CVPR 2024 Papers

In this paper we explore the potential of Snapshot Com- pressive Imaging (SCI) technique for recovering the under- lying 3D scene representation from a single temporal com- pressed image. SCI is a cost-effective method that enables the recording of high-dimensional data such as hyperspec- tral or temporal information into a single image using low- cost 2D imaging sensors. To achieve this a series of spe- cially designed 2D masks are usually employed which not only reduces storage requirements but also offers potential privacy protection. Inspired by this to take one step further our approach builds upon the powerful 3D scene represen- tation capabilities of neural radiance fields (NeRF). Specif- ically we formulate the physical imaging process of SCI as part of the training of NeRF allowing us to exploit its impressive performance in capturing complex scene struc- tures. To assess the effectiveness of our method we con- duct extensive evaluations using both synthetic data and real data captured by our SCI system. Extensive experi- mental results demonstrate that our proposed approach sur- passes the state-of-the-art methods in terms of image re- construction and novel view image synthesis. Moreover our method also exhibits the ability to restore high frame- rate multi-view consistent images by leveraging SCI and the rendering capabilities of NeRF. The code is available at https://github.com/WU-CVGL/SCINeRF.

Image diffusion models have been utilized in various tasks such as text-to-image generation and controllable image synthesis. Recent research has introduced tuning methods that make subtle adjustments to the original models yielding promising results in specific adaptations of foundational generative diffusion models. Rather than modifying the main backbone of the diffusion model we delve into the role of skip connection in U-Net and reveal that hierarchical features aggregating long-distance information across encoder and decoder make a significant impact on the content and quality of image generation. Based on the observation we propose an efficient generative tuning framework dubbed SCEdit which integrates and edits Skip Connection using a lightweight tuning module named SC-Tuner. Furthermore the proposed framework allows for straightforward extension to controllable image synthesis by injecting different conditions with Controllable SC-Tuner simplifying and unifying the network design for multi-condition inputs. Our SCEdit substantially reduces training parameters memory usage and computational expense due to its lightweight tuners with backward propagation only passing to the decoder blocks. Extensive experiments conducted on text-to-image generation and controllable image synthesis tasks demonstrate the superiority of our method in terms of efficiency and performance. Project page: https://scedit.github.io/.

This research focuses on the issue of single-image reflection removal (SIRR) in real-world conditions examining it from two angles: the collection pipeline of real reflection pairs and the perception of real reflection locations. We devise an advanced reflection collection pipeline that is highly adaptable to a wide range of real-world reflection scenarios and incurs reduced costs in collecting large-scale aligned reflection pairs. In the process we develop a large-scale high-quality reflection dataset named Reflection Removal in the Wild (RRW). RRW contains over 14950 high-resolution real-world reflection pairs a dataset forty-five times larger than its predecessors. Regarding perception of reflection locations we identify that numerous virtual reflection objects visible in reflection images are not present in the corresponding ground-truth images. This observation drawn from the aligned pairs leads us to conceive the Maximum Reflection Filter (MaxRF). The MaxRF could accurately and explicitly characterize reflection locations from pairs of images. Building upon this we design a reflection location-aware cascaded framework specifically tailored for SIRR. Powered by these innovative techniques our solution achieves superior performance than current leading methods across multiple real-world benchmarks. Codes and datasets are available at \href https://github.com/zhuyr97/Reflection_RemoVal_CVPR2024 \color blue here .

Significant advancements have been made in image editing with the recent advance of the Diffusion model. However most of the current methods primarily focus on global or subject-level modifications and often face limitations when it comes to editing specific objects when there are other objects coexisting in the scene given solely textual prompts. In response to this challenge we introduce an object-level generative task called Referring Image Editing (RIE) which enables the identification and editing of specific source objects in an image using text prompts. To tackle this task effectively we propose a tailored framework called ReferDiffusion. It aims to disentangle input prompts into multiple embeddings and employs a mixed-supervised multi-stage training strategy. To facilitate further research in this domain we introduce the RefCOCO-Edit dataset comprising images editing prompts source object segmentation masks and reference edited images for training and evaluation. Our extensive experiments demonstrate the effectiveness of our approach in identifying and editing target objects while conventional general image editing and region-based image editing methods have difficulties in this challenging task.

