CVPR 2024 Papers

Recent image tone adjustment (or enhancement) approaches have predominantly adopted supervised learning for learning human-centric perceptual assessment. However these approaches are constrained by intrinsic challenges of supervised learning. Primarily the requirement for expertly-curated or retouched images escalates the data acquisition expenses. Moreover their coverage of target styles is confined to stylistic variants inferred from the training data. To surmount the above challenges we propose an unsupervised learning-based approach for text-based image tone adjustment CLIPtone that extends an existing image enhancement method to accommodate natural language descriptions. Specifically we design a hyper-network to adaptively modulate the pretrained parameters of a backbone model based on a text description. To assess whether an adjusted image aligns with its text description without a ground-truth image we utilize CLIP which is trained on a vast set of language-image pairs and thus encompasses the knowledge of human perception. The major advantages of our approach are threefold: (i) minimal data collection expenses (ii) support for a range of adjustments and (iii) the ability to handle novel text descriptions unseen in training. The efficacy of the proposed method is demonstrated through comprehensive experiments including a user study.

3D Scene Graph Generation (3DSGG) aims to classify objects and their predicates within 3D point cloud scenes. However current 3DSGG methods struggle with two main challenges. 1) The dependency on labor-intensive ground-truth annotations. 2) Closed-set classes training hampers the recognition of novel objects and predicates. Addressing these issues our idea is to extract cross-modality features by CLIP from text and image data naturally related to 3D point clouds. Cross-modality features are used to train a robust 3D scene graph (3DSG) feature extractor. Specifically we propose a novel Cross-Modality Contrastive Learning 3DSGG (CCL-3DSGG) method. Firstly to align the text with 3DSG the text is parsed into word level that are consistent with the 3DSG annotation. To enhance robustness during the alignment adjectives are exchanged for different objects as negative samples. Then to align the image with 3DSG the camera view is treated as a positive sample and other views as negatives. Lastly the recognition of novel object and predicate classes is achieved by calculating the cosine similarity between prompts and 3DSG features. Our rigorous experiments confirm the superior open-vocabulary capability and applicability of CCL-3DSGG in real-world contexts.

Face Image Quality Assessment (FIQA) is pivotal for guaranteeing the accuracy of face recognition in unconstrained environments. Recent progress in deep quality-fitting-based methods that train models to align with quality anchors has shown promise in FIQA. However these methods heavily depend on a recognition model to yield quality anchors and indiscriminately treat the confidence of inaccurate anchors as equivalent to that of accurate ones during the FIQA model training leading to a fitting bottleneck issue. This paper seeks a solution by putting forward the Confidence-Calibrated Face Image Quality Assessment (CLIB-FIQA) approach underpinned by the synergistic interplay between the quality anchors and objective quality factors such as blur pose expression occlusion and illumination. Specifically we devise a joint learning framework built upon the vision-language alignment model which leverages the joint distribution with multiple quality factors to facilitate the quality fitting of the FIQA model. Furthermore to alleviate the issue of the model placing excessive trust in inaccurate quality anchors we propose a confidence calibration method to correct the quality distribution by exploiting to the fullest extent of these objective quality factors characterized as the merged-factor distribution during training. Experimental results on eight datasets reveal the superior performance of the proposed method.

Diffusion Models (DMs) have evolved into advanced image generation tools especially for few-shot generation where a pre-trained model is fine-tuned on a small set of images to capture a specific style or object. Despite their success concerns exist about potential copyright violations stemming from the use of unauthorized data in this process. In response we present Contrasting Gradient Inversion for Diffusion Models (CGI-DM) a novel method featuring vivid visual representations for digital copyright authentication. Our approach involves removing partial information of an image and recovering missing details by exploiting conceptual differences between the pre-trained and fine-tuned models. We formulate the differences as KL divergence between latent variables of the two models when given the same input image which can be maximized through Monte Carlo sampling and Projected Gradient Descent (PGD). The similarity between original and recovered images serves as a strong indicator of potential infringements. Extensive experiments on the WikiArt and Dreambooth datasets demonstrate the high accuracy of CGI-DM in digital copyright authentication surpassing alternative validation techniques. Code implementation is available at https://github.com/Nicholas0228/Revelio.

