The rapid expansion of large-scale text-to-image diffusion models has raised growing concerns regarding their potential misuse in creating harmful or misleading content. In this paper we introduce MACE a finetuning framework for the task of mass concept erasure. This task aims to prevent models from generating images that embody unwanted concepts when prompted. Existing concept erasure methods are typically restricted to handling fewer than five concepts simultaneously and struggle to find a balance between erasing concept synonyms (generality) and maintaining unrelated concepts (specificity). In contrast MACE differs by successfully scaling the erasure scope up to 100 concepts and by achieving an effective balance between generality and specificity. This is achieved by leveraging closed-form cross-attention refinement along with LoRA finetuning collectively eliminating the information of undesirable concepts. Furthermore MACE integrates multiple LoRAs without mutual interference. We conduct extensive evaluations of MACE against prior methods across four different tasks: object erasure celebrity erasure explicit content erasure and artistic style erasure. Our results reveal that MACE surpasses prior methods in all evaluated tasks. Code is available at https://github.com/Shilin-LU/MACE.
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This paper introduces the first text-guided work for generating the sequence of hand-object interaction in 3D. The main challenge arises from the lack of labeled data where existing ground-truth datasets are nowhere near generalizable in interaction type and object category which inhibits the modeling of diverse 3D hand-object interaction with the correct physical implication (e.g. contacts and semantics) from text prompts. To address this challenge we propose to decompose the interaction generation task into two subtasks: hand-object contact generation; and hand-object motion generation. For contact generation a VAE-based network takes as input a text and an object mesh and generates the probability of contacts between the surfaces of hands and the object during the interaction. The network learns a variety of local geometry structure of diverse objects that is independent of the objects' category and thus it is applicable to general objects. For motion generation a Transformer-based diffusion model utilizes this 3D contact map as a strong prior for generating physically plausible hand-object motion as a function of text prompts by learning from the augmented labeled dataset; where we annotate text labels from many existing 3D hand and object motion data. Finally we further introduce a hand refiner module that minimizes the distance between the object surface and hand joints to improve the temporal stability of the object-hand contacts and to suppress the penetration artifacts. In the experiments we demonstrate that our method can generate more realistic and diverse interactions compared to other baseline methods. We also show that our method is applicable to unseen objects. We will release our model and newly labeled data as a strong foundation for future research. Codes and data are available in: https://github.com/JunukCha/Text2HOI.
Recent advancements in personalized image generation using diffusion models have been noteworthy. However existing methods suffer from inefficiencies due to the requirement for subject-specific fine-tuning. This computationally intensive process hinders efficient deployment limiting practical usability. Moreover these methods often grapple with identity distortion and limited expression diversity. In light of these challenges we propose PortraitBooth an innovative approach designed for high efficiency robust identity preservation and expression-editable text-to-image generation without the need for fine-tuning. PortraitBooth leverages subject embeddings from a face recognition model for personalized image generation without fine-tuning. It eliminates computational overhead and mitigates identity distortion. The introduced dynamic identity preservation strategy further ensures close resemblance to the original image identity. Moreover PortraitBooth incorporates emotion-aware cross-attention control for diverse facial expressions in generated images supporting text-driven expression editing. Its scalability enables efficient and high-quality image creation including multi-subject generation. Extensive results demonstrate superior performance over other state-of-the-art methods in both single and multiple image generation scenarios.
Estimating 3D full-body avatars from AR/VR devices is essential for creating immersive experiences in AR/VR applications. This task is challenging due to the limited input from Head Mounted Devices which capture only sparse observations from the head and hands. Predicting the full-body avatars particularly the lower body from these sparse observations presents significant difficulties. In this paper we are inspired by the inherent property of the kinematic tree defined in the Skinned Multi-Person Linear (SMPL) model where the upper body and lower body share only one common ancestor node bringing the potential of decoupled reconstruction. We propose a stratified approach to decouple the conventional full-body avatar reconstruction pipeline into two stages with the reconstruction of the upper body first and a subsequent reconstruction of the lower body conditioned on the previous stage. To implement this straightforward idea we leverage the latent diffusion model as a powerful probabilistic generator and train it to follow the latent distribution of decoupled motions explored by a VQ-VAE encoder-decoder model. Extensive experiments on AMASS mocap dataset demonstrate our state-of-the-art performance in the reconstruction of full-body motions.
