We present HAAR a new strand-based generative model for 3D human hairstyles. Specifically based on textual inputs HAAR produces 3D hairstyles that could be used as production-level assets in modern computer graphics engines. Current AI-based generative models take advantage of powerful 2D priors to reconstruct 3D content in the form of point clouds meshes or volumetric functions. However by using the 2D priors they are intrinsically limited to only recovering the visual parts. Highly occluded hair structures can not be reconstructed with those methods and they only model the "outer shell" which is not ready to be used in physics-based rendering or simulation pipelines. In contrast we propose a first text-guided generative method that uses 3D hair strands as an underlying representation. Leveraging 2D visual question-answering (VQA) systems we automatically annotate synthetic hair models that are generated from a small set of artist-created hairstyles. This allows us to train a latent diffusion model that operates in a common hairstyle UV space. In qualitative and quantitative studies we demonstrate the capabilities of the proposed model and compare it to existing hairstyle generation approaches. For results please refer to our project page https://haar.is.tue.mpg.de/.
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WOUAF: Weight Modulation for User Attribution and Fingerprinting in Text-to-Image Diffusion Models
PDF ↗The rapid advancement of generative models facilitating the creation of hyper-realistic images from textual descriptions has concurrently escalated critical societal concerns such as misinformation. Although providing some mitigation traditional fingerprinting mechanisms fall short in attributing responsibility for the malicious use of synthetic images. This paper introduces a novel approach to model fingerprinting that assigns responsibility for the generated images thereby serving as a potential countermeasure to model misuse. Our method modifies generative models based on each user's unique digital fingerprint imprinting a unique identifier onto the resultant content that can be traced back to the user. This approach incorporating fine-tuning into Text-to-Image (T2I) tasks using the Stable Diffusion Model demonstrates near-perfect attribution accuracy with a minimal impact on output quality. Through extensive evaluation we show that our method outperforms baseline methods with an average improvement of 11% in handling image post-processes. Our method presents a promising and novel avenue for accountable model distribution and responsible use. Our code is available in https://github.com/kylemin/WOUAF.
Generating novel views of an object from a single image is a challenging task. It requires an understanding of the underlying 3D structure of the object from an image and rendering high-quality spatially consistent new views. While recent methods for view synthesis based on diffusion have shown great progress achieving consistency among various view estimates and at the same time abiding by the desired camera pose remains a critical problem yet to be solved. In this work we demonstrate a strikingly simple method where we utilize a pre-trained video diffusion model to solve this problem. Our key idea is that synthesizing a novel view could be reformulated as synthesizing a video of a camera going around the object of interest---a scanning video---which then allows us to leverage the powerful priors that a video diffusion model would have learned. Thus to perform novel-view synthesis we create a smooth camera trajectory to the target view that we wish to render and denoise using both a view-conditioned diffusion model and a video diffusion model. By doing so we obtain a highly consistent novel view synthesis outperforming the state of the art.
Recent advances in generative AI have significantly enhanced image and video editing particularly in the context of text prompt control. State-of-the-art approaches predominantly rely on diffusion models to accomplish these tasks. However the computational demands of diffusion-based methods are substantial often necessitating large-scale paired datasets for training and therefore challenging the deployment in real applications. To address these issues this paper breaks down the text-based video editing task into two stages. First we leverage an pre-trained text-to-image diffusion model to simultaneously edit few keyframes in an zero-shot way. Second we introduce an efficient model called MaskINT which is built on non-autoregressive masked generative transformers and specializes in frame interpolation between the edited keyframes using the structural guidance from intermediate frames. Experimental results suggest that our MaskINT achieves comparable performance with diffusion-based methodologies while significantly improve the inference time. This research offers a practical solution for text-based video editing and showcases the potential of non-autoregressive masked generative transformers in this domain.
Character Animation aims to generating character videos from still images through driving signals. Currently diffusion models have become the mainstream in visual generation research owing to their robust generative capabilities. However challenges persist in the realm of image-to-video especially in character animation where temporally maintaining consistency with detailed information from character remains a formidable problem. In this paper we leverage the power of diffusion models and propose a novel framework tailored for character animation. To preserve consistency of intricate appearance features from reference image we design ReferenceNet to merge detail features via spatial attention. To ensure controllability and continuity we introduce an efficient pose guider to direct character's movements and employ an effective temporal modeling approach to ensure smooth inter-frame transitions between video frames. By expanding the training data our approach can animate arbitrary characters yielding superior results in character animation compared to other image-to-video methods. Furthermore we evaluate our method on image animation benchmarks achieving state-of-the-art results.
