Co-speech gestures if presented in the lively form of videos can achieve superior visual effects in human-machine interaction. While previous works mostly generate structural human skeletons resulting in the omission of appearance information we focus on the direct generation of audio-driven co-speech gesture videos in this work. There are two main challenges: 1) A suitable motion feature is needed to describe complex human movements with crucial appearance information. 2) Gestures and speech exhibit inherent dependencies and should be temporally aligned even of arbitrary length. To solve these problems we present a novel motion-decoupled framework to generate co-speech gesture videos. Specifically we first introduce a well-designed nonlinear TPS transformation to obtain latent motion features preserving essential appearance information. Then a transformer-based diffusion model is proposed to learn the temporal correlation between gestures and speech and performs generation in the latent motion space followed by an optimal motion selection module to produce long-term coherent and consistent gesture videos. For better visual perception we further design a refinement network focusing on missing details of certain areas. Extensive experimental results show that our proposed framework significantly outperforms existing approaches in both motion and video-related evaluations. Our code demos and more resources are available at https://github.com/thuhcsi/S2G-MDDiffusion.
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Rethinking the Up-Sampling Operations in CNN-based Generative Network for Generalizable Deepfake Detection
PDF ↗Recently the proliferation of highly realistic synthetic images facilitated through a variety of GANs and Diffusions has significantly heightened the susceptibility to misuse. While the primary focus of deepfake detection has traditionally centered on the design of detection algorithms an investigative inquiry into the generator architectures has remained conspicuously absent in recent years. This paper contributes to this lacuna by rethinking the architectures of CNN-based generator thereby establishing a generalized representation of synthetic artifacts. Our findings illuminate that the up-sampling operator can beyond frequency-based artifacts produce generalized forgery artifacts. In particular the local interdependence among image pixels caused by upsampling operators is significantly demonstrated in synthetic images generated by GAN or diffusion. Building upon this observation we introduce the concept of Neighboring Pixel Relationships(NPR) as a means to capture and characterize the generalized structural artifacts stemming from up-sampling operations. A comprehensive analysis is conducted on an open-world dataset comprising samples generated by 28 distinct generative models. This analysis culminates in the establishment of a novel state-of-the-art performance showcasing a remarkable 12.8% improvement over existing methods. The code is available at https://github.com/chuangchuangtan/NPR-DeepfakeDetection.
Text-to-image diffusion models produce high quality images but do not offer control over individual instances in the image. We introduce InstanceDiffusion that adds precise instance-level control to text-to-image diffusion models. InstanceDiffusion supports free-form language conditions per instance and allows flexible ways to specify instance locations such as simple single points scribbles bounding boxes or intricate instance segmentation masks and combinations thereof. We propose three major changes to text-to-image models that enable precise instance-level control. Our UniFusion block enables instance-level conditions for text-to-image models the ScaleU block improves image fidelity and our Multi-instance Sampler improves generations for multiple instances. InstanceDiffusion significantly surpasses specialized state-of-the-art models for each location condition. Notably on the COCO dataset we outperform previous state-of-the-art by 20.4% AP50box for box inputs and 25.4% IoU for mask inputs.
LiDAR-based 3D object detection plays an essential role in autonomous driving. Existing high-performing 3D object detectors usually build dense feature maps in the backbone network and prediction head. However the computational costs introduced by the dense feature maps grow quadratically as the perception range increases making these models hard to scale up to long-range detection. Some recent works have attempted to construct fully sparse detectors to solve this issue; nevertheless the resulting models either rely on a complex multi-stage pipeline or exhibit inferior performance. In this work we propose a fully sparse adaptive feature diffusion network (SAFDNet) for LiDAR-based 3D object detection. In SAFDNet an adaptive feature diffusion strategy is designed to address the center feature missing problem. We conducted extensive experiments on Waymo Open nuScenes and Argoverse2 datasets. SAFDNet performed slightly better than the previous SOTA on the first two datasets but much better on the last dataset which features long-range detection verifying the efficacy of SAFDNet in scenarios where long-range detection is required. Notably on Argoverse2 SAFDNet surpassed the previous best hybrid detector HEDNet by 2.6% mAP while being 2.1x faster and yielded 2.1% mAP gains over the previous best sparse detector FSDv2 while being 1.3x faster. The code will be available at https://github.com/zhanggang001/HEDNet.