DiffusionRegPose: Enhancing Multi-Person Pose Estimation using a Diffusion-Based End-to-End Regression Approach
PDF ↗This paper presents the DiffusionRegPose a novel approach to multi-person pose estimation that converts a one-stage end-to-end keypoint regression model into a diffusion-based sampling process. Existing one-stage deterministic regression methods though efficient are often prone to missed or false detections in crowded or occluded scenes due to their inability to reason pose ambiguity. To address these challenges we handle ambiguous poses in a generative fashion i.e. sampling from the image-conditioned pose distributions characterized by a diffusion probabilistic model. Specifically with initial pose tokens extracted from the image noisy pose candidates are progressively refined by interacting with the initial tokens via attention layers. Extensive evaluations on the COCO and CrowdPose datasets show that DiffusionRegPose clearly improves the pose accuracy in crowded scenarios as evidenced by a notable 4.0 AP increase in the AP_H metric on the CrowdPose dataset. This demonstrates the model's potential for robust and precise human pose estimation in real-world applications.
In-distribution Public Data Synthesis with Diffusion Models for Differentially Private Image Classification
PDF ↗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 present DiffInDScene a novel framework for tackling the problem of high-quality 3D indoor scene generation which is challenging due to the complexity and diversity of the indoor scene geometry. Although diffusion-based generative models have previously demonstrated impressive performance in image generation and object-level 3D generation they have not yet been applied to room-level 3D generation due to their computationally intensive costs. In DiffInDScene we propose a cascaded 3D diffusion pipeline that is efficient and possesses strong generative performance for Truncated Signed Distance Function (TSDF). The whole pipeline is designed to run on a sparse occupancy space in a coarse-to-fine fashion. Inspired by KinectFusion's incremental alignment and fusion of local TSDF volumes we propose a diffusion-based SDF fusion approach that iteratively diffuses and fuses local TSDF volumes facilitating the generation of an entire room environment. The generated results demonstrate that our work is capable to achieve high-quality room generation directly in three-dimensional space starting from scratch. In addition to the scene generation the final part of DiffInDScene can be used as a post-processing module to refine the 3D reconstruction results from multi-view stereo. According to the user study the mesh quality generated by our DiffInDScene can even outperform the ground truth mesh provided by ScanNet.
We address the problem of synthesizing multi-view optical illusions: images that change appearance upon a transformation such as a flip or rotation. We propose a simple zero-shot method for obtaining these illusions from off-the-shelf text-to-image diffusion models. During the reverse diffusion process we estimate the noise from different views of a noisy image and then combine these noise estimates together and denoise the image. A theoretical analysis suggests that this method works precisely for views that can be written as orthogonal transformations of which permutations are a subset. This leads to the idea of a visual anagram ---an image that changes appearance under some rearrangement of pixels. This includes rotations and flips but also more exotic pixel permutations such as a jigsaw rearrangement. Our approach also naturally extends to illusions with more than two views. We provide both qualitative and quantitative results demonstrating the effectiveness and flexibility of our method. Please see our project webpage for additional visualizations and results: https://dangeng.github.io/visual_anagrams/
Addressing pose ambiguity in 6D object pose estimation from single RGB images presents a significant challenge particularly due to object symmetries or occlusions. In response we introduce a novel score-based diffusion method applied to the SE(3) group marking the first application of diffusion models to SE(3) within the image domain specifically tailored for pose estimation tasks. Extensive evaluations demonstrate the method's efficacy in handling pose ambiguity mitigating perspective-induced ambiguity and showcasing the robustness of our surrogate Stein score formulation on SE(3). This formulation not only improves the convergence of denoising process but also enhances computational efficiency. Thus we pioneer a promising strategy for 6D object pose estimation.
Retrieval Augmented Generation (RAG) is emerging as a flexible and robust technique to adapt models to private users data without training to handle credit attribution and to allow efficient machine unlearning at scale. However RAG techniques for image generation may lead to parts of the retrieved samples being copied in the model's output. To reduce risks of leaking private information contained in the retrieved set we introduce Copy-Protected generation with Retrieval (CPR) a new method for RAG with strong copyright protection guarantees in a mixed-private setting for diffusion models. CPR allows to condition the output of diffusion models on a set of retrieved images while also guaranteeing that unique identifiable information about those example is not exposed in the generated outputs. In particular it does so by sampling from a mixture of public (safe) distribution and private (user) distribution by merging their diffusion scores at inference. We prove that CPR satisfies Near Access Freeness (NAF) which bounds the amount of information an attacker may be able to extract from the generated images. We provide two algorithms for copyright protection CPR-KL and CPR-Choose. Unlike previously proposed rejection-sampling-based NAF methods our methods enable efficient copyright-protected sampling with a single run of backward diffusion. We show that our method can be applied to any pre-trained conditional diffusion model such as Stable Diffusion or unCLIP. In particular we empirically show that applying CPR on top of un- CLIP improves quality and text-to-image alignment of the generated results (81.4 to 83.17 on TIFA benchmark) while enabling credit attribution copy-right protection and deterministic constant time unlearning.