We propose CG-HOI the first method to address the task of generating dynamic 3D human-object interactions (HOIs) from text. We model the motion of both human and object in an interdependent fashion as semantically rich human motion rarely happens in isolation without any interactions. Our key insight is that explicitly modeling contact between the human body surface and object geometry can be used as strong proxy guidance both during training and inference. Using this guidance to bridge human and object motion enables generating more realistic and physically plausible interaction sequences where the human body and corresponding object move in a coherent manner. Our method first learns to model human motion object motion and contact in a joint diffusion process inter-correlated through cross-attention. We then leverage this learned contact for guidance during inference to synthesize realistic and coherent HOIs. Extensive evaluation shows that our joint contact-based human-object interaction approach generates realistic and physically plausible sequences and we show two applications highlighting the capabilities of our method. Conditioned on a given object trajectory we can generate the corresponding human motion without re-training demonstrating strong human-object interdependency learning. Our approach is also flexible and can be applied to static real-world 3D scene scans.

Transformer-based models have revolutionized the field of image super-resolution (SR) by harnessing their inherent ability to capture complex contextual features. The overlapping rectangular shifted window technique used in transformer architecture nowadays is a common practice in super-resolution models to improve the quality and robustness of image upscaling. However it suffers from distortion at the boundaries and has limited unique shifting modes. To overcome these weaknesses we propose a non-overlapping triangular window technique that synchronously works with the rectangular one to mitigate boundary-level distortion and allows the model to access more unique sifting modes. In this paper we propose a Composite Fusion Attention Transformer (CFAT) that incorporates triangular-rectangular window-based local attention with a channel-based global attention technique in image super-resolution. As a result CFAT enables attention mechanisms to be activated on more image pixels and captures long-range multi-scale features to improve SR performance. The extensive experimental results and ablation study demonstrate the effectiveness of CFAT in the SR domain. Our proposed model shows a significant 0.7 dB performance improvement over other state-of-the-art SR architectures.

Existing Blind image Super-Resolution (BSR) methods focus on estimating either kernel or degradation information but have long overlooked the essential content details. In this paper we propose a novel BSR approach Content-aware Degradation-driven Transformer (CDFormer) to capture both degradation and content representations. However low-resolution images cannot provide enough content details and thus we introduce a diffusion-based module CDFormer_ diff to first learn Content Degradation Prior (CDP) in both low- and high-resolution images and then approximate the real distribution given only low-resolution information. Moreover we apply an adaptive SR network CDFormer_ SR that effectively utilizes CDP to refine features. Compared to previous diffusion-based SR methods we treat the diffusion model as an estimator that can overcome the limitations of expensive sampling time and excessive diversity. Experiments show that CDFormer can outperform existing methods establishing a new state-of-the-art performance on various benchmarks under blind settings. Codes and models will be available at https://github.com/I2-Multimedia-Lab/CDFormer.

Generative Adversarial Networks (GANs) dominate the research field in image-based virtual try-on but have not resolved problems such as unnatural deformation of garments and the blurry generation quality. While the generative quality of diffusion models is impressive achieving controllability poses a significant challenge when applying it to virtual try-on and multiple denoising iterations limit its potential for real-time applications. In this paper we propose Controllable Accelerated virtual Try-on with Diffusion Model (CAT-DM). To enhance the controllability a basic diffusion-based virtual try-on network is designed which utilizes ControlNet to introduce additional control conditions and improves the feature extraction of garment images. In terms of acceleration CAT-DM initiates a reverse denoising process with an implicit distribution generated by a pre-trained GAN-based model. Compared with previous try-on methods based on diffusion models CAT-DM not only retains the pattern and texture details of the in-shop garment but also reduces the sampling steps without compromising generation quality. Extensive experiments demonstrate the superiority of CAT-DM against both GAN-based and diffusion-based methods in producing more realistic images and accurately reproducing garment patterns.

To satisfy the rapidly increasing demands on the large image (2K-8K) super-resolution (SR) prevailing methods follow two independent tracks: 1) accelerate existing networks by content-aware routing and 2) design better super-resolution networks via token mixer refining. Despite directness they encounter unavoidable defects (e.g. inflexible route or non-discriminative processing) limiting further improvements of quality-complexity trade-off. To erase the drawbacks we integrate these schemes by proposing a content-aware mixer (CAMixer) which assigns convolution for simple contexts and additional deformable window-attention for sparse textures. Specifically the CAMixer uses a learnable predictor to generate multiple bootstraps including offsets for windows warping a mask for classifying windows and convolutional attentions for endowing convolution with the dynamic property which modulates attention to include more useful textures self-adaptively and improves the representation capability of convolution. We further introduce a global classification loss to improve the accuracy of predictors. By simply stacking CAMixers we obtain CAMixerSR which achieves superior performance on large-image SR lightweight SR and omnidirectional-image SR.

We address the challenge of generating 3D articulated objects in a controllable fashion. Currently modeling articulated 3D objects is either achieved through laborious manual authoring or using methods from prior work that are hard to scale and control directly. We leverage the interplay between part shape connectivity and motion using a denoising diffusion-based method with attention modules designed to extract correlations between part attributes. Our method takes an object category label and a part connectivity graph as input and generates an object's geometry and motion parameters. The generated objects conform to user-specified constraints on the object category part shape and part articulation. Our experiments show that our method outperforms the state-of-the-art in articulated object generation producing more realistic objects while conforming better to user constraints.