The fidelity of relighting is bounded by both geometry and appearance representations. For geometry both mesh and volumetric approaches have difficulty modeling intricate structures like 3D hair geometry. For appearance existing relighting models are limited in fidelity and often too slow to render in real-time with high-resolution continuous environments. In this work we present Relightable Gaussian Codec Avatars a method to build high-fidelity relightable head avatars that can be animated to generate novel expressions. Our geometry model based on 3D Gaussians can capture 3D-consistent sub-millimeter details such as hair strands and pores on dynamic face sequences. To support diverse materials of human heads such as the eyes skin and hair in a unified manner we present a novel relightable appearance model based on learnable radiance transfer. Together with global illumination-aware spherical harmonics for the diffuse components we achieve real-time relighting with all-frequency reflections using spherical Gaussians. This appearance model can be efficiently relit under both point light and continuous illumination. We further improve the fidelity of eye reflections and enable explicit gaze control by introducing relightable explicit eye models. Our method outperforms existing approaches without compromising real-time performance. We also demonstrate real-time relighting of avatars on a tethered consumer VR headset showcasing the efficiency and fidelity of our avatars.
Traditional 3D content creation tools empower users to bring their imagination to life by giving them direct control over a scene's geometry appearance motion and camera path. Creating computer-generated videos however is a tedious manual process which can be automated by emerging text-to-video diffusion models. Despite great promise video diffusion models are difficult to control hindering users to apply their creativity rather than amplifying it. To address this challenge we present a novel approach that combines the controllability of dynamic 3D meshes with the expressivity and editability of emerging diffusion models. For this purpose our approach takes an animated low-fidelity rendered mesh as input and injects the ground truth correspondence information obtained from the dynamic mesh into various stages of a pre-trained text-to-image generation model to output high-quality and temporally consistent frames. We demonstrate our approach on various examples where motion can be obtained by animating rigged assets or changing the camera path.
There are five types of trajectory prediction tasks: deterministic stochastic domain adaptation momentary observation and few-shot. These associated tasks are defined by various factors such as the length of input paths data split and pre-processing methods. Interestingly even though they commonly take sequential coordinates of observations as input and infer future paths in the same coordinates as output designing specialized architectures for each task is still necessary. For the other task generality issues can lead to sub-optimal performances. In this paper we propose SingularTrajectory a diffusion-based universal trajectory prediction framework to reduce the performance gap across the five tasks. The core of SingularTrajectory is to unify a variety of human dynamics representations on the associated tasks. To do this we first build a Singular space to project all types of motion patterns from each task into one embedding space. We next propose an adaptive anchor working in the Singular space. Unlike traditional fixed anchor methods that sometimes yield unacceptable paths our adaptive anchor enables correct anchors which are put into a wrong location based on a traversability map. Finally we adopt a diffusion-based predictor to further enhance the prototype paths using a cascaded denoising process. Our unified framework ensures the generality across various benchmark settings such as input modality and trajectory lengths. Extensive experiments on five public benchmarks demonstrate that SingularTrajectory substantially outperforms existing models highlighting its effectiveness in estimating general dynamics of human movements. Code is publicly available at https://github.com/inhwanbae/SingularTrajectory.
Recent innovations on text-to-3D generation have featured Score Distillation Sampling (SDS) which enables the zero-shot learning of implicit 3D models (NeRF) by directly distilling prior knowledge from 2D diffusion models. However current SDS-based models still struggle with intricate text prompts and commonly result in distorted 3D models with unrealistic textures or cross-view inconsistency issues. In this work we introduce a novel Visual Prompt-guided text-to-3D diffusion model (VP3D) that explicitly unleashes the visual appearance knowledge in 2D visual prompt to boost text-to-3D generation. Instead of solely supervising SDS with text prompt VP3D first capitalizes on 2D diffusion model to generate a high-quality image from input text which subsequently acts as visual prompt to strengthen SDS optimization with explicit visual appearance. Meanwhile we couple the SDS optimization with additional differentiable reward function that encourages rendering images of 3D models to better visually align with 2D visual prompt and semantically match with text prompt. Through extensive experiments we show that the 2D Visual Prompt in our VP3D significantly eases the learning of visual appearance of 3D models and thus leads to higher visual fidelity with more detailed textures. It is also appealing in view that when replacing the self-generating visual prompt with a given reference image VP3D is able to trigger a new task of stylized text-to-3D generation. Our project page is available at https://vp3d-cvpr24.github.io.
