While diffusion models have significantly advanced the quality of image generation their capability to accurately and coherently render text within these images remains a substantial challenge. Conventional diffusion-based methods for scene text generation are typically limited by their reliance on an intermediate layout output. This dependency often results in a constrained diversity of text styles and fonts an inherent limitation stemming from the deterministic nature of the layout generation phase. To address these challenges this paper introduces SceneTextGen a novel diffusion-based model specifically designed to circumvent the need for a predefined layout stage. By doing so SceneTextGen facilitates a more natural and varied representation of text. The novelty of SceneTextGen lies in its integration of three key components: a character-level encoder for capturing detailed typographic properties coupled with a character-level instance segmentation model and a word-level spotting model to address the issues of unwanted text generation and minor character inaccuracies. We validate the performance of our method by demonstrating improved character recognition rates on generated images across different public visual text datasets in comparison to both standard diffusion based methods and text specific methods.
论文检索
输入标题、作者或关键词，从 9,256 篇学术成果中精准定位
Diffusion Reflectance Map: Single-Image Stochastic Inverse Rendering of Illumination and Reflectance
PDF ↗Reflectance bounds the frequency spectrum of illumination in the object appearance. In this paper we introduce the first stochastic inverse rendering method which recovers the attenuated frequency spectrum of an illumination jointly with the reflectance of an object of known geometry from a single image. Our key idea is to solve this blind inverse problem in the reflectance map an appearance representation invariant to the underlying geometry by learning to reverse the image formation with a novel diffusion model which we refer to as the Diffusion Reflectance Map Network (DRMNet). Given an observed reflectance map converted and completed from the single input image DRMNet generates a reflectance map corresponding to a perfect mirror sphere while jointly estimating the reflectance. The forward process can be understood as gradually filtering a natural illumination with lower and lower frequency reflectance and additive Gaussian noise. DRMNet learns to invert this process with two subnetworks IllNet and RefNet which work in concert towards this joint estimation. The network is trained on an extensive synthetic dataset and is demonstrated to generalize to real images showing state-of-the-art accuracy on established datasets.
Diffusion models (DMs) excel in photo-realistic image synthesis but their adaptation to LiDAR scene generation poses a substantial hurdle. This is primarily because DMs operating in the point space struggle to preserve the curve-like patterns and 3D geometry of LiDAR scenes which consumes much of their representation power. In this paper we propose LiDAR Diffusion Models (LiDMs) to generate LiDAR-realistic scenes from a latent space tailored to capture the realism of LiDAR scenes by incorporating geometric priors into the learning pipeline. Our method targets three major desiderata: pattern realism geometry realism and object realism. Specifically we introduce curve-wise compression to simulate real-world LiDAR patterns point-wise coordinate supervision to learn scene geometry and patch-wise encoding for a full 3D object context. With these three core designs our method achieves competitive performance on unconditional LiDAR generation in 64-beam scenario and state of the art on conditional LiDAR generation while maintaining high efficiency compared to point-based DMs (up to 107xfaster). Furthermore by compressing LiDAR scenes into a latent space we enable the controllability of DMs with various conditions such as semantic maps camera views and text prompts. Our code and pretrained weights are available at https://github.com/hancyran/LiDAR-Diffusion.
CGI-DM: Digital Copyright Authentication for Diffusion Models via Contrasting Gradient Inversion
PDF ↗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.
Diffusion probabilistic models (DPMs) have shown remarkable performance in high-resolution image synthesis but their sampling efficiency is still to be desired due to the typically large number of sampling steps. Recent advancements in high-order numerical ODE solvers for DPMs have enabled the generation of high-quality images with much fewer sampling steps. While this is a significant development most sampling methods still employ uniform time steps which is not optimal when using a small number of steps. To address this issue we propose a general framework for designing an optimization problem that seeks more appropriate time steps for a specific numerical ODE solver for DPMs. This optimization problem aims to minimize the distance between the ground-truth solution to the ODE and an approximate solution corresponding to the numerical solver. It can be efficiently solved using the constrained trust region method taking less than 15 seconds. Our extensive experiments on both unconditional and conditional sampling using pixel- and latent-space DPMs demonstrate that when combined with the state-of-the-art sampling method UniPC our optimized time steps significantly improve image generation performance in terms of FID scores for datasets such as CIFAR-10 and ImageNet compared to using uniform time steps.
