormer: Creative Sketch Drawing with Transformers
Authors: Salman Khan, Hisham Cholakkal, Rao Muhammad Anwer, Fahad Shahbaz Khan, Jorma Laaksonen, Michael Felsberg Ankan Kumar Bhunia
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Creative sketching or doodling is an expressive activity, where imaginative and previously unseen depictions of everyday visual objects are drawn. Creative sketch image generation is a challenging vision problem, where the task is to generate diverse, yet realistic creative sketches possessing the unseen composition of the visual-world objects. Here, we propose a novel coarse-to-fine two-stage framework, DoodleFormer, that decomposes the creative sketch generation problem into the creation of coarse sketch composition followed by the incorporation of fine-details in the sketch. We introduce graph-aware transformer encoders that effectively capture global dynamic as well as local static structural relations among different body parts. To ensure diversity of the generated creative sketches, we introduce a probabilistic coarse sketch decoder that explicitly models the variations of each sketch body part to be drawn. Experiments are performed on two creative sketch datasets: Creative Birds and Creative Creatures. Our qualitative, quantitative and human-based evaluations show that DoodleFormer outperforms the state-of-the-art on both datasets, yielding realistic and diverse creative sketches. On Creative Creatures, DoodleFormer achieves an absolute gain of 25 in Fr\`echet inception distance (FID) over state-of-the-art. We also demonstrate the effectiveness of DoodleFormer for related applications of text to creative sketch generation, sketch completion and house layout generation."
Implicit Neural Representations for Variable Length Human Motion Generation
Authors: Yusuke Sekikawa, Ikuro Sato, Koichi Shinoda Pablo Cervantes
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "We propose an action-conditional human motion generation method using variational implicit neural representations (INR). The variational formalism enables action-conditional distributions of INRs, from which one can easily sample representations to generate novel human motion sequences. Our method offers variable-length sequence generation by construction because a part of INR is optimized for a whole sequence of arbitrary length with temporal embeddings. In contrast, previous works reported difficulties with modeling variable-length sequences. We confirm that our method with a Transformer decoder outperforms all relevant methods on HumanAct12, NTU-RGBD, and UESTC datasets in terms of realism and diversity of generated motions. Surprisingly, even our method with an MLP decoder consistently outperforms the state-of-the-art Transformer-based auto-encoder. In particular, we show that variable-length motions generated by our method are better than fixed-length motions generated by the state-of-the-art method in terms of realism and diversity. Code at https://github.com/PACerv/ImplicitMotion"
Learning Object Placement via Dual-Path Graph Completion
Authors: Liu Liu, Li Niu, Liqing Zhang Siyuan Zhou
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Object placement aims to place a foreground object over a background image with a suitable location and size. In this work, we treat object placement as a graph completion problem and propose a novel graph completion module (GCM). The background scene is represented by a graph with multiple nodes at different spatial locations with various receptive fields. The foreground object is encoded as a special node that should be inserted at a reasonable place in this graph. We also design a dual-path framework upon the structure of GCM to fully exploit annotated composite images. With extensive experiments on OPA dataset, our method proves to significantly outperform existing methods in generating plausible object placement without loss of diversity."
Expanded Adaptive Scaling Normalization for End to End Image Compression
Authors: Hyeongmin Lee, Hanbin Son, Sangjin Lee, Dogyoon Lee, Sangyoun Lee Chajin Shin
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Recently, learning-based image compression methods that utilize convolutional neural layers have been developed rapidly. Rescaling modules such as batch normalization which are often used in convolutional neural networks do not operate adaptively for the various inputs. Therefore, Generalized Divisible Normalization(GDN) has been widely used in image compression to rescale the input features adaptively across both spatial and channel axes. However, the representation power or degree of freedom of GDN is severely limited. Additionally, GDN cannot consider the spatial correlation of an image. To handle the limitations of GDN, we construct an expanded form of the adaptive scaling module, named Expanded Adaptive Scaling Normalization(EASN). First, we exploit the swish function to increase the representation ability. Then, we increase the receptive field to make the adaptive rescaling module consider the spatial correlation. Furthermore, we introduce an input mapping function to give the module a higher degree of freedom. We demonstrate how our EASN works in an image compression network using the visualization results of the feature map, and we conduct extensive experiments to show that our EASN increases the rate-distortion performance remarkably, and even outperforms the VVC intra at a high bit rate."