UltrAvatar: A Realistic Animatable 3D Avatar Diffusion Model with Authenticity Guided Textures
PDF ↗Recent advances in 3D avatar generation have gained significant attention. These breakthroughs aim to produce more realistic animatable avatars narrowing the gap between virtual and real-world experiences. Most of existing works employ Score Distillation Sampling (SDS) loss combined with a differentiable renderer and text condition to guide a diffusion model in generating 3D avatars. However SDS often generates over-smoothed results with few facial details thereby lacking the diversity compared with ancestral sampling. On the other hand other works generate 3D avatar from a single image where the challenges of unwanted lighting effects perspective views and inferior image quality make them difficult to reliably reconstruct the 3D face meshes with the aligned complete textures. In this paper we propose a novel 3D avatar generation approach termed UltrAvatar with enhanced fidelity of geometry and superior quality of physically based rendering (PBR) textures without unwanted lighting. To this end the proposed approach presents a diffuse color extraction model and an authenticity guided texture diffusion model. The former removes the unwanted lighting effects to reveal true diffuse colors so that the generated avatars can be rendered under various lighting conditions. The latter follows two gradient-based guidances for generating PBR textures to render diverse face-identity features and details better aligning with 3D mesh geometry. We demonstrate the effectiveness and robustness of the proposed method outperforming the state-of-the-art methods by a large margin in the experiments.
Although diffusion models are rising as a powerful solution for blind face restoration they are criticized for two problems: 1) slow training and inference speed and 2) failure in preserving identity and recovering fine-grained facial details. In this work we propose WaveFace to solve the problems in the frequency domain where low- and high-frequency components decomposed by wavelet transformation are considered individually to maximize authenticity as well as efficiency. The diffusion model is applied to recover the low-frequency component only which presents general information of the original image but 1/16 in size. To preserve the original identity the generation is conditioned on the low-frequency component of low-quality images at each denoising step. Meanwhile high-frequency components at multiple decomposition levels are handled by a unified network which recovers complex facial details in a single step. Evaluations on four benchmark datasets show that: 1) WaveFace outperforms state-of-the-art methods in authenticity especially in terms of identity preservation and 2) authentic images are restored with the efficiency 10x faster than existing diffusion model-based BFR methods.
We introduce a new task -- language-driven video inpainting which uses natural language instructions to guide the inpainting process. This approach overcomes the limitations of traditional video inpainting methods that depend on manually labeled binary masks a process often tedious and labor-intensive. We present the Remove Objects from Videos by Instructions (ROVI) dataset containing 5650 videos and 9091 inpainting results to support training and evaluation for this task. We also propose a novel diffusion-based language-driven video inpainting framework the first end-to-end baseline for this task integrating Multimodal Large Language Models to understand and execute complex language-based inpainting requests effectively. Our comprehensive results showcase the dataset's versatility and the model's effectiveness in various language-instructed inpainting scenarios. We have made datasets code and models publicly available at https://github.com/jianzongwu/Language-Driven-Video-Inpainting.
Optical flow estimation a process of predicting pixel-wise displacement between consecutive frames has commonly been approached as a regression task in the age of deep learning. Despite notable advancements this de facto paradigm unfortunately falls short in generalization performance when trained on synthetic or constrained data. Pioneering a paradigm shift we reformulate optical flow estimation as a conditional flow generation challenge unveiling FlowDiffuser --- a new family of optical flow models that could have stronger learning and generalization capabilities. FlowDiffuser estimates optical flow through a `noise-to-flow' strategy progressively eliminating noise from randomly generated flows conditioned on the provided pairs. To optimize accuracy and efficiency our FlowDiffuser incorporates a novel Conditional Recurrent Denoising Decoder (Conditional-RDD) streamlining the flow estimation process. It incorporates a unique Hidden State Denoising (HSD) paradigm effectively leveraging the information from previous time steps. Moreover FlowDiffuser can be easily integrated into existing flow networks leading to significant improvements in performance metrics compared to conventional implementations. Experiments on challenging benchmarks including Sintel and KITTI demonstrate the effectiveness of our FlowDiffuser with superior performance to existing state-of-the-art models. Code is available at https://github.com/LA30/FlowDiffuser.