The increased demand for 3D data in AR/VR robotics and gaming applications gave rise to powerful generative pipelines capable of synthesizing high-quality 3D objects. Most of these models rely on the Score Distillation Sampling (SDS) algorithm to optimize a 3D representation such that the rendered image maintains a high likelihood as evaluated by a pre-trained diffusion model. However this distillation process involves finding a correct mode in the high-dimensional and large-variance distribution produced by the diffusion model. This task is challenging and often leads to issues such as over-saturation over-smoothing and Janus-like artifacts in the 3D generation. In this paper we propose a novel learning paradigm for 3D synthesis that utilizes pre-trained diffusion models. Instead of focusing on mode-seeking our method directly models the distribution discrepancy between multi-view renderings and diffusion priors in an adversarial manner which unlocks the generation of high-fidelity and photorealistic 3D content conditioned on a single image and prompt. Moreover by harnessing the latent space of GANs and expressive diffusion model priors our method enables a wide variety of 3D applications including single-view reconstruction high diversity generation and continuous 3D interpolation in open domain. Our experiments demonstrate the superiority of our pipeline compared to previous works in terms of generation quality and diversity.

We present Compound Conditioned ControlNet C3Net a novel generative neural architecture taking conditions from multiple modalities and synthesizing multimodal contents simultaneously (e.g. image text audio). C3Net adapts the ControlNet architecture to jointly train and make inferences on a production-ready diffusion model and its trainable copies. Specifically C3Net first aligns the conditions from multi-modalities to the same semantic latent space using modality-specific encoders based on contrastive training. Then it generates multimodal outputs based on the aligned latent space whose semantic information is combined using a ControlNet-like architecture called Control C3-UNet. Correspondingly with this system design our model offers an improved solution for joint-modality generation through learning and explaining multimodal conditions involving more than just linear interpolation within the latent space. Meanwhile as we align conditions to a unified latent space C3Net only requires one trainable Control C3-UNet to work on multimodal semantic information. Furthermore our model employs unimodal pretraining on the condition alignment stage outperforming the non-pretrained alignment even on relatively scarce training data and thus demonstrating high-quality compound condition generation. We contribute the first high-quality tri-modal validation set to validate quantitatively that C3Net outperforms or is on par with the first and contemporary state-of-the-art multimodal generation. Our codes and tri-modal dataset will be released.

Most neural compression models are trained on large datasets of images or videos in order to generalize to unseen data. Such generalization typically requires large and expressive architectures with a high decoding complexity. Here we introduce C3 a neural compression method with strong rate-distortion (RD) performance that instead overfits a small model to each image or video separately. The resulting decoding complexity of C3 can be an order of magnitude lower than neural baselines with similar RD performance. C3 builds on COOL-CHIC [Ladune et al 2023] and makes several simple and effective improvements for images. We further develop new methodology to apply C3 to videos. On the CLIC2020 image benchmark we match the RD performance of VTM the reference implementation of the H.266 codec with less than3k MACs/pixel for decoding. On the UVG video benchmark we match the RD performance of the Video Compression Transformer [Mentzer er al 2022] a well-established neural video codec with less than 5k MACs/pixel for decoding.

Radiologists highly desire fully automated versatile AI for medical imaging interpretation. However the lack of extensively annotated large-scale multi-disease datasets has hindered the achievement of this goal. In this paper we explore the feasibility of leveraging language as a naturally high-quality supervision for chest CT imaging. In light of the limited availability of image-report pairs we bootstrap the understanding of 3D chest CT images by distilling chest-related diagnostic knowledge from an extensively pre-trained 2D X-ray expert model. Specifically we propose a language-guided retrieval method to match each 3D CT image with its semantically closest 2D X-ray image and perform pair-wise and semantic relation knowledge distillation. Subsequently we use contrastive learning to align images and reports within the same patient while distinguishing them from the other patients. However the challenge arises when patients have similar semantic diagnoses such as healthy patients potentially confusing if treated as negatives. We introduce a robust contrastive learning that identifies and corrects these false negatives. We train our model with over 12K pairs of chest CT images and radiology reports. Extensive experiments across multiple scenarios including zero-shot learning report generation and fine-tuning processes demonstrate the model's feasibility in interpreting chest CT images.

