CVPR 2024 论文列表

We present Intrinsic Image Diffusion a generative model for appearance decomposition of indoor scenes. Given a single input view we sample multiple possible material explanations represented as albedo roughness and metallic maps. Appearance decomposition poses a considerable challenge in computer vision due to the inherent ambiguity between lighting and material properties and the lack of real datasets. To address this issue we advocate for a probabilistic formulation where instead of attempting to directly predict the true material properties we employ a conditional generative model to sample from the solution space. Furthermore we show that utilizing the strong learned prior of recent diffusion models trained on large-scale real-world images can be adapted to material estimation and highly improves the generalization to real images. Our method produces significantly sharper more consistent and more detailed materials outperforming state-of-the-art methods by 1.5dB on PSNR and by 45% better FID score on albedo prediction. We demonstrate the effectiveness of our approach through experiments on both synthetic and real-world datasets.

Denoising probabilistic diffusion models have shown breakthrough performance to generate more photo-realistic images or human-level illustrations than the prior models such as GANs. This high image-generation capability has stimulated the creation of many downstream applications in various areas. However we find that this technology is actually a double-edged sword: We identify a new type of attack called the Natural Denoising Diffusion (NDD) attack based on the finding that state-of-the-art deep neural network (DNN) models still hold their prediction even if we intentionally remove their robust features which are essential to the human visual system (HVS) through text prompts. The NDD attack shows a significantly high capability to generate low-cost model-agnostic and transferable adversarial attacks by exploiting the natural attack capability in diffusion models. To systematically evaluate the risk of the NDD attack we perform a large-scale empirical study with our newly created dataset the Natural Denoising Diffusion Attack (NDDA) dataset. We evaluate the natural attack capability by answering 6 research questions. Through a user study we find that it can achieve an 88% detection rate while being stealthy to 93% of human subjects; we also find that the non-robust features embedded by diffusion models contribute to the natural attack capability. To confirm the model-agnostic and transferable attack capability we perform the NDD attack against the Tesla Model 3 and find that 73% of the physically printed attacks can be detected as stop signs. Our hope is that the study and dataset can help our community be aware of the risks in diffusion models and facilitate further research toward robust DNN models.

Large-scale text-to-image (T2I) diffusion models have showcased incredible capabilities in generating coherent images based on textual descriptions enabling vast applications in content generation. While recent advancements have introduced control over factors such as object localization posture and image contours a crucial gap remains in our ability to control the interactions between objects in the generated content. Well-controlling interactions in generated images could yield meaningful applications such as creating realistic scenes with interacting characters. In this work we study the problems of conditioning T2I diffusion models with Human-Object Interaction (HOI) information consisting of a triplet label (person action object) and corresponding bounding boxes. We propose a pluggable interaction control model called InteractDiffusion that extends existing pre-trained T2I diffusion models to enable them being better conditioned on interactions. Specifically we tokenize the HOI information and learn their relationships via interaction embeddings. A conditioning self-attention layer is trained to map HOI tokens to visual tokens thereby conditioning the visual tokens better in existing T2I diffusion models. Our model attains the ability to control the interaction and location on existing T2I diffusion models which outperforms existing baselines by a large margin in HOI detection score as well as fidelity in FID and KID. Project page: https://jiuntian.github.io/interactdiffusion.

We present InterHandGen a novel framework that learns the generative prior of two-hand interaction. Sampling from our model yields plausible and diverse two-hand shapes in close interaction with or without an object. Our prior can be incorporated into any optimization or learning methods to reduce ambiguity in an ill-posed setup. Our key observation is that directly modeling the joint distribution of multiple instances imposes high learning complexity due to its combinatorial nature. Thus we propose to decompose the modeling of joint distribution into the modeling of factored unconditional and conditional single instance distribution. In particular we introduce a diffusion model that learns the single-hand distribution unconditional and conditional to another hand via conditioning dropout. For sampling we combine anti-penetration and classifier-free guidance to enable plausible generation. Furthermore we establish the rigorous evaluation protocol of two-hand synthesis where our method significantly outperforms baseline generative models in terms of plausibility and diversity. We also demonstrate that our diffusion prior can boost the performance of two-hand reconstruction from monocular in-the-wild images achieving new state-of-the-art accuracy.