Generator Knows What Discriminator Should Learn in Unconditional GANs
Authors: Hyunsu Kim, Junho Kim, Seonghyeon Kim, Jung-Woo Ha, Yunjey Choi Gayoung Lee
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Recent methods for conditional image generation benefit from dense supervision such as segmentation label maps to achieve high-fidelity. However, it is rarely explored to employ dense supervision for unconditional image generation. Here we explore the efficacy of dense supervision in unconditional generation and find generator feature maps can be an alternative of cost-expensive semantic label maps. From our empirical evidences, we propose a new generator-guided discriminator regularization (GGDR) in which the generator feature maps supervise the discriminator to have rich semantic representations in unconditional generation. In specific, we employ an U-Net architecture for discriminator, which is trained to predict the generator feature maps given fake images as inputs. Extensive experiments on mulitple datasets show that our GGDR consistently improves the performance of baseline methods in terms of quantitative and qualitative aspects. Code is available at https://github.com/naver-ai/GGDR."
Compositional Visual Generation with Composable Diffusion Models
Authors: Shuang Li, Yilun Du, Antonio Torralba, Joshua B. Tenenbaum Nan Liu
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Large text-guided diffusion models, such as DALLE-2, are able to generate stunning photorealistic images given natural language descriptions. While such models are highly flexible, they struggle to understand the composition of certain concepts, such as confusing the attributes of different objects or relations between objects. In this paper, we propose an alternative structured approach for compositional generation using diffusion models. An image is generated by composing a set of diffusion models, with each of them modeling a certain component of the image. To do this, we interpret diffusion models as energy-based models in which the data distributions defined by the energy functions may be explicitly combined. The proposed method can generate scenes at test time that are substantially more complex than those seen in training, composing sentence descriptions, object relations, human facial attributes, and even generalizing to new combinations that are rarely seen in the real world. We further illustrate how our approach may be used to compose pre-trained text-guided diffusion models and generate photorealistic images containing all the details described in the input descriptions, including the binding of certain object attributes that have been shown difficult for DALLE-2. These results point to the effectiveness of the proposed method in promoting structured generalization for visual generation."
ManiFest: Manifold Deformation for Few-Shot Image Translation
Authors: Jean-François Lalonde, Raoul de Charette Fabio Pizzati
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Most image-to-image translation methods require a large number of training images, which restricts their applicability. We instead propose ManiFest: a framework for few-shot image translation that learns a context-aware representation of a target domain from a few images only. To enforce feature consistency, our framework learns a style manifold between source and additional anchor domains (assumed to be composed of large numbers of images). The learned manifold is interpolated and deformed towards the few-shot target domain via patch-based adversarial and feature statistics alignment losses. All of these components are trained simultaneously during a single end-to-end loop. In addition to the general few-shot translation task, our approach can alternatively be conditioned on a single exemplar image to reproduce its specific style. Extensive experiments demonstrate the efficacy of ManiFest on multiple tasks, outperforming the state-of-the-art on all metrics. Our code is avaliable at https://github.com/cv-rits/ManiFest."
Supervised Attribute Information Removal and Reconstruction for Image Manipulation
Authors: Bryan A. Plummer Nannan Li
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "The goal of attribute manipulation is to control specified attribute(s) in given images. Prior work approaches this problem by learning disentangled representations for each attribute that enables it to manipulate the encoded source attributes to the target attributes. However, encoded attributes are often correlated with relevant image content. Thus, the source attribute information can often be hidden in the disentangled features, leading to unwanted image editing effects. In this paper, we propose an Attribute Information Removal and Reconstruction (AIRR) network that prevents such information hiding by learning how to remove the attribute information entirely, creating attribute excluded features, and then learns to directly inject the desired attributes in a reconstructed image. We evaluate our approach on four diverse datasets with a variety of attributes including DeepFashion Synthesis, DeepFashion Fine-grained Attribute, CelebA and CelebA-HQ, where our model improves attribute manipulation accuracy and top-k retrieval rate by 10% on average over prior work. A user study also reports that AIRR manipulated images are preferred over prior work in up to 76% of cases."
BLT: Bidirectional Layout Transformer for Controllable Layout Generation
Authors: Lu Jiang, Huiwen Chang, Han Zhang, Yuan Hao, Haifeng Gong, Irfan Essa Xiang Kong
Published in: European Conference on Computer Vision (ECCV), 2022 (Main Conference Track), 2022

Abstract: "Creating visual layouts is a critical step in graphic design. Automatic generation of such layouts is essential for scalable and diverse visual designs. To advance conditional layout generation, we introduce BLT, a bidirectional layout transformer. BLT differs from previous work on transformers in adopting non-autoregressive transformers. In training, BLT learns to predict the masked attributes by attending to surrounding attributes in two directions. During inference, BLT first generates a draft layout from the input and then iteratively refines it into a high-quality layout by masking out low-confident attributes. The masks generated in both training and inference are controlled by a new hierarchical sampling policy. We verify the proposed model on six benchmarks of diverse design tasks. Experimental results demonstrate two benefits compared to the state-of-the-art layout transformer models. First, our model empowers layout transformers to fulfill controllable layout generation. Second, it achieves up to 10x speedup in generating a layout at inference time than the layout transformer baseline. Code is released at https://shawnkx.github.io/blt."