CVPR 2024 Papers

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.

Reconstructing the viewed images from human brain activity bridges human and computer vision through the Brain-Computer Interface. The inherent variability in brain function between individuals leads existing literature to focus on acquiring separate models for each individual using their respective brain signal data ignoring commonalities between these data. In this article we devise Psychometry an omnifit model for reconstructing images from functional Magnetic Resonance Imaging (fMRI) obtained from different subjects. Psychometry incorporates an omni mixture-of-experts (Omni MoE) module where all the experts work together to capture the inter-subject commonalities while each expert associated with subject-specific parameters copes with the individual differences. Moreover Psychometry is equipped with a retrieval-enhanced inference strategy termed Ecphory which aims to enhance the learned fMRI representation via retrieving from prestored subject-specific memories. These designs collectively render Psychometry omnifit and efficient enabling it to capture both inter-subject commonality and individual specificity across subjects. As a result the enhanced fMRI representations serve as conditional signals to guide a generation model to reconstruct high-quality and realistic images establishing Psychometry as state-of-the-art in terms of both high-level and low-level metrics.

The rapid increase in cases of non-alcoholic fatty liver disease (NAFLD) in recent years has raised significant public concern. Accurately identifying tissue alteration regions is crucial for the diagnosis of NAFLD but this task presents challenges in pathology image analysis particularly with small-scale datasets. Recently the paradigm shift from full fine-tuning to prompting in adapting vision foundation models has offered a new perspective for small-scale data analysis. However existing prompting methods based on task-agnostic prompts are mainly developed for generic image recognition which fall short in providing instructive cues for complex pathology images. In this paper we propose Q uantitative A ttribute-based P rompting (QAP) a novel prompting method specifically for liver pathology image analysis. QAP is based on two quantitative attributes namely K-function-based spatial attributes and histogram-based morphological attributes which are aimed for quantitative assessment of tissue states. Moreover a conditional prompt generator is designed to turn these instance-specific attributes into visual prompts. Extensive experiments on three diverse tasks demonstrate that our task-specific prompting method achieves better diagnostic performance as well as better interpretability. Code is available at \href https://github.com/7LFB/QAP https://github.com/7LFB/QAP .

The quality of the prompts provided to text-to-image diffusion models determines how faithful the generated content is to the user's intent often requiring `prompt engineering'. To harness visual concepts from target images without prompt engineering current approaches largely rely on embedding inversion by optimizing and then mapping them to pseudo-tokens. However working with such high-dimensional vector representations is challenging because they lack semantics and interpretability and only allow simple vector operations when using them. Instead this work focuses on inverting the diffusion model to obtain interpretable language prompts directly. The challenge of doing this lies in the fact that the resulting optimization problem is fundamentally discrete and the space of prompts is exponentially large; this makes using standard optimization techniques such as stochastic gradient descent difficult. To this end we utilize a delayed projection scheme to optimize for prompts representative of the vocabulary space in the model. Further we leverage the findings that different timesteps of the diffusion process cater to different levels of detail in an image. The later noisy timesteps of the forward diffusion process correspond to the semantic information and therefore prompt inversion in this range provides tokens representative of the image semantics. We show that our approach can identify semantically interpretable and meaningful prompts for a target image which can be used to synthesize diverse images with similar content. We further illustrate the application of the optimized prompts in evolutionary image generation and concept removal.

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.

Text-guided image editing finds applications in various creative and practical fields. While recent studies in image generation have advanced the field they often struggle with the dual challenges of coherent image transformation and context preservation. In response our work introduces prompt augmentation a method amplifying a single input prompt into several target prompts strengthening textual context and enabling localised image editing. Specifically we use the augmented prompts to delineate the intended manipulation area. We propose a Contrastive Loss tailored to driving effective image editing by displacing edited areas and drawing preserved regions closer. Acknowledging the continuous nature of image manipulations we further refine our approach by incorporating the similarity concept creating a Soft Contrastive Loss. The new losses are incorporated to the diffusion model demonstrating improved or competitive image editing results on public datasets and generated images over state-of-the-art approaches.

