We introduce a 3D-aware diffusion model ZeroNVS for single-image novel view synthesis for in-the-wild scenes. While existing methods are designed for single objects with masked backgrounds we propose new techniques to address challenges introduced by in-the-wild multi-object scenes with complex backgrounds. Specifically we train a generative prior on a mixture of data sources that capture object-centric indoor and outdoor scenes. To address issues from data mixture such as depth-scale ambiguity we propose a novel camera conditioning parameterization and normalization scheme. Further we observe that Score Distillation Sampling (SDS) tends to truncate the distribution of complex backgrounds during distillation of 360-degree scenes and propose "SDS anchoring" to improve the diversity of synthesized novel views. Our model sets a new state-of-the-art result in LPIPS on the DTU dataset in the zero-shot setting even outperforming methods specifically trained on DTU. We further adapt the challenging Mip-NeRF 360 dataset as a new benchmark for single-image novel view synthesis and demonstrate strong performance in this setting. Code and models will be publicly available.
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
Large language models (LLMs) are fine-tuned using human comparison data with Reinforcement Learning from Human Feedback (RLHF) methods to make them better aligned with users' preferences. In contrast to LLMs human preference learning has not been widely explored in text-to-image diffusion models; the best existing approach is to fine-tune a pretrained model using carefully curated high quality images and captions to improve visual appeal and text alignment. We propose Diffusion-DPO a method to align diffusion models to human preferences by directly optimizing on human comparison data. Diffusion-DPO is adapted from the recently developed Direct Preference Optimization (DPO) a simpler alternative to RLHF which directly optimizes a policy that best satisfies human preferences under a classification objective. We re-formulate DPO to account for a diffusion model notion of likelihood utilizing the evidence lower bound to derive a differentiable objective. Using the Pick-a-pic dataset of 851K crowdsourced pairwise preferences we fine-tune the base model of the state-of-the-art Stable Diffusion XL (SDXL)-1.0 model with Diffusion-DPO. Our fine-tuned base model significantly outperforms both base SDXL-1.0 and the larger SDXL-1.0 model consisting of an additional refinement model in human evaluation improving visual appeal and prompt alignment. We also develop a variant that uses AI feedback and has comparable performance to training on human preferences opening the door for scaling of diffusion model alignment methods.
NoiseCLR: A Contrastive Learning Approach for Unsupervised Discovery of Interpretable Directions in Diffusion Models
PDF ↗Generative models have been very popular in the recent years for their image generation capabilities. GAN-based models are highly regarded for their disentangled latent space which is a key feature contributing to their success in controlled image editing. On the other hand diffusion models have emerged as powerful tools for generating high-quality images. However the latent space of diffusion models is not as thoroughly explored or understood. Existing methods that aim to explore the latent space of diffusion models usually relies on text prompts to pinpoint specific semantics. However this approach may be restrictive in areas such as art fashion or specialized fields like medicine where suitable text prompts might not be available or easy to conceive thus limiting the scope of existing work. In this paper we propose an unsupervised method to discover latent semantics in text-to-image diffusion models without relying on text prompts. Our method takes a small set of unlabeled images from specific domains such as faces or cats and a pre-trained diffusion model and discovers diverse semantics in unsupervised fashion using a contrastive learning objective. Moreover the learned directions can be applied simultaneously either within the same domain (such as various types of facial edits) or across different domains (such as applying cat and face edits within the same image) without interfering with each other. Our extensive experiments show that our method achieves highly disentangled edits outperforming existing approaches in both diffusion-based and GAN-based latent space editing methods.
Video and audio content creation serves as the core technique for the movie industry and professional users. Recently existing diffusion-based methods tackle video and audio generation separately which hinders the technique transfer from academia to industry. In this work we aim at filling the gap with a carefully designed optimization-based framework for cross-visual-audio and joint-visual-audio generation. We observe the powerful generation ability of off-the-shelf video or audio generation models. Thus instead of training the giant models from scratch we propose to bridge the existing strong models with a shared latent representation space. Specifically we propose a multimodality latent aligner with the pre-trained ImageBind model. Our latent aligner shares a similar core as the classifier guidance that guides the diffusion denoising process during inference time. Through carefully designed optimization strategy and loss functions we show the superior performance of our method on joint video-audio generation visual-steered audio generation and audio-steered visual generation tasks. The project website can be found at \href https://yzxing87.github.io/Seeing-and-Hearing/ https://yzxing87.github.io/Seeing-and-Hearing/ .