We introduce pix2gestalt a framework for zero-shot amodal segmentation which learns to estimate the shape and appearance of whole objects that are only partially visible behind occlusions. By capitalizing on large-scale diffusion models and transferring their representations to this task we learn a conditional diffusion model for reconstructing whole objects in challenging zero-shot cases including examples that break natural and physical priors such as art. As training data we use a synthetically curated dataset containing occluded objects paired with their whole counterparts. Experiments show that our approach outperforms supervised baselines on established benchmarks. Our model can furthermore be used to significantly improve the performance of existing object recognition and 3D reconstruction methods in the presence of occlusions.
We present a scene representation that brings vision and touch into a shared 3D space which we call a tactile-augmented radiance field. This representation capitalizes on two key insights: (i) ubiquitous vision-based touch sensors are built on perspective cameras and (ii) visually and structurally similar regions of a scene share the same tactile features. We use these insights to train a conditional diffusion model that provided with an RGB image and a depth map rendered from a neural radiance field generates its corresponding tactile "image". To train this diffusion model we collect the largest collection of spatially-aligned visual and tactile data. Through qualitative and quantitative experiments we demonstrate the accuracy of our cross-modal generative model and the utility of collected and rendered visual-tactile pairs across a range of downstream tasks. Project page: https://dou-yiming.github.io/TaRF
In the realm of image composition generating realistic shadow for the inserted foreground remains a formidable challenge. Previous works have developed image-to-image translation models which are trained on paired training data. However they are struggling to generate shadows with accurate shapes and intensities hindered by data scarcity and inherent task complexity. In this paper we resort to foundation model with rich prior knowledge of natural shadow images. Specifically we first adapt ControlNet to our task and then propose intensity modulation modules to improve the shadow intensity. Moreover we extend the small-scale DESOBA dataset to DESOBAv2 using a novel data acquisition pipeline. Experimental results on both DESOBA and DESOBAv2 datasets as well as real composite images demonstrate the superior capability of our model for shadow generation task. The dataset code and model are released at https://github.com/bcmi/Object-Shadow-Generation-Dataset-DESOBAv2.
Carve3D: Improving Multi-view Reconstruction Consistency for Diffusion Models with RL Finetuning
PDF ↗Multi-view diffusion models obtained by applying Supervised Finetuning (SFT) to text-to-image diffusion models have driven recent breakthroughs in text-to-3D research. However due to the limited size and quality of existing 3D datasets they still suffer from multi-view inconsistencies and Neural Radiance Field (NeRF) reconstruction artifacts. We argue that multi-view diffusion models can benefit from further Reinforcement Learning Finetuning (RLFT) which allows models to learn from the data generated by themselves and improve beyond their dataset limitations during SFT. To this end we introduce Carve3D an improved RLFT algorithm coupled with a novel Multi-view Reconstruction Consistency (MRC) metric to enhance the consistency of multi-view diffusion models. To measure the MRC metric on a set of multi-view images we compare them with their corresponding NeRF renderings at the same camera viewpoints. The resulting model which we denote as Carve3DM demonstrates superior multi-view consistency and NeRF reconstruction quality than existing models. Our results suggest that pairing SFT with Carve3D's RLFT is essential for developing multi-view-consistent diffusion models mirroring the standard Large Language Model (LLM) alignment pipeline. Our code training and testing data and video results are available at: https://desaixie.github.io/carve-3d.
In the current era of generative AI breakthroughs generating panoramic scenes from a single input image remains a key challenge. Most existing methods use diffusion-based iterative or simultaneous multi-view inpainting. However the lack of global scene layout priors leads to subpar outputs with duplicated objects (e.g. multiple beds in a bedroom) or requires time-consuming human text inputs for each view. We propose L-MAGIC a novel method leveraging large language models for guidance while diffusing multiple coherent views of 360 degree panoramic scenes. L-MAGIC harnesses pre-trained diffusion and language models without fine-tuning ensuring zero-shot performance. The output quality is further enhanced by super-resolution and multi-view fusion techniques. Extensive experiments demonstrate that the resulting panoramic scenes feature better scene layouts and perspective view rendering quality compared to related works with >70% preference in human evaluations. Combined with conditional diffusion models L-MAGIC can accept various input modalities including but not limited to text depth maps sketches and colored scripts. Applying depth estimation further enables 3D point cloud generation and dynamic scene exploration with fluid camera motion.