Infrared and visible image fusion aims to generate a fused image by integrating and distinguishing complementary information from multiple sources. While the cross-attention mechanism with global spatial interactions appears promising it only capture second-order spatial interactions neglecting higher-order interactions in both spatial and channel dimensions. This limitation hampers the exploitation of synergies between multi-modalities. To bridge this gap we introduce a Synergistic High-order Interaction Paradigm (SHIP) designed to systematically investigate spatial fine-grained and global statistics collaborations between infrared and visible images across two fundamental dimensions: 1) Spatial dimension: we construct spatial fine-grained interactions through element-wise multiplication mathematically equivalent to global interactions and then foster high-order formats by iteratively aggregating and evolving complementary information enhancing both efficiency and flexibility. 2) Channel dimension: expanding on channel interactions with first-order statistics (mean) we devise high-order channel interactions to facilitate the discernment of inter-dependencies between source images based on global statistics. Harnessing high-order interactions significantly enhances our model's ability to exploit multi-modal synergies leading in superior performance over state-of-the-art alternatives as shown through comprehensive experiments across various benchmarks.

Diffusion models have achieved remarkable success in generating high-quality diverse and creative images. However in text-based image generation they often struggle to accurately capture the intended meaning of the text. For instance a specified object might not be generated or an adjective might incorrectly alter unintended objects. Moreover we found that relationships indicating possession between objects are frequently overlooked. Despite the diversity of users' intentions in text existing methods often focus on only some aspects of these intentions. In this paper we propose Predicated Diffusion a unified framework designed to more effectively express users' intentions. It represents the intended meaning as propositions using predicate logic and treats the pixels in attention maps as fuzzy predicates. This approach provides a differentiable loss function that offers guidance for the image generation process to better fulfill the propositions. Comparative evaluations with existing methods demonstrated that Predicated Diffusion excels in generating images faithful to various text prompts while maintaining high image quality as validated by human evaluators and pretrained image-text models.

Material appearance is a key component of photorealism with a pronounced impact on human perception. Although there are many prior works targeting at measuring opaque materials using light-weight setups (e.g. consumer-level cameras) little attention is paid on acquiring the optical properties of translucent materials which are also quite common in nature. In this paper we present a practical method for acquiring scattering properties of translucent materials based solely on ordinary images captured with unknown lighting and camera parameters. The key to our method is an inter-pixel translucency prior which states that image pixels of a given homogeneous translucent material typically form curves (dubbed translucent curves) in the RGB space of which the shapes are determined by the parameters of the material. We leverage this prior in a specially-designed convolutional neural network comprising multiple encoders a translucency-aware feature fusion module and a cascaded decoder. We demonstrate through both visual comparisons and quantitative evaluations that high accuracy can be achieved on a wide range of real-world translucent materials.

We introduce Posterior Distillation Sampling (PDS) a novel optimization method for parametric image editing based on diffusion models. Existing optimization-based methods which leverage the powerful 2D prior of diffusion models to handle various parametric images have mainly focused on generation. Unlike generation editing requires a balance between conforming to the target attribute and preserving the identity of the source content. Recent 2D image editing methods have achieved this balance by leveraging the stochastic latent encoded in the generative process of diffusion models. To extend the editing capabilities of diffusion models shown in pixel space to parameter space we reformulate the 2D image editing method into an optimization form named PDS. PDS matches the stochastic latents of the source and the target enabling the sampling of targets in diverse parameter spaces that align with a desired attribute while maintaining the source's identity. We demonstrate that this optimization resembles running a generative process with the target attribute but aligning this process with the trajectory of the source's generative process. Extensive editing results in Neural Radiance Fields and Scalable Vector Graphics representations demonstrate that PDS is capable of sampling targets to fulfill the aforementioned balance across various parameter spaces.