Generative models have recently exhibited exceptional capabilities in text-to-image generation but still struggle to generate image sequences coherently. In this work we focus on a novel yet challenging task of generating a coherent image sequence based on a given storyline denoted as open-ended visual storytelling. We make the following three contributions: (i) to fulfill the task of visual storytelling we propose a learning-based auto-regressive image generation model termed as StoryGen with a novel vision-language context module that enables to generate the current frame by conditioning on the corresponding text prompt and preceding image-caption pairs; (ii) to address the data shortage of visual storytelling we collect paired image-text sequences by sourcing from online videos and open-source E-books establishing processing pipeline for constructing a large-scale dataset with diverse characters storylines and artistic styles named StorySalon; (iii) Quantitative experiments and human evaluations have validated the superiority of our StoryGen where we show it can generalize to unseen characters without any optimization and generate image sequences with coherent content and consistent character. Code dataset and models are available at https://haoningwu3639.github.io/StoryGen_Webpage/

Diffusion models have emerged as the de facto paradigm for video generation. However their reliance on web-scale data of varied quality often yields results that are visually unappealing and misaligned with the textual prompts. To tackle this problem we propose InstructVideo to instruct text-to-video diffusion models with human feedback by reward fine-tuning. InstructVideo has two key ingredients: 1) To ameliorate the cost of reward fine-tuning induced by generating through the full DDIM sampling chain we recast reward fine-tuning as editing. By leveraging the diffusion process to corrupt a sampled video InstructVideo requires only partial inference of the DDIM sampling chain reducing fine-tuning cost while improving fine-tuning efficiency. 2) To mitigate the absence of a dedicated video reward model for human preferences we repurpose established image reward models e.g. HPSv2. To this end we propose Segmental Video Reward a mechanism to provide reward signals based on segmental sparse sampling and Temporally Attenuated Reward a method that mitigates temporal modeling degradation during fine-tuning. Extensive experiments both qualitative and quantitative validate the practicality and efficacy of using image reward models in InstructVideo significantly enhancing the visual quality of generated videos without compromising generalization capabilities. Code and models can be accessed through our project page https://instructvideo.github.io/.

This paper presents Instruct-Imagen a model that tackles heterogeneous image generation tasks and generalizes across unseen tasks. We introduce multi-modal instruction for image generation a task representation articulating a range of generation intents with precision. It uses natural language to amalgamate disparate modalities (e.g. text edge style subject etc.) such that abundant generation intents can be standardized in a uniform format. We then build Instruct-Imagen by fine-tuning a pre-trained text-to-image diffusion model with two stages. First we adapt the model using the retrieval-augmented training to enhance model's capabilities to ground its generation on external multi-modal context. Subsequently we fine-tune the adapted model on diverse image generation tasks that requires vision-language understanding (e.g. subject-driven generation etc.) each paired with a multi-modal instruction encapsulating the task's essence. Human evaluation on various image generation datasets reveals that Instruct-Imagen matches or surpasses prior task-specific models in-domain and demonstrates promising generalization to unseen and more complex tasks. Our evaluation suite will be made publicly available.

This paper proposes Instruct 4D-to-4D that achieves 4D awareness and spatial-temporal consistency for 2D diffusion models to generate high-quality instruction-guided dynamic scene editing results. Traditional applications of 2D diffusion models in dynamic scene editing often result in inconsistency primarily due to their inherent frame-by-frame editing methodology. Addressing the complexities of extending instruction-guided editing to 4D our key insight is to treat a 4D scene as a pseudo-3D scene decoupled into two sub-problems: achieving temporal consistency in video editing and applying these edits to the pseudo-3D scene. Following this we first enhance the Instruct-Pix2Pix (IP2P) model with an anchor-aware attention module for batch processing and consistent editing. Additionally we integrate optical flow-guided appearance propagation in a sliding window fashion for more precise frame-to-frame editing and incorporate depth-based projection to manage the extensive data of pseudo-3D scenes followed by iterative editing to achieve convergence. We extensively evaluate our approach in various scenes and editing instructions and demonstrate that it achieves spatially and temporally consistent editing results with significantly enhanced detail and sharpness over the prior art. Notably Instruct 4D-to-4D is general and applicable to both monocular and challenging multi-camera scenes.

Text-to-image diffusion models produce high quality images but do not offer control over individual instances in the image. We introduce InstanceDiffusion that adds precise instance-level control to text-to-image diffusion models. InstanceDiffusion supports free-form language conditions per instance and allows flexible ways to specify instance locations such as simple single points scribbles bounding boxes or intricate instance segmentation masks and combinations thereof. We propose three major changes to text-to-image models that enable precise instance-level control. Our UniFusion block enables instance-level conditions for text-to-image models the ScaleU block improves image fidelity and our Multi-instance Sampler improves generations for multiple instances. InstanceDiffusion significantly surpasses specialized state-of-the-art models for each location condition. Notably on the COCO dataset we outperform previous state-of-the-art by 20.4% AP50box for box inputs and 25.4% IoU for mask inputs.

