CVPR 2024 Papers

We present "SemCity" a 3D diffusion model for semantic scene generation in real-world outdoor environments. Most 3D diffusion models focus on generating a single object synthetic indoor scenes or synthetic outdoor scenes while the generation of real-world outdoor scenes is rarely addressed. In this paper we concentrate on generating a real-outdoor scene through learning a diffusion model on a real-world outdoor dataset. In contrast to synthetic data real-outdoor datasets often contain more empty spaces due to sensor limitations causing challenges in learning real-outdoor distributions. To address this issue we exploit a triplane representation as a proxy form of scene distributions to be learned by our diffusion model. Furthermore we propose a triplane manipulation that integrates seamlessly with our triplane diffusion model. The manipulation improves our diffusion model's applicability in a variety of downstream tasks related to outdoor scene generation such as scene inpainting scene outpainting and semantic scene completion refinements. In experimental results we demonstrate that our triplane diffusion model shows meaningful generation results compared with existing work in a real-outdoor dataset SemanticKITTI. We also show our triplane manipulation facilitates seamlessly adding removing or modifying objects within a scene. Further it also enables the expansion of scenes toward a city-level scale. Finally we evaluate our method on semantic scene completion refinements where our diffusion model enhances predictions of semantic scene completion networks by learning scene distribution. Our code is available at https://github.com/zoomin-lee/SemCity.

Text-to-image generation has witnessed significant progress with the advent of diffusion models. Despite the ability to generate photorealistic images current text-to-image diffusion models still often struggle to accurately interpret and follow complex input text prompts. In contrast to existing models that aim to generate images only with their best effort we introduce Self-correcting LLM-controlled Diffusion (SLD). SLD is a framework that generates an image from the input prompt assesses its alignment with the prompt and performs self-corrections on the inaccuracies in the generated image. Steered by an LLM controller SLD turns text-to-image generation into an iterative closed-loop process ensuring correctness in the resulting image. SLD is not only training-free but can also be seamlessly integrated with diffusion models behind API access such as DALL-E 3 to further boost the performance of state-of-the-art diffusion models. Experimental results show that our approach can rectify a majority of incorrect generations particularly in generative numeracy attribute binding and spatial relationships. Furthermore by simply adjusting the instructions to the LLM SLD can perform image editing tasks bridging the gap between text-to-image generation and image editing pipelines. Our code is available at: https://self-correcting-llm-diffusion.github.io.

Diffusion-based models have gained significant popularity for text-to-image generation due to their exceptional image-generation capabilities. A risk with these models is the potential generation of inappropriate content such as biased or harmful images. However the underlying reasons for generating such undesired content from the perspective of the diffusion model's internal representation remain unclear. Previous work interprets vectors in an interpretable latent space of diffusion models as semantic concepts. However existing approaches cannot discover directions for arbitrary concepts such as those related to inappropriate concepts. In this work we propose a novel self-supervised approach to find interpretable latent directions for a given concept. With the discovered vectors we further propose a simple approach to mitigate inappropriate generation. Extensive experiments have been conducted to verify the effectiveness of our mitigation approach namely for fair generation safe generation and responsible text-enhancing generation. Project page: https://interpretdiffusion.github.io.

Artifact-free super-resolution (SR) aims to translate low-resolution images into their high-resolution counterparts with a strict integrity of the original content eliminating any distortions or synthetic details. While traditional diffusion-based SR techniques have demonstrated remarkable abilities to enhance image detail they are prone to artifact introduction during iterative procedures. Such artifacts ranging from trivial noise to unauthentic textures deviate from the true structure of the source image thus challenging the integrity of the super-resolution process. In this work we propose Self-Adaptive Reality-Guided Diffusion (SARGD) a training-free method that delves into the latent space to effectively identify and mitigate the propagation of artifacts. Our SARGD begins by using an artifact detector to identify implausible pixels creating a binary mask that highlights artifacts. Following this the Reality Guidance Refinement (RGR) process refines artifacts by integrating this mask with realistic latent representations improving alignment with the original image. Nonetheless initial realistic-latent representations from lower-quality images result in over-smoothing in the final output. To address this we introduce a Self-Adaptive Guidance (SAG) mechanism. It dynamically computes a reality score enhancing the sharpness of the realistic latent. These alternating mechanisms collectively achieve artifact-free super-resolution. Extensive experiments demonstrate the superiority of our method delivering detailed artifact-free high-resolution images while reducing sampling steps by 2X. We release our code at https://github.com/ProAirVerse/Self-Adaptive-Guidance-Diffusion.git.