We present As-Plausible-as-Possible (APAP) mesh deformation technique that leverages 2D diffusion priors to preserve the plausibility of a mesh under user-controlled deformation. Our framework uses per-face Jacobians to represent mesh deformations where mesh vertex coordinates are computed via a differentiable Poisson Solve. The deformed mesh is rendered and the resulting 2D image is used in the Score Distillation Sampling (SDS) process which enables extracting meaningful plausibility priors from a pretrained 2D diffusion model. To better preserve the identity of the edited mesh we fine-tune our 2D diffusion model with LoRA. Gradients extracted by SDS and a user-prescribed handle displacement are then backpropagated to the per-face Jacobians and we use iterative gradient descent to compute the final deformation that balances between the user edit and the output plausibility. We evaluate our method with 2D and 3D meshes and demonstrate qualitative and quantitative improvements when using plausibility priors over geometry-preservation or distortion-minimization priors used by previous techniques. Our project page is at: https://as-plausible-aspossible.github.io/
We present DiffuScene for indoor 3D scene synthesis based on a novel scene configuration denoising diffusion model. It generates 3D instance properties stored in an unordered object set and retrieves the most similar geometry for each object configuration which is characterized as a concatenation of different attributes including location size orientation semantics and geometry features. We introduce a diffusion network to synthesize a collection of 3D indoor objects by denoising a set of unordered object attributes. Unordered parametrization simplifies and eases the joint distribution approximation. The shape feature diffusion facilitates natural object placements including symmetries. Our method enables many downstream applications including scene completion scene arrangement and text-conditioned scene synthesis. Experiments on the 3D-FRONT dataset show that our method can synthesize more physically plausible and diverse indoor scenes than state-of-the-art methods. Extensive ablation studies verify the effectiveness of our design choice in scene diffusion models.
There has been a growing interest in the task of generating sound for silent videos primarily because of its practicality in streamlining video post-production. However existing methods for video-sound generation attempt to directly create sound from visual representations which can be challenging due to the difficulty of aligning visual representations with audio representations. In this paper we present SonicVisionLM a novel framework aimed at generating a wide range of sound effects by leveraging vision-language models(VLMs). Instead of generating audio directly from video we use the capabilities of powerful VLMs. When provided with a silent video our approach first identifies events within the video using a VLM to suggest possible sounds that match the video content. This shift in approach transforms the challenging task of aligning image and audio into more well-studied sub-problems of aligning image-to-text and text-to-audio through the popular diffusion models. To improve the quality of audio recommendations with LLMs we have collected an extensive dataset that maps text descriptions to specific sound effects and developed a time-controlled audio adapter. Our approach surpasses current state-of-the-art methods for converting video to audio enhancing synchronization with the visuals and improving alignment between audio and video components. Project page: https://yusiissy.github.io/SonicVisionLM.github.io/
Point-based interactive editing serves as an essential tool to complement the controllability of existing generative models. A concurrent work DragDiffusion updates the diffusion latent map in response to user inputs causing global latent map alterations. This results in imprecise preservation of the original content and unsuccessful editing due to gradient vanishing. In contrast we present DragNoise offering robust and accelerated editing without retracing the latent map. The core rationale of DragNoise lies in utilizing the predicted noise output of each U-Net as a semantic editor. This approach is grounded in two critical observations: firstly the bottleneck features of U-Net inherently possess semantically rich features ideal for interactive editing; secondly high-level semantics established early in the denoising process show minimal variation in subsequent stages. Leveraging these insights DragNoise edits diffusion semantics in a single denoising step and efficiently propagates these changes ensuring stability and efficiency in diffusion editing. Comparative experiments reveal that DragNoise achieves superior control and semantic retention reducing the optimization time by over 50% compared to DragDiffusion. Our codes are available at https://github.com/haofengl/DragNoise.