Diffusion models (DMs) have exhibited superior performance in generating high-quality and diverse images. However this exceptional performance comes at the cost of expensive generation process particularly due to the heavily used attention module in leading models. Existing works mainly adopt a retraining process to enhance DM efficiency. This is computationally expensive and not very scalable. To this end we introduce the Attention-driven Training-free Efficient Diffusion Model (AT-EDM) framework that leverages attention maps to perform run-time pruning of redundant tokens without the need for any retraining. Specifically for single-denoising-step pruning we develop a novel ranking algorithm Generalized Weighted Page Rank (G-WPR) to identify redundant tokens and a similarity-based recovery method to restore tokens for the convolution operation. In addition we propose a Denoising-Steps-Aware Pruning (DSAP) approach to adjust the pruning budget across different denoising timesteps for better generation quality. Extensive evaluations show that AT-EDM performs favorably against prior art in terms of efficiency (e.g. 38.8% FLOPs saving and up to 1.53x speed-up over Stable Diffusion XL) while maintaining nearly the same FID and CLIP scores as the full model.
Hyperspectral image (HSI) restoration aims at recovering clean images from degraded observations and plays a vital role in downstream tasks. Existing model-based methods have limitations in accurately modeling the complex image characteristics with handcraft priors and deep learning-based methods suffer from poor generalization ability. To alleviate these issues this paper proposes an unsupervised HSI restoration framework with pre-trained diffusion model (HIR-Diff) which restores the clean HSIs from the product of two low-rank components i.e. the reduced image and the coefficient matrix. Specifically the reduced image which has a low spectral dimension lies in the image field and can be inferred from our improved diffusion model where a new guidance function with total variation (TV) prior is designed to ensure that the reduced image can be well sampled. The coefficient matrix can be effectively pre-estimated based on singular value decomposition (SVD) and rank-revealing QR (RRQR) factorization. Furthermore a novel exponential noise schedule is proposed to accelerate the restoration process (about 5xacceleration for denoising) with little performance decrease. Extensive experimental results validate the superiority of our method in both performance and speed on a variety of HSI restoration tasks including HSI denoising noisy HSI super-resolution and noisy HSI inpainting. The code is available at https://github.com/LiPang/HIRDiff.
Learning Spatial Adaptation and Temporal Coherence in Diffusion Models for Video Super-Resolution
PDF ↗Diffusion models are just at a tipping point for image super-resolution task. Nevertheless it is not trivial to capitalize on diffusion models for video super-resolution which necessitates not only the preservation of visual appearance from low-resolution to high-resolution videos but also the temporal consistency across video frames. In this paper we propose a novel approach pursuing Spatial Adaptation and Temporal Coherence (SATeCo) for video super-resolution. SATeCo pivots on learning spatial-temporal guidance from low-resolution videos to calibrate both latent-space high-resolution video denoising and pixel-space video reconstruction. Technically SATeCo freezes all the parameters of the pre-trained UNet and VAE and only optimizes two deliberately-designed spatial feature adaptation (SFA) and temporal feature alignment (TFA) modules in the decoder of UNet and VAE. SFA modulates frame features via adaptively estimating affine parameters for each pixel guaranteeing pixel-wise guidance for high-resolution frame synthesis. TFA delves into feature interaction within a 3D local window (tubelet) through self-attention and executes cross-attention between tubelet and its low-resolution counterpart to guide temporal feature alignment. Extensive experiments conducted on the REDS4 and Vid4 datasets demonstrate the effectiveness of our approach.
Text-to-image (T2I) generative models have recently emerged as a powerful tool enabling the creation of photo-realistic images and giving rise to a multitude of applications. However the effective integration of T2I models into fundamental image classification tasks remains an open question. A prevalent strategy to bolster image classification performance is through augmenting the training set with synthetic images generated by T2I models. In this study we scrutinize the shortcomings of both current generative and conventional data augmentation techniques. Our analysis reveals that these methods struggle to produce images that are both faithful (in terms of foreground objects) and diverse (in terms of background contexts) for domain-specific concepts. To tackle this challenge we introduce an innovative inter-class data augmentation method known as Diff-Mix (\href https://github.com/Zhicaiwww/Diff-Mix) https://github.com/Zhicaiwww/Diff-Mix which enriches the dataset by performing image translations between classes. Our empirical results demonstrate that Diff-Mix achieves a better balance between faithfulness and diversity leading to a marked improvement in performance across diverse image classification scenarios including few-shot conventional and long-tail classifications for domain-specific datasets.