We introduce MultiDiff a novel approach for consistent novel view synthesis of scenes from a single RGB image. The task of synthesizing novel views from a single reference image is highly ill-posed by nature as there exist multiple plausible explanations for unobserved areas. To address this issue we incorporate strong priors in form of monocular depth predictors and video-diffusion models. Monocular depth enables us to condition our model on warped reference images for the target views increasing geometric stability. The video-diffusion prior provides a strong proxy for 3D scenes allowing the model to learn continuous and pixel-accurate correspondences across generated images. In contrast to approaches relying on autoregressive image generation that are prone to drifts and error accumulation MultiDiff jointly synthesizes a sequence of frames yielding high-quality and multi-view consistent results -- even for long-term scene generation with large camera movements while reducing inference time by an order of magnitude. For additional consistency and image quality improvements we introduce a novel structured noise distribution. Our experimental results demonstrate that MultiDiff outperforms state-of-the-art methods on the challenging real-world datasets RealEstate10K and ScanNet. Finally our model naturally supports multi-view consistent editing without the need for further tuning.
This paper focuses on synthesizing high-quality and complete textures directly on the surface of 3D models within 3D space. 2D diffusion-based methods face challenges in generating 2D texture maps due to the infinite possibilities of UV mapping for a given 3D mesh. Utilizing point clouds helps circumvent variations arising from diverse mesh topologies and UV mappings. Nevertheless achieving dense point clouds to accurately represent texture details poses a challenge due to limited computational resources. To address these challenges we propose an efficient octree-based diffusion pipeline called TexOct. Our method starts by sampling a point cloud from the surface of a given 3D model with each point containing texture noise values. We utilize an octree structure to efficiently represent this point cloud. Additionally we introduce an innovative octree-based diffusion model that leverages the denoising capabilities of the Denoising Diffusion Probabilistic Model (DDPM). This model gradually reduces the texture noise on the octree nodes resulting in the restoration of fine texture. Experimental results on ShapeNet demonstrate that TexOct effectively generates high-quality 3D textures in both unconditional and text / image-conditional scenarios.
We introduce a framework for intrinsic latent diffusion models operating directly on the surfaces of 3D shapes with the goal of synthesizing high-quality textures. Our approach is underpinned by two contributions: Field Latents a latent representation encoding textures as discrete vector fields on the mesh vertices and Field Latent Diffusion Models which learn to denoise a diffusion process in the learned latent space on the surface. We consider a single-textured-mesh paradigm where our models are trained to generate variations of a given texture on a mesh. We show the synthesized textures are of superior fidelity compared those from existing single-textured-mesh generative models. Our models can also be adapted for user-controlled editing tasks such as inpainting and label-guided generation. The efficacy of our approach is due in part to the equivariance of our proposed framework under isometries allowing our models to seamlessly reproduce details across locally similar regions and opening the door to a notion of generative texture transfer. Code and visualizations are available at https://single-mesh-diffusion.github.io/.
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.
While GAN-based models have been successful in image stylization tasks they often struggle with structure preservation while stylizing a wide range of input images. Recently diffusion models have been adopted for image stylization but still lack the capability to maintain the original quality of input images. Building on this we propose OSASIS: a novel one-shot stylization method that is robust in structure preservation. We show that OSASIS is able to effectively disentangle the semantics from the structure of an image allowing it to control the level of content and style implemented to a given input. We apply OSASIS to various experimental settings including stylization with out-of-domain reference images and stylization with text-driven manipulation. Results show that OSASIS outperforms other stylization methods especially for input images that were rarely encountered during training providing a promising solution to stylization via diffusion models.