Universal image restoration is a practical and potential computer vision task for real-world applications. The main challenge of this task is handling the different degradation distributions at once. Existing methods mainly utilize task-specific conditions (e.g. prompt) to guide the model to learn different distributions separately named multi-partite mapping. However it is not suitable for universal model learning as it ignores the shared information between different tasks. In this work we propose an advanced selective hourglass mapping strategy based on diffusion model termed DiffUIR. Two novel considerations make our DiffUIR non-trivial. Firstly we equip the model with strong condition guidance to obtain accurate generation direction of diffusion model (selective). More importantly DiffUIR integrates a flexible shared distribution term (SDT) into the diffusion algorithm elegantly and naturally which gradually maps different distributions into a shared one. In the reverse process combined with SDT and strong condition guidance DiffUIR iteratively guides the shared distribution to the task-specific distribution with high image quality (hourglass). Without bells and whistles by only modifying the mapping strategy we achieve state-of-the-art performance on five image restoration tasks 22 benchmarks in the universal setting and zero-shot generalization setting. Surprisingly by only using a lightweight model (only 0.89M) we could achieve outstanding performance. The source code and pre-trained models are available at https://github.com/iSEE-Laboratory/DiffUIR

Owe to the powerful generative priors the pre-trained text-to-image (T2I) diffusion models have become increasingly popular in solving the real-world image super-resolution problem. However as a consequence of the heavy quality degradation of input low-resolution (LR) images the destruction of local structures can lead to ambiguous image semantics. As a result the content of reproduced high-resolution image may have semantic errors deteriorating the super-resolution performance. To address this issue we present a semantics-aware approach to better preserve the semantic fidelity of generative real-world image super-resolution. First we train a degradation-aware prompt extractor which can generate accurate soft and hard semantic prompts even under strong degradation. The hard semantic prompts refer to the image tags aiming to enhance the local perception ability of the T2I model while the soft semantic prompts compensate for the hard ones to provide additional representation information. These semantic prompts encourage the T2I model to generate detailed and semantically accurate results. Furthermore during the inference process we integrate the LR images into the initial sampling noise to mitigate the diffusion model's tendency to generate excessive random details. The experiments show that our method can reproduce more realistic image details and hold better the semantics. The source code of our method can be found at https://github.com/cswry/SeeSR

Recent works on text-to-3d generation show that using only 2D diffusion supervision for 3D generation tends to produce results with inconsistent appearances (e.g. faces on the back view) and inaccurate shapes (e.g. animals with extra legs). Existing methods mainly address this issue by retraining diffusion models with images rendered from 3D data to ensure multi-view consistency while struggling to balance 2D generation quality with 3D consistency. In this paper we present a new framework Sculpt3D that equips the current pipeline with explicit injection of 3D priors from retrieved reference objects without re-training the 2D diffusion model. Specifically we demonstrate that high-quality and diverse 3D geometry can be guaranteed by keypoints supervision through a sparse ray sampling approach. Moreover to ensure accurate appearances of different views we further modulate the output of the 2D diffusion model to the correct patterns of the template views without altering the generated object's style. These two decoupled designs effectively harness 3D information from reference objects to generate 3D objects while preserving the generation quality of the 2D diffusion model. Extensive experiments show our method can largely improve the multi-view consistency while retaining fidelity and diversity.

We present Score-Guided Human Mesh Recovery (ScoreHMR) an approach for solving inverse problems for 3D human pose and shape reconstruction. These inverse problems involve fitting a human body model to image observations traditionally solved through optimization techniques. ScoreHMR mimics model fitting approaches but alignment with the image observation is achieved through score guidance in the latent space of a diffusion model. The diffusion model is trained to capture the conditional distribution of the human model parameters given an input image. By guiding its denoising process with a task-specific score ScoreHMR effectively solves inverse problems for various applications without the need for retraining the task-agnostic diffusion model. We evaluate our approach on three settings/applications. These are: (i) single-frame model fitting; (ii) reconstruction from multiple uncalibrated views; (iii) reconstructing humans in video sequences. ScoreHMR consistently outperforms all optimization baselines on popular benchmarks across all settings. We make our code and models available on the project website: https://statho.github.io/ScoreHMR.