Existing text-to-image (T2I) diffusion models usually struggle in interpreting complex prompts especially those with quantity object-attribute binding and multi-subject descriptions. In this work we introduce a semantic panel as the middleware in decoding texts to images supporting the generator to better follow instructions. The panel is obtained through arranging the visual concepts parsed from the input text by the aid of large language models and then injected into the denoising network as a detailed control signal to complement the text condition. To facilitate text-to-panel learning we come up with a carefully designed semantic formatting protocol accompanied by a fully-automatic data preparation pipeline. Thanks to such a design our approach which we call Ranni manages to enhance a pre-trained T2I generator regarding its textual controllability. More importantly the introduction of the generative middleware brings a more convenient form of interaction (i.e. directly adjusting the elements in the panel or using language instructions) and further allows users to finely customize their generation based on which we develop a practical system and showcase its potential in continuous generation and chatting-based editing.

Text-to-image (T2I) research has grown explosively in the past year owing to the large-scale pre-trained diffusion models and many emerging personalization and editing approaches. Yet one pain point persists: the text prompt engineering and searching high-quality text prompts for customized results is more art than science. Moreover as commonly argued: "an image is worth a thousand words" - the attempt to describe a desired image with texts often ends up being ambiguous and cannot comprehensively cover delicate visual details hence necessitating more additional controls from the visual domain. In this paper we take a bold step forward: taking "Text" out of a pretrained T2I diffusion model to reduce the burdensome prompt engineering efforts for users. Our proposed framework Prompt-Free Diffusion relies on only visual inputs to generate new images: it takes a reference image as "context" an optional image structural conditioning and an initial noise with absolutely no text prompt. The core architecture behind the scene is Semantic Context Encoder (SeeCoder) substituting the commonly used CLIP-based or LLM-based text encoder. The reusability of SeeCoder also makes it a convenient drop-in component: one can also pre-train a SeeCoder in one T2I model and reuse it for another. Through extensive experiments Prompt-Free Diffusion is experimentally found to (i) outperform prior exemplar-based image synthesis approaches; (ii) perform on par with state-of-the-art T2I models using prompts following the best practice; and (iii) be naturally extensible to other downstream applications such as anime figure generation and virtual try-on with promising quality. Our code and models will be open-sourced.

Recently there were remarkable advances in image editing tasks in various ways. Nevertheless existing image editing models are not designed for Human-Object Interaction (HOI) image editing. One of these approaches (e.g. ControlNet) employs the skeleton guidance to offer precise representations of human showing better results in HOI image editing. However using conventional methods manually creating HOI skeleton guidance is necessary. This paper proposes the object interactive diffuser with associative attention that considers both the interaction with objects and the joint graph structure automating the generation of HOI skeleton guidance. Additionally we propose the HOI loss with novel scaling parameter demonstrating its effectiveness in generating skeletons that interact better. To evaluate generated object-interactive skeletons we propose two metrics top-N accuracy and skeleton probabilistic distance. Our framework integrates object interactive diffuser that generates object-interactive skeletons with previous methods demonstrating the outstanding results in HOI image editing. Finally we present potentials of our framework beyond HOI image editing as applications to human-to-human interaction skeleton editing and 3D mesh optimization. The code is available at https://github.com/YangChangHee/CVPR2024_Person-In-Place_RELEASE

Planet-scale image geolocalization remains a challenging problem due to the diversity of images originating from anywhere in the world. Although approaches based on vision transformers have made significant progress in geolocalization accuracy success in prior literature is constrained to narrow distributions of images of landmarks and performance has not generalized to unseen places. We present a new geolocalization system that combines semantic geocell creation multi-task contrastive pretraining and a novel loss function. Additionally our work is the first to perform retrieval over location clusters for guess refinements. We train two models for evaluations on street-level data and general-purpose image geolocalization; the first model PIGEON is trained on data from the game of GeoGuessr and is capable of placing over 40% of its guesses within 25 kilometers of the target location globally. We also develop a bot and deploy PIGEON in a blind experiment against humans ranking in the top 0.01% of players. We further challenge one of the world's foremost professional GeoGuessr players to a series of six matches with millions of viewers winning all six games. Our second model PIGEOTTO differs in that it is trained on a dataset of images from Flickr and Wikipedia achieving state-of-the-art results on a wide range of image geolocalization benchmarks outperforming the previous SOTA by up to 7.7 percentage points on the city accuracy level and up to 38.8 percentage points on the country level. Our findings suggest that PIGEOTTO is the first image geolocalization model that effectively generalizes to unseen places and that our approach can pave the way for highly accurate planet-scale image geolocalization systems. Our code is available on GitHub.