Generative AI (GenAI) is transforming creative workflows through the capability to synthesize and manipulate images via high-level prompts. Yet creatives are not well supported to receive recognition or reward for the use of their content in GenAI training. To this end we propose ProMark a causal attribution technique to attribute a synthetically generated image to its training data concepts like objects motifs templates artists or styles. The concept information is proactively embedded into the input training images using imperceptible watermarks and the diffusion models (unconditional or conditional) are trained to retain the corresponding watermarks in generated images. We show that we can embed as many as 2^ 16 unique watermarks into the training data and each training image can contain more than one watermark. ProMark can maintain image quality whilst outperforming correlation-based attribution. Finally several qualitative examples are presented providing the confidence that the presence of the watermark conveys a causative relationship between training data and synthetic images.
We present TextureDreamer a novel image-guided texture synthesis method to transfer relightable textures from a small number of input images (3 to 5) to target 3D shapes across arbitrary categories. Texture creation is a pivotal challenge in vision and graphics. Industrial companies hire experienced artists to manually craft textures for 3D assets. Classical methods require densely sampled views and accurately aligned geometry while learning-based methods are confined to category-specific shapes within the dataset. In contrast TextureDreamer can transfer highly detailed intricate textures from real-world environments to arbitrary objects with only a few casually captured images potentially significantly democratizing texture creation. Our core idea personalized geometry-aware score distillation (PGSD) draws inspiration from recent advancements in diffuse models including personalized modeling for texture information extraction score distillation for detailed appearance synthesis and explicit geometry guidance with ControlNet. Our integration and several essential modifications substantially improve the texture quality. Experiments on real images spanning different categories show that TextureDreamer can successfully transfer highly realistic semantic meaningful texture to arbitrary objects surpassing the visual quality of previous state-of-the-art. Project page: https://texturedreamer.github.io
Recent developments in face restoration have achieved remarkable results in producing high-quality and lifelike outputs. The stunning results however often fail to be faithful with respect to the identity of the person as the models lack necessary context. In this paper we explore the potential of personalized face restoration with diffusion models. In our approach a restoration model is personalized using a few images of the identity leading to tailored restoration with respect to the identity while retaining fine-grained details. By using independent trainable blocks for personalization the rich prior of a base restoration model can be exploited to its fullest. To avoid the model relying on parts of identity left in the conditioning low-quality images a generative regularizer is employed. With a learnable parameter the model learns to balance between the details generated based on the input image and the degree of personalization. Moreover we improve the training pipeline of face restoration models to enable an alignment-free approach. We showcase the robust capabilities of our approach in several real-world scenarios with multiple identities demonstrating our method's ability to generate fine-grained details with faithful restoration. In the user study we evaluate the perceptual quality and faithfulness of the generated details with our method being voted best 61% of the time compared to the second best with 25% of the votes.
We train a set of open text-to-image (T2I) diffusion models on a dataset of curated Creative-Commons-licensed (CC) images which yields models that are competitive with Stable Diffusion 2 (SD2). This task presents two challenges: (1) high-resolution CC images lack the captions necessary to train T2I models; (2) CC images are relatively scarce. To address these challenges we use an intuitive transfer learning technique to produce a set of high-quality synthetic captions paired with our assembled CC images. We then develop a data- and compute-efficient training recipe that requires as little as 3% of the LAION data (i.e. roughly 70 million examples) needed to train existing SD2 models but obtains the same quality. These results indicate that we have a sufficient number of CC images (also roughly 70 million) for training high-quality models. Our recipe also implements a variety of optimizations that achieve 2.71x training speed-ups enabling rapid model iteration. We leverage this recipe to train several high-quality T2I mod- els which we dub the CommonCanvas family. Our largest model achieves comparable performance to SD2 on human evaluation even though we use a synthetically captioned CC-image dataset that is only <3% the size of LAION for training. We release our models data and code on GitHub.