Despite recent advances in inversion-based editing text-guided image manipulation remains challenging for diffusion models. The primary bottlenecks include 1) the time-consuming nature of the inversion process; 2) the struggle to balance consistency with accuracy; 3) the lack of compatibility with efficient consistency sampling methods used in consistency models. To address the above issues we start by asking ourselves if the inversion process can be eliminated for editing. We show that when the initial sample is known a special variance schedule reduces the denoising step to the same form as the multi-step consistency sampling. We name this Denoising Diffusion Consistent Model (DDCM) and note that it implies a virtual inversion strategy without explicit inversion in sampling. We further unify the attention control mechanisms in a tuning-free framework for text-guided editing. Combining them we present inversion-free editing (InfEdit) which allows for consistent and faithful editing for both rigid and non-rigid semantic changes catering to intricate modifications without compromising on the image's integrity and explicit inversion. Through extensive experiments InfEdit shows strong performance in various editing tasks and also maintains a seamless workflow (less than 3 seconds on one single A40) demonstrating the potential for real-time applications.
Generate Subgoal Images before Act: Unlocking the Chain-of-Thought Reasoning in Diffusion Model for Robot Manipulation with Multimodal Prompts
PDF ↗Robotics agents often struggle to understand and follow the multi-modal prompts in complex manipulation scenes which are challenging to be sufficiently and accurately described by text alone. Moreover for long-horizon manipulation tasks the deviation from general instruction tends to accumulate if lack of intermediate guidance from high-level subgoals. For this we consider can we generate subgoal images before act to enhance the instruction following in long-horizon manipulation with multi-modal prompts? Inspired by the great success of diffusion model in image generation tasks we propose a novel hierarchical framework named as CoTDiffusion that incorporates diffusion model as a high-level planner to convert the general and multi-modal prompts into coherent visual subgoal plans which further guide the low-level policy model before action execution. We design a semantic alignment module that can anchor the progress of generated keyframes along a coherent generation chain unlocking the chain-of-thought reasoning ability of diffusion model. Additionally we propose bi-directional generation and frame concat mechanism to further enhance the fidelity of generated subgoal images and the accuracy of instruction following. The experiments cover various robotics manipulation scenarios including visual reasoning visual rearrange and visual constraints. CoTDiffusion achieves outstanding performance gain compared to the baselines without explicit subgoal generation which proves that a subgoal image is worth a thousand words of instruction.
We introduce X-Adapter a universal upgrader to enable the pretrained plug-and-play modules (e.g. ControlNet LoRA) to work directly with the upgraded text-to-image diffusion model (e.g. SDXL) without further retraining. We achieve this goal by training an additional network to control the frozen upgraded model with the new text-image data pairs. In detail X-Adapter keeps a frozen copy of the old model to preserve the connectors of different plugins. Additionally X-Adapter adds trainable mapping layers that bridge the decoders from models of different versions for feature remapping. The remapped features will be used as guidance for the upgraded model. To enhance the guidance ability of X-Adapter we employ a -text training strategy for the upgraded model. After training we also introduce a two-stage denoising strategy to align the initial latents of X-Adapter and the upgraded model. Thanks to our strategies X-Adapter demonstrates universal compatibility with various plugins and also enables plugins of different versions to work together thereby expanding the functionalities of diffusion community. To verify the effectiveness of the proposed method we conduct extensive experiments and the results show that X-Adapter may facilitate wider application in the upgraded foundational diffusion model. Project page at: https://showlab.github.io/X-Adapter.
Large generative diffusion models have revolutionized text-to-image generation and offer immense potential for conditional generation tasks such as image enhancement restoration editing and compositing. However their widespread adoption is hindered by the high computational cost which limits their real-time application. To address this challenge we introduce a novel method dubbed CoDi that adapts a pre-trained latent diffusion model to accept additional image conditioning inputs while significantly reducing the sampling steps required to achieve high-quality results. Our method can leverage architectures such as ControlNet to incorporate conditioning inputs without compromising the model's prior knowledge gained during large scale pre-training. Additionally a conditional consistency loss enforces consistent predictions across diffusion steps effectively compelling the model to generate high-quality images with conditions in a few steps. Our conditional-task learning and distillation approach outperforms previous distillation methods achieving a new state-of-the-art in producing high-quality images with very few steps (e.g. 1-4) across multiple tasks including super-resolution text-guided image editing and depth-to-image generation.