In this paper we propose a novel virtual try-on from unconstrained designs (ucVTON) task to enable photorealistic synthesis of personalized composite clothing on input human images. Unlike prior arts constrained by specific input types our method allows flexible specification of style (text or image) and texture (full garment cropped sections or texture patches) conditions. To address the entanglement challenge when using full garment images as conditions we develop a two-stage pipeline with explicit disentanglement of style and texture. In the first stage we generate a human parsing map reflecting the desired style conditioned on the input. In the second stage we composite textures onto the parsing map areas based on the texture input. To represent complex and non-stationary textures that have never been achieved in previous fashion editing works we first propose extracting hierarchical and balanced CLIP features and applying position encoding in VTON. Experiments demonstrate superior synthesis quality and personalization enabled by our method. The flexible control over style and texture mixing brings virtual try-on to a new level of user experience for online shopping and fashion design.

Generative image editing has recently witnessed extremely fast-paced growth. Some works use high-level conditioning such as text while others use low-level conditioning. Nevertheless most of them lack fine-grained control over the properties of the different objects present in the image i.e. object-level image editing. In this work we tackle the task by perceiving the images as an amalgamation of various objects and aim to control the properties of each object in a fine-grained manner. Out of these properties we identify structure and appearance as the most intuitive to understand and useful for editing purposes. We propose PAIR Diffusion a generic framework that enables a diffusion model to control the structure and appearance properties of each object in the image. We show that having control over the properties of each object in an image leads to comprehensive editing capabilities. Our framework allows for various object-level editing operations on real images such as reference image-based appearance editing free-form shape editing adding objects and variations. Thanks to our design we do not require any inversion step. Additionally we propose multimodal classifier-free guidance which enables editing images using both reference images and text when using our approach with foundational diffusion models. We validate the above claims by extensively evaluating our framework on both unconditional and foundational diffusion models.

Diffusion probabilistic models (DPMs) are a key component in modern generative models. DPM-solvers have achieved reduced latency and enhanced quality significantly but have posed challenges to find the exact inverse (i.e. finding the initial noise from the given image). Here we investigate the exact inversions for DPM-solvers and propose algorithms to perform them when samples are generated by the first-order as well as higher-order DPM-solvers. For each explicit denoising step in DPM-solvers we formulated the inversions using implicit methods such as gradient descent or forward step method to ensure the robustness to large classifier-free guidance unlike the prior approach using fixed-point iteration. Experimental results demonstrated that our proposed exact inversion methods significantly reduced the error of both image and noise reconstructions greatly enhanced the ability to distinguish invisible watermarks and well prevented unintended background changes consistently during image editing.

Deep learning has led to a dramatic leap on Single Image Super-Resolution (SISR) performances in recent years. While most existing work assumes a simple and fixed degradation model (e.g. bicubic downsampling) the research of Blind SR seeks to improve model generalization ability with unknown degradation. Recently Kong et al. pioneer the investigation of a more suitable training strategy for Blind SR using Dropout. Although such method indeed brings substantial generalization improvements via mitigating overfitting we argue that Dropout simultaneously introduces undesirable side-effect that compromises model's capacity to faithfully reconstruct fine details. We show both the theoretical and experimental analyses in our paper and furthermore we present another easy yet effective training strategy that enhances the generalization ability of the model by simply modulating its first and second-order features statistics. Experimental results have shown that our method could serve as a model-agnostic regularization and outperforms Dropout on seven benchmark datasets including both synthetic and real-world scenarios.

The practicality of 3D object pose estimation remains limited for many applications due to the need for prior knowledge of a 3D model and a training period for new objects. To address this limitation we propose an approach that takes a single image of a new object as input and predicts the relative pose of this object in new images without prior knowledge of the object's 3D model and without requiring training time for new objects and categories. We achieve this by training a model to directly predict discriminative embeddings for viewpoints surrounding the object. This prediction is done using a simple U-Net architecture with attention and conditioned on the desired pose which yields extremely fast inference. We compare our approach to state-of-the-art methods and show it outperforms them both in terms of accuracy and robustness.

We introduce MultiDiff a novel approach for consistent novel view synthesis of scenes from a single RGB image. The task of synthesizing novel views from a single reference image is highly ill-posed by nature as there exist multiple plausible explanations for unobserved areas. To address this issue we incorporate strong priors in form of monocular depth predictors and video-diffusion models. Monocular depth enables us to condition our model on warped reference images for the target views increasing geometric stability. The video-diffusion prior provides a strong proxy for 3D scenes allowing the model to learn continuous and pixel-accurate correspondences across generated images. In contrast to approaches relying on autoregressive image generation that are prone to drifts and error accumulation MultiDiff jointly synthesizes a sequence of frames yielding high-quality and multi-view consistent results -- even for long-term scene generation with large camera movements while reducing inference time by an order of magnitude. For additional consistency and image quality improvements we introduce a novel structured noise distribution. Our experimental results demonstrate that MultiDiff outperforms state-of-the-art methods on the challenging real-world datasets RealEstate10K and ScanNet. Finally our model naturally supports multi-view consistent editing without the need for further tuning.