Your Student is Better Than Expected: Adaptive Teacher-Student Collaboration for Text-Conditional Diffusion Models
PDF ↗Knowledge distillation methods have recently shown to be a promising direction to speedup the synthesis of large-scale diffusion models by requiring only a few inference steps. While several powerful distillation methods were recently proposed the overall quality of student samples is typically lower compared to the teacher ones which hinders their practical usage. In this work we investigate the relative quality of samples produced by the teacher text-to-image diffusion model and its distilled student version. As our main empirical finding we discover that a noticeable portion of student samples exhibit superior fidelity compared to the teacher ones despite the approximate nature of the student. Based on this finding we propose an adaptive collaboration between student and teacher diffusion models for effective text-to-image synthesis. Specifically the distilled model produces an initial image sample and then an oracle decides whether it needs further improvements with the teacher model. Extensive experiments demonstrate that the designed pipeline surpasses state-of-the-art text-to-image alternatives for various inference budgets in terms of human preference. Furthermore the proposed approach can be naturally used in popular applications such as text-guided image editing and controllable generation.
Triplane Meets Gaussian Splatting: Fast and Generalizable Single-View 3D Reconstruction with Transformers
PDF ↗Recent advancements in 3D reconstruction from single images have been driven by the evolution of generative models. Prominent among these are methods based on Score Distillation Sampling (SDS) and the adaptation of diffusion models in the 3D domain. Despite their progress these techniques often face limitations due to slow optimization or rendering processes leading to extensive training and optimization times. In this paper we introduce a novel approach for single-view reconstruction that efficiently generates a 3D model from a single image via feed-forward inference. Our method utilizes two transformer-based networks namely a point decoder and a triplane decoder to reconstruct 3D objects using a hybrid Triplane-Gaussian intermediate representation. This hybrid representation strikes a balance achieving a faster rendering speed compared to implicit representations while simultaneously delivering superior rendering quality than explicit representations. The point decoder is designed for generating point clouds from single images offering an explicit representation which is then utilized by the triplane decoder to query Gaussian features for each point. This design choice addresses the challenges associated with directly regressing explicit 3D Gaussian attributes characterized by their non-structural nature. Subsequently the 3D Gaussians are decoded by an MLP to enable rapid rendering through splatting. Both decoders are built upon a scalable transformer-based architecture and have been efficiently trained on large-scale 3D datasets. The evaluations conducted on both synthetic datasets and real-world images demonstrate that our method not only achieves higher quality but also ensures a faster runtime in comparison to previous state-of-the-art techniques. Please see our project page at https://zouzx.github.io/TriplaneGaussian/
Lifting 2D diffusion for 3D generation is a challenging problem due to the lack of geometric prior and the complex entanglement of materials and lighting in natural images. Existing methods have shown promise by first creating the geometry through score-distillation sampling (SDS) applied to rendered surface normals followed by appearance modeling. However relying on a 2D RGB diffusion model to optimize surface normals is suboptimal due to the distribution discrepancy between natural images and normals maps leading to instability in optimization. In this paper recognizing that the normal and depth information effectively describe scene geometry and be automatically estimated from images we propose to learn a generalizable Normal-Depth diffusion model for 3D generation. We achieve this by training on the large-scale LAION dataset together with the generalizable image-to-depth and normal prior models. In an attempt to alleviate the mixed illumination effects in the generated materials we introduce an albedo diffusion model to impose data-driven constraints on the albedo component. Our experiments show that when integrated into existing text-to-3D pipelines our models significantly enhance the detail richness achieving state-of-the-art results. Our project page is at https://aigc3d.github.io/richdreamer/.