Recent advances in generative diffusion models have enabled the previously unfeasible capability of generating 3D assets from a single input image or a text prompt. In this work we aim to enhance the quality and functionality of these models for the task of creating controllable photorealistic human avatars. We achieve this by integrating a 3D morphable model into the state-of-the-art multi-view-consistent diffusion approach. We demonstrate that accurate conditioning of a generative pipeline on the articulated 3D model enhances the baseline model performance on the task of novel view synthesis from a single image. More importantly this integration facilitates a seamless and accurate incorporation of facial expression and body pose control into the generation process. To the best of our knowledge our proposed framework is the first diffusion model to enable the creation of fully 3D-consistent animatable and photorealistic human avatars from a single image of an unseen subject; extensive quantitative and qualitative evaluations demonstrate the advantages of our approach over existing state-of-the-art avatar creation models on both novel view and novel expression synthesis tasks. The code for our project is publicly available.
Creating high-dynamic videos such as motion-rich actions and sophisticated visual effects poses a significant challenge in the field of artificial intelligence. Unfortunately current state-of-the-art video generation methods primarily focusing on text-to-video generation tend to produce video clips with minimal motions despite maintaining high fidelity. We argue that relying solely on text instructions is insufficient and suboptimal for video generation. In this paper we introduce PixelDance a novel approach based on diffusion models that incorporates image instructions for both the first and last frames in conjunction with text instructions for video generation. Comprehensive experimental results demonstrate that PixelDance trained with public data exhibits significantly better proficiency in synthesizing videos with complex scenes and intricate motions setting a new standard for video generation.
Diffusion models have shown tremendous results in image generation. However due to the iterative nature of the diffusion process and its reliance on classifier-free guidance inference times are slow. In this paper we propose a new distillation approach for guided diffusion models in which an external lightweight guide model is trained while the original text-to-image model remains frozen. We show that our method reduces the inference computation of classifier-free guided latent-space diffusion models by almost half and only requires 1% trainable parameters of the base model. Furthermore once trained our guide model can be applied to various fine-tuned domain-specific versions of the base diffusion model without the need for additional training: this "plug-and-play" functionality drastically improves inference computation while maintaining the visual fidelity of generated images. Empirically we show that our approach is able to produce visually appealing results and achieve a comparable FID score to the teacher with as few as 8 to 16 steps.
Confronting the challenges of data scarcity and advanced motion synthesis in human-scene interaction modeling we introduce the TRUMANS dataset alongside a novel HSI motion synthesis method. TRUMANS stands as the most comprehensive motion-captured HSI dataset currently available encompassing over 15 hours of human interactions across 100 indoor scenes. It intricately captures whole-body human motions and part-level object dynamics focusing on the realism of contact. This dataset is further scaled up by transforming physical environments into exact virtual models and applying extensive augmentations to appearance and motion for both humans and objects while maintaining interaction fidelity. Utilizing TRUMANS we devise a diffusion-based autoregressive model that efficiently generates HSI sequences of any length taking into account both scene context and intended actions. In experiments our approach shows remarkable zero-shot generalizability on a range of 3D scene datasets (e.g. PROX Replica ScanNet ScanNet++) producing motions that closely mimic original motion-captured sequences as confirmed by quantitative experiments and human studies.
DiffusionPoser: Real-time Human Motion Reconstruction From Arbitrary Sparse Sensors Using Autoregressive Diffusion
PDF ↗Motion capture from a limited number of body-worn sensors such as inertial measurement units (IMUs) and pressure insoles has important applications in health human performance and entertainment. Recent work has focused on accurately reconstructing whole-body motion from a specific sensor configuration using six IMUs. While a common goal across applications is to use the minimal number of sensors to achieve required accuracy the optimal arrangement of the sensors might differ from application to application. We propose a single diffusion model DiffusionPoser which reconstructs human motion in real-time from an arbitrary combination of sensors including IMUs placed at specified locations and pressure insoles. Unlike existing methods our model grants users the flexibility to determine the number and arrangement of sensors tailored to the specific activity of interest without the need for retraining. A novel autoregressive inferencing scheme ensures real-time motion reconstruction that closely aligns with measured sensor signals. The generative nature of DiffusionPoser ensures realistic behavior even for degrees-of-freedom not directly measured. Qualitative results can be found on our project website.
The inherent generative power of denoising diffusion models makes them well-suited for image restoration tasks where the objective is to find the optimal high-quality image within the generative space that closely resembles the input image. We propose a method to adapt a pretrained diffusion model for image restoration by simply adding noise to the input image to be restored and then denoise. Our method is based on the observation that the space of a generative model needs to be constrained. We impose this constraint by finetuning the generative model with a set of anchor images that capture the characteristics of the input image. With the constrained space we can then leverage the sampling strategy used for generation to do image restoration. We evaluate against previous methods and show superior performances on multiple real-world restoration datasets in preserving identity and image quality. We also demonstrate an important and practical application on personalized restoration where we use a personal album as the anchor images to constrain the generative space. This approach allows us to produce results that accurately preserve high-frequency details which previous works are unable to do. Project webpage: https://gen2res.github.io.