Recent advancements in personalized image generation using diffusion models have been noteworthy. However existing methods suffer from inefficiencies due to the requirement for subject-specific fine-tuning. This computationally intensive process hinders efficient deployment limiting practical usability. Moreover these methods often grapple with identity distortion and limited expression diversity. In light of these challenges we propose PortraitBooth an innovative approach designed for high efficiency robust identity preservation and expression-editable text-to-image generation without the need for fine-tuning. PortraitBooth leverages subject embeddings from a face recognition model for personalized image generation without fine-tuning. It eliminates computational overhead and mitigates identity distortion. The introduced dynamic identity preservation strategy further ensures close resemblance to the original image identity. Moreover PortraitBooth incorporates emotion-aware cross-attention control for diverse facial expressions in generated images supporting text-driven expression editing. Its scalability enables efficient and high-quality image creation including multi-subject generation. Extensive results demonstrate superior performance over other state-of-the-art methods in both single and multiple image generation scenarios.

Establishing an automatic evaluation metric that closely aligns with human judgments is essential for effectively developing image captioning models. Recent data-driven metrics have demonstrated a stronger correlation with human judgments than classic metrics such as CIDEr; however they lack sufficient capabilities to handle hallucinations and generalize across diverse images and texts partially because they compute scalar similarities merely using embeddings learned from tasks unrelated to image captioning evaluation. In this study we propose Polos a supervised automatic evaluation metric for image captioning models. Polos computes scores from multimodal inputs using a parallel feature extraction mechanism that leverages embeddings trained through large-scale contrastive learning. To train Polos we introduce Multimodal Metric Learning from Human Feedback (M2LHF) a framework for developing metrics based on human feedback. We constructed the Polaris dataset which comprises 131K human judgments from 550 evaluators which is approximately ten times larger than standard datasets. Our approach achieved state-of-the-art performance on Composite Flickr8K-Expert Flickr8K-CF PASCAL-50S FOIL and the Polaris dataset thereby demonstrating its effectiveness and robustness.

Diffusion models have shown tremendous results in image generation. However due to the iterative nature of the diffusion process and its reliance on classifier-free guidance inference times are slow. In this paper we propose a new distillation approach for guided diffusion models in which an external lightweight guide model is trained while the original text-to-image model remains frozen. We show that our method reduces the inference computation of classifier-free guided latent-space diffusion models by almost half and only requires 1% trainable parameters of the base model. Furthermore once trained our guide model can be applied to various fine-tuned domain-specific versions of the base diffusion model without the need for additional training: this "plug-and-play" functionality drastically improves inference computation while maintaining the visual fidelity of generated images. Empirically we show that our approach is able to produce visually appealing results and achieve a comparable FID score to the teacher with as few as 8 to 16 steps.

We present personalized residuals and localized attention-guided sampling for efficient concept-driven generation using text-to-image diffusion models. Our method first represents concepts by freezing the weights of a pretrained text-conditioned diffusion model and learning low-rank residuals for a small subset of the model's layers. The residual-based approach then directly enables application of our proposed sampling technique which applies the learned residuals only in areas where the concept is localized via cross-attention and applies the original diffusion weights in all other regions. Localized sampling therefore combines the learned identity of the concept with the existing generative prior of the underlying diffusion model. We show that personalized residuals effectively capture the identity of a concept in 3 minutes on a single GPU without the use of regularization images and with fewer parameters than previous models and localized sampling allows using the original model as strong prior for large parts of the image.