Recent advancements in diffusion-based models have demonstrated significant success in generating images from text. However video editing models have not yet reached the same level of visual quality and user control. To address this we introduce RAVE a zero-shot video editing method that leverages pre-trained text-to-image diffusion models without additional training. RAVE takes an input video and a text prompt to produce high-quality videos while preserving the original motion and semantic structure. It employs a novel noise shuffling strategy leveraging spatio-temporal interactions between frames to produce temporally consistent videos faster than existing methods. It is also efficient in terms of memory requirements allowing it to handle longer videos. RAVE is capable of a wide range of edits from local attribute modifications to shape transformations. In order to demonstrate the versatility of RAVE we create a comprehensive video evaluation dataset ranging from object-focused scenes to complex human activities like dancing and typing and dynamic scenes featuring swimming fish and boats. Our qualitative and quantitative experiments highlight the effectiveness of RAVE in diverse video editing scenarios compared to existing methods. Our code dataset and videos can be found in \href https://rave-video-edit.github.io/.
Sign Languages (SL) serve as the primary mode of communication for the Deaf and Hard of Hearing communities. Deep learning methods for SL recognition and translation have achieved promising results. However Sign Language Production (SLP) poses a challenge as the generated motions must be realistic and have precise semantic meaning. Most SLP methods rely on 2D data which hinders their realism. In this work a diffusion-based SLP model is trained on a curated large-scale dataset of 4D signing avatars and their corresponding text transcripts. The proposed method can generate dynamic sequences of 3D avatars from an unconstrained domain of discourse using a diffusion process formed on a novel and anatomically informed graph neural network defined on the SMPL-X body skeleton. Through quantitative and qualitative experiments we show that the proposed method considerably outperforms previous methods of SLP. This work makes an important step towards realistic neural sign avatars bridging the communication gap between Deaf and hearing communities.
Most 3D generation research focuses on up-projecting 2D foundation models into the 3D space either by minimizing 2D Score Distillation Sampling (SDS) loss or fine-tuning on multi-view datasets. Without explicit 3D priors these methods often lead to geometric anomalies and multi-view inconsistency. Recently researchers have attempted to improve the genuineness of 3D objects by directly training on 3D datasets albeit at the cost of low-quality texture generation due to the limited texture diversity in 3D datasets. To harness the advantages of both approaches we propose Bidirectional Diffusion (BiDiff) a unified framework that incorporates both a 3D and a 2D diffusion process to preserve both 3D fidelity and 2D texture richness respectively. Moreover as a simple combination may yield inconsistent generation results we further bridge them with novel bidirectional guidance. In addition our method can be used as an initialization of optimization-based models to further improve the quality of 3D model and efficiency of optimization reducing the process from 3.4 hours to 20 minutes. Experimental results have shown that our model achieves high-quality diverse and scalable 3D generation. Project website https://bidiff.github.io/.
We present DIRECT-3D a diffusion-based 3D generative model for creating high-quality 3D assets (represented by Neural Radiance Fields) from text prompts. Unlike recent 3D generative models that rely on clean and well-aligned 3D data limiting them to single or few-class generation our model is directly trained on extensive noisy and unaligned `in-the-wild' 3D assets mitigating the key challenge (i.e. data scarcity) in large-scale 3D generation. In particular DIRECT-3D is a tri-plane diffusion model that integrates two innovations: 1) A novel learning framework where noisy data are filtered and aligned automatically during the training process. Specifically after an initial warm-up phase using a small set of clean data an iterative optimization is introduced in the diffusion process to explicitly estimate the 3D pose of objects and select beneficial data based on conditional density. 2) An efficient 3D representation that is achieved by disentangling object geometry and color features with two separate conditional diffusion models that are optimized hierarchically. Given a prompt input our model generates high-quality high-resolution realistic and complex 3D objects with accurate geometric details in seconds. We achieve state-of-the-art performance in both single-class generation and text-to-3D generation. We also demonstrate that DIRECT-3D can serve as a useful 3D geometric prior of objects for example to alleviate the well-known Janus problem in 2D-lifting methods such as DreamFusion.
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.
Music is a universal language that can communicate emotions and feelings. It forms an essential part of the whole spectrum of creative media ranging from movies to social media posts. Machine learning models that can synthesize music are predominantly conditioned on textual descriptions of it. Inspired by how musicians compose music not just from a movie script but also through visualizations we propose MeLFusion a model that can effectively use cues from a textual description and the corresponding image to synthesize music. MeLFusion is a text-to-music diffusion model with a novel "visual synapse" which effectively infuses the semantics from the visual modality into the generated music. To facilitate research in this area we introduce a new dataset MeLBench and propose a new evaluation metric IMSM. Our exhaustive experimental evaluation suggests that adding visual information to the music synthesis pipeline significantly improves the quality of generated music measured both objectively and subjectively with a relative gain of up to 67.98% on the FAD score. We hope that our work will gather attention to this pragmatic yet relatively under-explored research area.