Physically-Based Rendering (PBR) is key to modeling the interaction between light and materials and finds extensive applications across computer graphics domains. However acquiring PBR materials is costly and requires special apparatus. In this paper we propose a method to extract PBR materials from a single real-world image. We do so in two steps: first we map regions of the image to material concept tokens using a diffusion model allowing the sampling of texture images resembling each material in the scene. Second we leverage a separate network to decompose the generated textures into spatially varying BRDFs (SVBRDFs) offering us readily usable materials for rendering applications. Our approach relies on existing synthetic material libraries with SVBRDF ground truth. It exploits a diffusion-generated RGB texture dataset to allow generalization to new samples using unsupervised domain adaptation (UDA). Our contributions are thoroughly evaluated on synthetic and real-world datasets. We further demonstrate the applicability of our method for editing 3D scenes with materials estimated from real photographs. Along with video we share code and models as open-source on the project page: https://github.com/astra-vision/MaterialPalette

Recent advances in generative AI have significantly enhanced image and video editing particularly in the context of text prompt control. State-of-the-art approaches predominantly rely on diffusion models to accomplish these tasks. However the computational demands of diffusion-based methods are substantial often necessitating large-scale paired datasets for training and therefore challenging the deployment in real applications. To address these issues this paper breaks down the text-based video editing task into two stages. First we leverage an pre-trained text-to-image diffusion model to simultaneously edit few keyframes in an zero-shot way. Second we introduce an efficient model called MaskINT which is built on non-autoregressive masked generative transformers and specializes in frame interpolation between the edited keyframes using the structural guidance from intermediate frames. Experimental results suggest that our MaskINT achieves comparable performance with diffusion-based methodologies while significantly improve the inference time. This research offers a practical solution for text-based video editing and showcases the potential of non-autoregressive masked generative transformers in this domain.

Autoregressive Initial Bits (ArIB) a framework that combines subimage autoregression and latent variable models has shown its advantages in lossless image compression. However in current methods the image splitting makes the information of latent variables being uniformly distributed in each subimage and causes inadequate use of latent variables in addition to posterior collapse. To tackle these issues we introduce Bit Plane Slicing (BPS) splitting images in the bit plane dimension with the considerations on different importance for latent variables. Thus BPS provides a more effective representation by arranging subimages with decreasing importance for latent variables. To solve the problem of the increased number of dimensions caused by BPS we further propose a dimension-tailored autoregressive model that tailors autoregression methods for each dimension based on their characteristics efficiently capturing the dependencies in plane space and color dimensions. As shown in the extensive experimental results our method demonstrates the superior compression performance with comparable inference speed when compared to the state-of-the-art normalizing-flow-based methods. The code is at https://github.com/ZZ022/ArIB-BPS.

This paper studies the problem of language-guided reflection separation which aims at addressing the ill-posed reflection separation problem by introducing language descriptions to provide layer content. We propose a unified framework to solve this problem which leverages the cross-attention mechanism with contrastive learning strategies to construct the correspondence between language descriptions and image layers. A gated network design and a randomized training strategy are employed to tackle the recognizable layer ambiguity. The effectiveness of the proposed method is validated by the significant performance advantage over existing reflection separation methods on both quantitative and qualitative comparisons.

Text-to-image diffusion models have recently received increasing interest for their astonishing ability to produce high-fidelity images from solely text inputs. Subsequent research efforts aim to exploit and apply their capabilities to real image editing. However existing image-to-image methods are often inefficient imprecise and of limited versatility. They either require time-consuming fine-tuning deviate unnecessarily strongly from the input image and/or lack support for multiple simultaneous edits. To address these issues we introduce LEDITS++ an efficient yet versatile and precise textual image manipulation technique. LEDITS++'s novel inversion approach requires no tuning nor optimization and produces high-fidelity results with a few diffusion steps. Second our methodology supports multiple simultaneous edits and is architecture-agnostic. Third we use a novel implicit masking technique that limits changes to relevant image regions. We propose the novel TEdBench++ benchmark as part of our exhaustive evaluation. Our results demonstrate the capabilities of LEDITS++ and its improvements over previous methods.