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

High dynamic range (HDR) rendering has the ability to faithfully reproduce the wide luminance ranges in natural scenes but how to accurately assess the rendering quality is relatively underexplored. Existing quality models are mostly designed for low dynamic range (LDR) images and do not align well with human perception of HDR image quality. To fill this gap we propose a family of HDR quality metrics in which the key step is employing a simple inverse display model to decompose an HDR image into a stack of LDR images with varying exposures. Subsequently these decomposed images are assessed through well-established LDR quality metrics. Our HDR quality models present three distinct benefits. First they directly inherit the recent advancements of LDR quality metrics. Second they do not rely on human perceptual data of HDR image quality for re-calibration. Third they facilitate the alignment and prioritization of specific luminance ranges for more accurate and detailed quality assessment. Experimental results show that our HDR quality metrics consistently outperform existing models in terms of quality assessment on four HDR image quality datasets and perceptual optimization of HDR novel view synthesis.

The quality of the data and annotation upper-bounds the quality of a downstream model. While there exist large text corpora and image-text pairs high-quality video-text data is much harder to collect. First of all manual labeling is more time-consuming as it requires an annotator to watch an entire video. Second videos have a temporal dimension consist of a number of scenes stacked together and show multiple actions. Accordingly to establish a video dataset with high-quality captions we propose an automatic approach leveraging multimodal inputs such as textual video description subtitles and individual video frames. Specifically we curate 3.8M high-resolution videos from the publicly available HD-VILA-100M dataset. We then split them into semantically consistent video clips and apply multiple cross-modality teacher models to obtain captions for each video. Next we finetune a retrieval model on a small subset where the best caption of each video is manually selected and then employ the model in the whole dataset to select the best caption as the annotation. In this way we get 70M videos paired with high-quality text captions. We dub the dataset as Panda-70M. We show the value of the proposed dataset on three downstream tasks: video captioning video and text retrieval and text-driven video generation. The models trained on the proposed data score substantially better on the majority of metrics across all the tasks.

Current vision-language pre-training (VLP) methodologies predominantly depend on paired image-text datasets a resource that is challenging to acquire in radiology due to privacy considerations and labelling complexities. Data augmentation provides a practical solution to overcome the issue of data scarcity however most augmentation methods exhibit a limited focus prioritising either image or text augmentation exclusively. Acknowledging this limitation our objective is to devise a framework capable of concurrently augmenting medical image and text data. We design a Pairwise Augmentation (PairAug) approach that contains an Inter-patient Augmentation (InterAug) branch and an Intra-patient Augmentation (IntraAug) branch. Specifically the InterAug branch of our approach generates radiology images using synthesised yet plausible reports derived from a Large Language Model (LLM). The generated pairs can be considered a collection of new patient cases since they are artificially created and may not exist in the original dataset. In contrast the IntraAug branch uses newly generated reports to manipulate images. This process allows us to create new paired data for each individual with diverse medical conditions. Our extensive experiments on various downstream tasks covering medical image classification zero-shot and fine-tuning analysis demonstrate that our PairAug concurrently expanding both image and text data substantially outperforms image-/text-only expansion baselines and advanced medical VLP baselines. Our code is released at https://github.com/YtongXie/PairAug.

This paper presents Paint3D a novel coarse-to-fine generative framework that is capable of producing high-resolution lighting-less and diverse 2K UV texture maps for untextured 3D meshes conditioned on text or image inputs. The key challenge addressed is generating high-quality textures without embedded illumination information which allows the textures to be re-lighted or re-edited within modern graphics pipelines. To achieve this our method first leverages a pre-trained depth-aware 2D diffusion model to generate view-conditional images and perform multi-view texture fusion producing an initial coarse texture map. However as 2D models cannot fully represent 3D shapes and disable lighting effects the coarse texture map exhibits incomplete areas and illumination artifacts. To resolve this we train separate UV Inpainting and UVHD diffusion models specialized for the shape-aware refinement of incomplete areas and the removal of illumination artifacts. Through this coarse-to-fine process Paint3D can produce high-quality 2K UV textures that maintain semantic consistency while being lighting-less significantly advancing the state-of-the-art in texturing 3D objects.