We propose SceneTex a novel method for effectively generating high-quality and style-consistent textures for indoor scenes using depth-to-image diffusion priors. Unlike previous methods that either iteratively warp 2D views onto a mesh surface or distillate diffusion latent features without accurate geometric and style cues SceneTex formulates the texture synthesis task as an optimization problem in the RGB space where style and geometry consistency are properly reflected. At its core SceneTex proposes a multiresolution texture field to implicitly encode the mesh appearance. We optimize the target texture via a score-distillation-based objective function in respective RGB renderings. To further secure the style consistency across views we introduce a cross-attention decoder to predict the RGB values by cross-attending to the pre-sampled reference locations in each instance. SceneTex enables various and accurate texture synthesis for 3D-FRONT scenes demonstrating significant improvements in visual quality and prompt fidelity over the prior texture generation methods.

Computer vision techniques play a central role in the perception stack of autonomous vehicles. Such methods are employed to perceive the vehicle surroundings given sensor data. 3D LiDAR sensors are commonly used to collect sparse 3D point clouds from the scene. However compared to human perception such systems struggle to deduce the unseen parts of the scene given those sparse point clouds. In this matter the scene completion task aims at predicting the gaps in the LiDAR measurements to achieve a more complete scene representation. Given the promising results of recent diffusion models as generative models for images we propose extending them to achieve scene completion from a single 3D LiDAR scan. Previous works used diffusion models over range images extracted from LiDAR data directly applying image-based diffusion methods. Distinctly we propose to directly operate on the points reformulating the noising and denoising diffusion process such that it can efficiently work at scene scale. Together with our approach we propose a regularization loss to stabilize the noise predicted during the denoising process. Our experimental evaluation shows that our method can complete the scene given a single LiDAR scan as input producing a scene with more details compared to state-of-the-art scene completion methods. We believe that our proposed diffusion process formulation can support further research in diffusion models applied to scene-scale point cloud data.

In recent years semantic segmentation has become a pivotal tool in processing and interpreting satellite imagery. Yet a prevalent limitation of supervised learning techniques remains the need for extensive manual annotations by experts. In this work we explore the potential of generative image diffusion to address the scarcity of annotated data in earth observation tasks. The main idea is to learn the joint data manifold of images and labels leveraging recent advancements in denoising diffusion probabilistic models. To the best of our knowledge we are the first to generate both images and corresponding masks for satellite segmentation. We find that the obtained pairs not only display high quality in fine-scale features but also ensure a wide sampling diversity. Both aspects are crucial for earth observation data where semantic classes can vary severely in scale and occurrence frequency. We employ the novel data instances for downstream segmentation as a form of data augmentation. In our experiments we provide comparisons to prior works based on discriminative diffusion models or GANs. We demonstrate that integrating generated samples yields significant quantitative improvements for satellite semantic segmentation -- both compared to baselines and when training only on the original data.

Recently text-guided scalable vector graphics (SVGs) synthesis has shown promise in domains such as iconography and sketch. However existing text-to-SVG generation methods lack editability and struggle with visual quality and result diversity. To address these limitations we propose a novel text-guided vector graphics synthesis method called SVGDreamer. SVGDreamer incorporates a semantic-driven image vectorization (SIVE) process that enables the decomposition of synthesis into foreground objects and background thereby enhancing editability. Specifically the SIVE process introduces attention-based primitive control and an attention-mask loss function for effective control and manipulation of individual elements. Additionally we propose a Vectorized Particle-based Score Distillation (VPSD) approach to address issues of shape over-smoothing color over-saturation limited diversity and slow convergence of the existing text-to-SVG generation methods by modeling SVGs as distributions of control points and colors. Furthermore VPSD leverages a reward model to re-weight vector particles which improves aesthetic appeal and accelerates convergence. Extensive experiments are conducted to validate the effectiveness of SVGDreamer demonstrating its superiority over baseline methods in terms of editability visual quality and diversity. Project page: \href https://ximinng.github.io/SVGDreamer-project/ https://ximinng.github.io/SVGDreamer-project/

Efficiently representing and reconstructing the 3D geometry of biological trees remains a challenging problem in computer vision and graphics. We propose a novel approach for generating realistic tree models from single-view photographs. We cast the 3D information inference problem to a semantic voxel diffusion process which converts an input image of a tree to a novel Semantic Voxel Structure (SVS) in 3D space. The SVS encodes the geometric appearance and semantic structural information (e.g. classifying trunks branches and leaves) which retains the intricate internal tree features. Tailored to the SVS we present SVDTree a new hybrid tree modeling approach by combining structure-oriented branch reconstruction and self-organization-based foliage reconstruction. We validate SVDTree by using images from both synthetic and real trees. The comparison results show that our approach can better preserve tree details and achieve more realistic and accurate reconstruction results than previous methods.