The quality of the prompts provided to text-to-image diffusion models determines how faithful the generated content is to the user's intent often requiring `prompt engineering'. To harness visual concepts from target images without prompt engineering current approaches largely rely on embedding inversion by optimizing and then mapping them to pseudo-tokens. However working with such high-dimensional vector representations is challenging because they lack semantics and interpretability and only allow simple vector operations when using them. Instead this work focuses on inverting the diffusion model to obtain interpretable language prompts directly. The challenge of doing this lies in the fact that the resulting optimization problem is fundamentally discrete and the space of prompts is exponentially large; this makes using standard optimization techniques such as stochastic gradient descent difficult. To this end we utilize a delayed projection scheme to optimize for prompts representative of the vocabulary space in the model. Further we leverage the findings that different timesteps of the diffusion process cater to different levels of detail in an image. The later noisy timesteps of the forward diffusion process correspond to the semantic information and therefore prompt inversion in this range provides tokens representative of the image semantics. We show that our approach can identify semantically interpretable and meaningful prompts for a target image which can be used to synthesize diverse images with similar content. We further illustrate the application of the optimized prompts in evolutionary image generation and concept removal.
With the rapid development of face recognition (FR) systems the privacy of face images on social media is facing severe challenges due to the abuse of unauthorized FR systems. Some studies utilize adversarial attack techniques to defend against malicious FR systems by generating adversarial examples. However the generated adversarial examples i.e. the protected face images tend to suffer from subpar visual quality and low transferability. In this paper we propose a novel face protection approach dubbed DiffAM which leverages the powerful generative ability of diffusion models to generate high-quality protected face images with adversarial makeup transferred from reference images. To be specific we first introduce a makeup removal module to generate non-makeup images utilizing a fine-tuned diffusion model with guidance of textual prompts in CLIP space. As the inverse process of makeup transfer makeup removal can make it easier to establish the deterministic relationship between makeup domain and non-makeup domain regardless of elaborate text prompts. Then with this relationship a CLIP-based makeup loss along with an ensemble attack strategy is introduced to jointly guide the direction of adversarial makeup domain achieving the generation of protected face images with natural-looking makeup and high black-box transferability. Extensive experiments demonstrate that DiffAM achieves higher visual quality and attack success rates with a gain of 12.98% under black-box setting compared with the state of the arts. The code will be available at https://github.com/HansSunY/DiffAM.
Diffusion model-based image restoration (IR) aims to use diffusion models to recover high-quality (HQ) images from degraded images achieving promising performance. Due to the inherent property of diffusion models most existing methods need long serial sampling chains to restore HQ images step-by-step resulting in expensive sampling time and high computation costs. Moreover such long sampling chains hinder understanding the relationship between inputs and restoration results since it is hard to compute the gradients in the whole chains. In this work we aim to rethink the diffusion model-based IR models through a different perspective i.e. a deep equilibrium (DEQ) fixed point system called DeqIR. Specifically we derive an analytical solution by modeling the entire sampling chain in these IR models as a joint multivariate fixed point system. Based on the analytical solution we can conduct parallel sampling and restore HQ images without training. Furthermore we compute fast gradients via DEQ inversion and found that initialization optimization can boost image quality and control the generation direction. Extensive experiments on benchmarks demonstrate the effectiveness of our method on typical IR tasks and real-world settings.
High-resolution image generation with Generative Artificial Intelligence (GenAI) has immense potential but due to the enormous capital investment required for training it is increasingly centralised to a few large corporations and hidden behind paywalls. This paper aims to democratise high-resolution GenAI by advancing the frontier of high-resolution generation while remaining accessible to a broad audience. We demonstrate that existing Latent Diffusion Models (LDMs) possess untapped potential for higher-resolution image generation. Our novel DemoFusion framework seamlessly extends open-source GenAI models employing Progressive Upscaling Skip Residual and Dilated Sampling mechanisms to achieve higher-resolution image generation. The progressive nature of DemoFusion requires more passes but the intermediate results can serve as "previews" facilitating rapid prompt iteration.