As a bio-inspired vision sensor with ultra-high speed spike cameras exhibit great potential in recording dynamic scenes with high-speed motion or drastic light changes. Different from traditional cameras each pixel in spike cameras records the arrival of photons continuously by firing binary spikes at an ultra-fine temporal granularity. In this process multiple factors impact the imaging including the photons' Poisson arrival thermal noises from circuits and quantization effects in spike readout. These factors introduce fluctuations to spikes making the recorded spike intervals unstable and unable to reflect accurate light intensities. In this paper we present an approach to deal with spike fluctuations and boost spike camera image reconstruction. We first analyze the quantization effects and reveal the unbiased estimation attribute of the reciprocal of differential of spike firing time (DSFT). Based on this we propose a spike representation module to use DSFT with multiple orders for fluctuation suppression where DSFT with higher orders indicates spike integration duration between multiple spikes. We also propose a module for inter-moment feature alignment at multiple granularities. The coarser alignment is based on patch-level cross-attention with a local search strategy and the finer alignment is based on deformable convolution at the pixel level. Experimental results demonstrate the effectiveness of our method on both synthetic and real-captured data. The source code and dataset are available at https://github.com/ruizhao26/BSF.

Pre-trained models with large-scale training data such as CLIP and Stable Diffusion have demonstrated remarkable performance in various high-level computer vision tasks such as image understanding and generation from language descriptions. Yet their potential for low-level tasks such as image restoration remains relatively unexplored. In this paper we explore such models to enhance image restoration. As off-the-shelf features (OSF) from pre-trained models do not directly serve image restoration we propose to learn an additional lightweight module called Pre-Train-Guided Refinement Module (PTG-RM) to refine restoration results of a target restoration network with OSF. PTG-RM consists of two components Pre-Train-Guided Spatial-Varying Enhancement (PTG-SVE) and Pre-Train-Guided Channel-Spatial Attention (PTG-CSA). PTG-SVE enables optimal short- and long-range neural operations while PTG-CSA enhances spatial-channel attention for restoration-related learning. Extensive experiments demonstrate that PTG-RM with its compact size (<1M parameters) effectively enhances restoration performance of various models across different tasks including low-light enhancement deraining deblurring and denoising.

This paper presents an innovative framework designed to train an image deblurring algorithm tailored to a specific camera device. This algorithm works by transforming a blurry input image which is challenging to deblur into another blurry image that is more amenable to deblurring. The transformation process from one blurry state to another leverages unpaired data consisting of sharp and blurry images captured by the target camera device. Learning this blur-to-blur transformation is inherently simpler than direct blur-to-sharp conversion as it primarily involves modifying blur patterns rather than the intricate task of reconstructing fine image details. The efficacy of the proposed approach has been demonstrated through comprehensive experiments on various benchmarks where it significantly outperforms state-of-the-art methods both quantitatively and qualitatively. Our code and data are available at https://github.com/VinAIResearch/Blur2Blur

A novel approach to blind image quality assessment called quality comparison network (QCN) is proposed in this paper which sorts the feature vectors of input images according to their quality scores in an embedding space. QCN employs comparison transformers (CTs) and score pivots which act as the centroids of feature vectors of similar-quality images. Each CT updates the score pivots and the feature vectors of input images based on their ordered correlation. To this end we adopt four loss functions. Then we estimate the quality score of a test image by searching the nearest score pivot to its feature vector in the embedding space. Extensive experiments show that the proposed QCN algorithm yields excellent image quality assessment performances on various datasets. Furthermore QCN achieves great performances in cross-dataset evaluation demonstrating its superb generalization capability. The source codes are available at https://github.com/nhshin-mcl/QCN.

Dance serves as a powerful medium for expressing human emotions but the lifelike generation of dance is still a considerable challenge. Recently diffusion models have showcased remarkable generative abilities across various domains. They hold promise for human motion generation due to their adaptable many-to-many nature. Nonetheless current diffusion-based motion generation models often create entire motion sequences directly and unidirectionally lacking focus on the motion with local and bidirectional enhancement. When choreographing high-quality dance movements people need to take into account not only the musical context but also the nearby music-aligned dance motions. To authentically capture human behavior we propose a Bidirectional Autoregressive Diffusion Model (BADM) for music-to-dance generation where a bidirectional encoder is built to enforce that the generated dance is harmonious in both the forward and backward directions. To make the generated dance motion smoother a local information decoder is built for local motion enhancement. The proposed framework is able to generate new motions based on the input conditions and nearby motions which foresees individual motion slices iteratively and consolidates all predictions. To further refine the synchronicity between the generated dance and the beat the beat information is incorporated as an input to generate better music-aligned dance movements. Experimental results demonstrate that the proposed model achieves state-of-the-art performance compared to existing unidirectional approaches on the prominent benchmark for music-to-dance generation.