Genuine Knowledge from Practice: Diffusion Test-Time Adaptation for Video Adverse Weather Removal
PDF ↗Real-world vision tasks frequently suffer from the appearance of unexpected adverse weather conditions including rain haze snow and raindrops. In the last decade convolutional neural networks and vision transformers have yielded outstanding results in single-weather video removal. However due to the absence of appropriate adaptation most of them fail to generalize to other weather conditions. Although ViWS-Net is proposed to remove adverse weather conditions in videos with a single set of pre-trained weights it is seriously blinded by seen weather at train-time and degenerates when coming to unseen weather during test-time. In this work we introduce test-time adaptation into adverse weather removal in videos and propose the first framework that integrates test-time adaptation into the iterative diffusion reverse process. Specifically we devise a diffusion-based network with a novel temporal noise model to efficiently explore frame-correlated information in degraded video clips at training stage. During inference stage we introduce a proxy task named Diffusion Tubelet Self-Calibration to learn the primer distribution of test video stream and optimize the model by approximating the temporal noise model for online adaptation. Experimental results on benchmark datasets demonstrate that our Test-Time Adaptation method with Diffusion-based network(Diff-TTA) outperforms state-of-the-art methods in terms of restoring videos degraded by seen weather conditions. Its generalizable capability is validated with unseen weather conditions in synthesized and real-world videos.
In this work we introduce Wonder3D a novel method for generating high-fidelity textured meshes from single-view images with remarkable efficiency. Recent methods based on the Score Distillation Sampling (SDS) loss methods have shown the potential to recover 3D geometry from 2D diffusion priors but they typically suffer from time-consuming per-shape optimization and inconsistent geometry. In contrast certain works directly produce 3D information via fast network inferences but their results are often of low quality and lack geometric details. To holistically improve the quality consistency and efficiency of image-to-3D tasks we propose a cross-domain diffusion model that generates multi-view normal maps and the corresponding color images. To ensure consistency we employ a multi-view cross-domain attention mechanism that facilitates information exchange across views and modalities. Lastly we introduce a geometry-aware normal fusion algorithm that extracts high-quality surfaces from the multi-view 2D representations in only 2 3 minutes. Our extensive evaluations demonstrate that our method achieves high-quality reconstruction results robust generalization and remarkable efficiency compared to prior works.
Human Mesh Recovery (HMR) aims to estimate the 3D human body from 2D images which is a challenging task due to inherent ambiguities in translating 2D observations to 3D space. A novel approach called PostureHMR is proposed to leverage a multi-step diffusion-style process which converts this task into a posture transformation from an SMPL T-pose mesh to the target mesh. To inject the learning process of posture transformation with the physical structure of the human body model a kinematics-based forward process is proposed to interpolate the intermediate state with pose and shape decomposition. Moreover a mesh-to-posture (M2P) decoder is designed by combining the input of 3D and 2D mesh constraints estimated from the image to model the posture changes in the reverse process. It mitigates the difficulties of posture change learning directly from RGB pixels. To overcome the limitation of pixel-level misalignment of modeling results with the input image a new trimap-based rendering loss is designed to highlight the areas with poor recognition. Experiments conducted on three widely used datasets demonstrate that the proposed approach outperforms the state-of-the-art methods.
In this paper we study the problem of generalizable synthetic image detection aiming to detect forgery images from diverse generative methods e.g. GANs and diffusion models. Cutting-edge solutions start to explore the benefits of pre-trained models and mainly follow the fixed paradigm of solely training an attached classifier e.g. combining frozen CLIP-ViT with a learnable linear layer in UniFD. However our analysis shows that such a fixed paradigm is prone to yield detectors with insufficient learning regarding forgery representations. We attribute the key challenge to the lack of forgery adaptation and present a novel forgery-aware adaptive transformer approach namely FatFormer. Based on the pre-trained vision-language spaces of CLIP FatFormer introduces two core designs for the adaption to build generalized forgery representations. First motivated by the fact that both image and frequency analysis are essential for synthetic image detection we develop a forgery-aware adapter to adapt image features to discern and integrate local forgery traces within image and frequency domains. Second we find that considering the contrastive objectives between adapted image features and text prompt embeddings a previously overlooked aspect results in a nontrivial generalization improvement. Accordingly we introduce language-guided alignment to supervise the forgery adaptation with image and text prompts in FatFormer. Experiments show that by coupling these two designs our approach tuned on 4-class ProGAN data attains a remarkable detection performance achieving an average of 98% accuracy to unseen GANs and surprisingly generalizes to unseen diffusion models with 95% accuracy.