DiffSHEG: A Diffusion-Based Approach for Real-Time Speech-driven Holistic 3D Expression and Gesture Generation
PDF ↗We propose DiffSHEG a Diffusion-based approach for Speech-driven Holistic 3D Expression and Gesture generation. While previous works focused on co-speech gesture or expression generation individually the joint generation of synchronized expressions and gestures remains barely explored. To address this our diffusion-based co-speech motion generation Transformer enables uni-directional information flow from expression to gesture facilitating improved matching of joint expression-gesture distributions. Furthermore we introduce an outpainting-based sampling strategy for arbitrary long sequence generation in diffusion models offering flexibility and computational efficiency. Our method provides a practical solution that produces high-quality synchronized expression and gesture generation driven by speech. Evaluated on two public datasets our approach achieves state-of-the-art performance both quantitatively and qualitatively. Additionally a user study confirms the superiority of our method over prior approaches. By enabling the real-time generation of expressive and synchronized motions our method showcases its potential for various applications in the development of digital humans and embodied agents.
This paper endeavors to advance the precision of snapshot compressive imaging (SCI) reconstruction for multispectral image (MSI). To achieve this we integrate the advantageous attributes of established SCI techniques and an image generative model propose a novel structured zero-shot diffusion model dubbed DiffSCI. DiffSCI leverages the structural insights from the deep prior and optimization-based methodologies complemented by the generative capabilities offered by the contemporary denoising diffusion model. Specifically firstly we employ a pre-trained diffusion model which has been trained on a substantial corpus of RGB images as the generative denoiser within the Plug-and-Play framework for the first time. This integration allows for the successful completion of SCI reconstruction especially in the case that current methods struggle to address effectively. Secondly we systematically account for spectral band correlations and introduce a robust methodology to mitigate wavelength mismatch thus enabling seamless adaptation of the RGB diffusion model to MSIs.Thirdly an accelerated algorithm is implemented to expedite the resolution of the data subproblem. This augmentation not only accelerates the convergence rate but also elevates the quality of the reconstruction process.We present extensive testing to show that DiffSCI exhibits discernible performance enhancements over prevailing self-supervised and zero-shot approaches surpassing even supervised transformer counterparts across both simulated and real datasets. Code is at https://github.com/PAN083/DiffSCI.
The evolution of Diffusion Models has dramatically improved image generation quality making it increasingly difficult to differentiate between real and generated images. This development while impressive also raises significant privacy and security concerns. In response to this we propose a novel Latent REconstruction error guided feature REfinement method (LaRE^2) for detecting the diffusion-generated images. We come up with the Latent Reconstruction Error (LaRE) the first reconstruction-error based feature in the latent space for generated image detection. LaRE surpasses existing methods in terms of feature extraction efficiency while preserving crucial cues required to differentiate between the real and the fake. To exploit LaRE we propose an Error-Guided feature REfinement module (EGRE) which can refine the image feature guided by LaRE to enhance the discriminativeness of the feature. Our EGRE utilizes an align-then-refine mechanism which effectively refines the image feature for generated-image detection from both spatial and channel perspectives. Extensive experiments on the large-scale GenImage benchmark demonstrate the superiority of our LaRE^2 which surpasses the best SoTA method by up to 11.9%/12.1% average ACC/AP across 8 different image generators. LaRE also surpasses existing methods in terms of feature extraction cost delivering an impressive speed enhancement of 8 times.
While there has been significant progress in customizing text-to-image generation models generating images that combine multiple personalized concepts remains challenging. In this work we introduce Concept Weaver a method for composing customized text-to-image diffusion models at inference time. Specifically the method breaks the process into two steps: creating a template image aligned with the semantics of input prompts and then personalizing the template using a concept fusion strategy. The fusion strategy incorporates the appearance of the target concepts into the template image while retaining its structural details. The results indicate that our method can generate multiple custom concepts with higher identity fidelity compared to alternative approaches. Furthermore the method is shown to seamlessly handle more than two concepts and closely follow the semantic meaning of the input prompt without blending appearances across different subjects.