In this paper we present a novel paradigm to enhance the ability of object detector e.g. expanding categories or improving detection performance by training on syn- thetic dataset generated from diffusion models. Specifically we integrate an instance-level grounding head into a pre- trained generative diffusion model to augment it with the ability of localising instances in the generated images. The grounding head is trained to align the text embedding of category names with the regional visual feature of the diffusion model using supervision from an off-the-shelf object detector and a novel self-training scheme on (novel) categories not covered by the detector. We conduct thorough experiments to show that this enhanced version of diffusion model termed as InstaGen can serve as a data synthesizer to enhance object detectors by training on its generated samples demonstrating superior performance over existing state-of-the-art methods in open-vocabulary (+4.5 AP) and data-sparse (+1.2 ? 5.2 AP) scenarios.

Recent strides in the development of diffusion models exemplified by advancements such as Stable Diffusion have underscored their remarkable prowess in generating visually compelling images. However the imperative of achieving a seamless alignment between the generated image and the provided prompt persists as a formidable challenge. This paper traces the root of these difficulties to invalid initial noise and proposes a solution in the form of Initial Noise Optimization (InitNO) a paradigm that refines this noise. Considering text prompts not all random noises are effective in synthesizing semantically-faithful images. We design the cross-attention response score and the self-attention conflict score to evaluate the initial noise bifurcating the initial latent space into valid and invalid sectors. A strategically crafted noise optimization pipeline is developed to guide the initial noise towards valid regions. Our method validated through rigorous experimentation shows a commendable proficiency in generating images in strict accordance with text prompts. Our code is available at https://github.com/xiefan-guo/initno.

To alleviate the utility degradation of deep learning image classification with differential privacy (DP) employing extra public data or pre-trained models has been widely explored. Recently the use of in-distribution public data has been investigated where tiny subsets of datasets are released publicly. In this paper we investigate a framework that leverages recent diffusion models to amplify the information of public data. Subsequently we identify data diversity and generalization gap between public and private data as critical factors addressing the limited public data. While assuming 4% of training data as public our method achieves 85.48% on CIFAR-10 with a privacy budget of ?=2 without employing extra public data for training.

We introduce in-context matting a novel task setting of image matting. Given a reference image of a certain foreground and guided priors such as points scribbles and masks in-context matting enables automatic alpha estimation on a batch of target images of the same foreground category without additional auxiliary input. This setting marries good performance in auxiliary input-based matting and ease of use in automatic matting which finds a good trade-off between customization and automation. To overcome the key challenge of accurate foreground matching we introduce IconMatting an in-context matting model built upon a pre-trained text-to-image diffusion model. Conditioned on inter- and intra-similarity matching IconMatting can make full use of reference context to generate accurate target alpha mattes. To benchmark the task we also introduce a novel testing dataset ICM-57 covering 57 groups of real-world images. Quantitative and qualitative results on the ICM-57 testing set show that IconMatting rivals the accuracy of trimap-based matting while retaining the automation level akin to automatic matting. Code is available at https://github.com/tiny-smart/in-context-matting.

Diffusion models emerging as powerful deep generative tools excel in various applications. They operate through a two-steps process: introducing noise into training samples and then employing a model to convert random noise into new samples (e.g. images). However their remarkable generative performance is hindered by slow training and sampling. This is due to the necessity of tracking extensive forward and reverse diffusion trajectories and employing a large model with numerous parameters across multiple timesteps (i.e. noise levels). To tackle these challenges we present a multi-stage framework inspired by our empirical findings. These observations indicate the advantages of employing distinct parameters tailored to each timestep while retaining universal parameters shared across all time steps. Our approach involves segmenting the time interval into multiple stages where we employ custom multi-decoder U-net architecture that blends time-dependent models with a universally shared encoder. Our framework enables the efficient distribution of computational resources and mitigates inter-stage interference which substantially improves training efficiency. Extensive numerical experiments affirm the effectiveness of our framework showcasing significant training and sampling efficiency enhancements on three state-of-the-art diffusion models including large-scale latent diffusion models. Furthermore our ablation studies illustrate the impact of two important components in our framework: (i) a novel timestep clustering algorithm for stage division and (ii) an innovative multi-decoder U-net architecture seamlessly integrating universal and customized hyperparameters.