We present SPAD a novel approach for creating consistent multi-view images from text prompts or single images. To enable multi-view generation we repurpose a pretrained 2D diffusion model by extending its self-attention layers with cross-view interactions and fine-tune it on a high quality subset of Objaverse. We find that a naive extension of the self-attention proposed in prior work (e.g. MVDream) leads to content copying between views. Therefore we explicitly constrain the cross-view attention based on epipolar geometry. To further enhance 3D consistency we utilize Pl ?ucker coordinates derived from camera rays and inject them as positional encoding. This enables SPAD to reason over spatial proximity in 3D well. Compared to concurrent works that can only generate views at fixed azimuth and elevation (e.g. MVDream SyncDreamer) SPAD offers full camera control and achieves state-of-the-art results in novel view synthesis on unseen objects from the Objaverse and Google Scanned Objects datasets. Finally we demonstrate that text-to-3D generation using SPAD prevents the multi-face Janus issue.

We introduce SODA a self-supervised diffusion model designed for representation learning. The model incorporates an image encoder which distills a source view into a compact representation that in turn guides the generation of related novel views. We show that by imposing a tight bottleneck between the encoder and a denoising decoder and leveraging novel view synthesis as a self-supervised objective we can turn diffusion models into strong representation learners capable of capturing visual semantics in an unsupervised manner. To the best of our knowledge SODA is the first diffusion model to succeed at ImageNet linear-probe classification and at the same time it accomplishes reconstruction editing and synthesis tasks across a wide range of datasets. Further investigation reveals the disentangled nature of its emergent latent space that serves as an effective interface to control and manipulate the produced images. All in all we aim to shed light on the exciting and promising potential of diffusion models not only for image generation but also for learning rich and robust representations. See our website at soda-diffusion.github.io.

While AI-generated content has garnered significant attention achieving photo-realistic video synthesis remains a formidable challenge. Despite the promising advances in diffusion models for video generation quality the complex model architecture and substantial computational demands for both training and inference create a significant gap between these models and real-world applications. This paper presents SNED a superposition network architecture search method for efficient video diffusion model. Our method employs a supernet training paradigm that targets various model cost and resolution options using a weight-sharing method. Moreover we propose the supernet training sampling warm-up for fast training optimization. To showcase the flexibility of our method we conduct experiments involving both pixel-space and latent-space video diffusion models. The results demonstrate that our framework consistently produces comparable results across different model options with high efficiency. According to the experiment for the pixel-space video diffusion model we can achieve consistent video generation results simultaneously across 64 x 64 to 256 x 256 resolutions with a large range of model sizes from 640M to 1.6B number of parameters for pixel-space video diffusion models.

Advances in image diffusion models have recently led to notable improvements in the generation of high-quality images. In combination with Neural Radiance Fields (NeRFs) they enabled new opportunities in 3D generation. However most generative 3D approaches are object-centric and applying them to editing existing photorealistic scenes is not trivial. We propose SIGNeRF a novel approach for fast and controllable NeRF scene editing and scene-integrated object generation. A new generative update strategy ensures 3D consistency across the edited images without requiring iterative optimization. We find that depth-conditioned diffusion models inherently possess the capability to generate 3D consistent views by requesting a grid of images instead of single views. Based on these insights we introduce a multi-view reference sheet of modified images. Our method updates an image collection consistently based on the reference sheet and refines the original NeRF with the newly generated image set in one go. By exploiting the depth conditioning mechanism of the image diffusion model we gain fine control over the spatial location of the edit and enforce shape guidance by a selected region or an external mesh.

In the field of class incremental learning (CIL) generative replay has become increasingly prominent as a method to mitigate the catastrophic forgetting alongside the continuous improvements in generative models. However its application in class incremental object detection (CIOD) has been significantly limited primarily due to the complexities of scenes involving multiple labels. In this paper we propose a novel approach called stable diffusion deep generative replay (SDDGR) for CIOD. Our method utilizes a diffusion-based generative model with pre-trained text-to-image diffusion networks to generate realistic and diverse synthetic images. SDDGR incorporates an iterative refinement strategy to produce high-quality images encompassing old classes. Additionally we adopt an L2 knowledge distillation technique to improve the retention of prior knowledge in synthetic images. Furthermore our approach includes pseudo-labeling for old objects within new task images preventing misclassification as background elements. Extensive experiments on the COCO 2017 dataset demonstrate that SDDGR significantly outperforms existing algorithms achieving a new state-of-the-art in various CIOD scenarios.