Recent advancements in video modeling extensively rely on optical flow to represent the relationships across frames but this approach often lacks efficiency and fails to model the probability of the intrinsic motion of objects. In addition conventional encoder-decoder frameworks in video processing focus on modeling the correlation in the encoder leading to limited generative capabilities and redundant intermediate representations. To address these challenges this paper proposes a novel Implicit Motion Function (IMF) method. Our approach utilizes a low-dimensional latent token as the implicit representation along with the use of cross-attention to implicitly model the correlation between frames. This enables the implicit modeling of temporal correlations and understanding of object motions. Our method not only improves sparsity and efficiency in representation but also explores the generative capabilities of the decoder by integrating correlation modeling within it. The IMF framework facilitates video editing and other generative tasks by allowing the direct manipulation of latent tokens. We validate the effectiveness of IMF through extensive experiments on multiple video tasks demonstrating superior performance in terms of reconstructed video quality compression efficiency and generation ability.

We establish rigorous benchmarks for visual perception robustness. Synthetic images such as ImageNet-C ImageNet-9 and Stylized ImageNet provide specific type of evaluation over synthetic corruptions backgrounds and textures yet those robustness benchmarks are restricted in specified variations and have low synthetic quality. In this work we introduce generative model as a data source for synthesizing hard images that benchmark deep models' robustness. Leveraging diffusion models we are able to generate images with more diversified backgrounds textures and materials than any prior work where we term this benchmark as ImageNet-D. Experimental results show that ImageNet-D results in a significant accuracy drop to a range of vision models from the standard ResNet visual classifier to the latest foundation models like CLIP and MiniGPT-4 significantly reducing their accuracy by up to 60%. Our work suggests that diffusion models can be an effective source to test vision models. The code and dataset are available at https://github.com/chenshuang-zhang/imagenet_d.

Training deep neural networks has become a common approach for addressing image restoration problems. An alternative for training a "task-specific" network for each observation model is to use pretrained deep denoisers for imposing only the signal's prior within iterative algorithms without additional training. Recently a sampling-based variant of this approach has become popular with the rise of diffusion/score-based generative models. Using denoisers for general purpose restoration requires guiding the iterations to ensure agreement of the signal with the observations. In low-noise settings guidance that is based on back-projection (BP) has been shown to be a promising strategy (used recently also under the names "pseudoinverse" or "range/-space" guidance). However the presence of noise in the observations hinders the gains from this approach. In this paper we propose a novel guidance technique based on preconditioning that allows traversing from BP-based guidance to least squares based guidance along the restoration scheme. The proposed approach is robust to noise while still having much simpler implementation than alternative methods (e.g. it does not require SVD or a large number of iterations). We use it within both an optimization scheme and a sampling-based scheme and demonstrate its advantages over existing methods for image deblurring and super-resolution.

Diffusion models have shown an impressive ability to model complex data distributions with several key advantages over GANs such as stable training better coverage of the training distribution's modes and the ability to solve inverse problems without extra training. However most diffusion models learn the distribution of fixed-resolution images. We propose to learn the distribution of continuous images by training diffusion models on image neural fields which can be rendered at any resolution and show its advantages over fixed-resolution models. To achieve this a key challenge is to obtain a latent space that represents photorealistic image neural fields. We propose a simple and effective method inspired by several recent techniques but with key changes to make the image neural fields photorealistic. Our method can be used to convert existing latent diffusion autoencoders into image neural field autoencoders. We show that image neural field diffusion models can be trained using mixed-resolution image datasets outperform fixed-resolution diffusion models followed by super-resolution models and can solve inverse problems with conditions applied at different scales efficiently.

Image deblurring aims to remove undesired blurs from an image captured in a dynamic scene. Much research has been dedicated to improving deblurring performance through model architectural designs. However there is little work on data augmentation for image deblurring. Since continuous motion causes blurred artifacts during image exposure we aspire to develop a groundbreaking blur augmentation method to generate diverse blurred images by simulating motion trajectories in a continuous space. This paper proposes Implicit Diffusion-based reBLurring AUgmentation (ID-Blau) utilizing a sharp image paired with a controllable blur condition map to produce a corresponding blurred image. We parameterize the blur patterns of a blurred image with their orientations and magnitudes as a pixel-wise blur condition map to simulate motion trajectories and implicitly represent them in a continuous space. By sampling diverse blur conditions ID-Blau can generate various blurred images unseen in the training set. Experimental results demonstrate that ID-Blau can produce realistic blurred images for training and thus significantly improve performance for state-of-the-art deblurring models. The source code is available at https://github.com/plusgood-steven/ID-Blau.