Extracting keypoint locations from input hand frames known as 3D hand pose estimation is a critical task in various human-computer interaction applications. Essentially the 3D hand pose estimation can be regarded as a 3D point subset generative problem conditioned on input frames. Thanks to the recent significant progress on diffusion-based generative models hand pose estimation can also benefit from the diffusion model to estimate keypoint locations with high quality. However directly deploying the existing diffusion models to solve hand pose estimation is non-trivial since they cannot achieve the complex permutation mapping and precise localization. Based on this motivation this paper proposes HandDiff a diffusion-based hand pose estimation model that iteratively denoises accurate hand pose conditioned on hand-shaped image-point clouds. In order to recover keypoint permutation and accurate location we further introduce joint-wise condition and local detail condition. Experimental results demonstrate that the proposed HandDiff significantly outperforms the existing approaches on four challenging hand pose benchmark datasets. Codes and pre-trained models are publicly available at https://github.com/cwc1260/HandDiff.
This paper proposes ConsistDreamer - a novel framework that lifts 2D diffusion models with 3D awareness and 3D consistency thus enabling high-fidelity instruction-guided scene editing. To overcome the fundamental limitation of missing 3D consistency in 2D diffusion models our key insight is to introduce three synergetic strategies that augment the input of the 2D diffusion model to become 3D-aware and to explicitly enforce 3D consistency during the training process. Specifically we design surrounding views as context-rich input for the 2D diffusion model and generate 3D-consistent structured noise instead of image-independent noise. Moreover we introduce self-supervised consistency-enforcing training within the per-scene editing procedure. Extensive evaluation shows that our ConsistDreamer achieves state-of-the-art performance for instruction-guided scene editing across various scenes and editing instructions particularly in complicated large-scale indoor scenes from ScanNet++ with significantly improved sharpness and fine-grained textures. Notably ConsistDreamer stands as the first work capable of successfully editing complex (e.g. plaid/checkered) patterns.
Personalized text-to-image generation models enable users to create images that depict their individual possessions in diverse scenes finding applications in various domains. To achieve the personalization capability existing methods rely on finetuning a text-to-image foundation model on a user's custom dataset which can be non-trivial for general users resource-intensive and time-consuming. Despite attempts to develope finetuning-free methods their generation quality is much lower compared to their finetuning counterparts. In this paper we propose Joint-Image Diffusion (\jedi) an effective technique for learning a finetuning-free personalization model. Our key idea is to learn the joint distribution of multiple related text-image pairs that share a common subject. To facilitate learning we propose a scalable synthetic dataset generation technique. Once trained our model enables fast and easy personalization at test time by simply using reference images as input during the sampling process. Our approach does not require any expensive optimization process or additional modules and can faithfully preserve the identity represented by any number of reference images. Experimental results show that our model achieves state-of-the-art generation quality both quantitatively and qualitatively significantly outperforming both the prior finetuning-based and finetuning-free personalization baselines.
Current subject-driven image generation methods encounter significant challenges in person-centric image generation. The reason is that they learn the semantic scene and person generation by fine-tuning a common pre-trained diffusion which involves an irreconcilable training imbalance. Precisely to generate realistic persons they need to sufficiently tune the pre-trained model which inevitably causes the model to forget the rich semantic scene prior and makes scene generation over-fit to the training data. Moreover even with sufficient fine-tuning these methods can still not generate high-fidelity persons since joint learning of the scene and person generation also lead to quality compromise. In this paper we propose Face-diffuser an effective collaborative generation pipeline to eliminate the above training imbalance and quality compromise. Specifically we first develop two specialized pre-trained diffusion models i.e. Text-driven Diffusion Model (TDM) and Subject-augmented Diffusion Model (SDM) for scene and person generation respectively. The sampling process is divided into three sequential stages i.e. semantic scene construction subject-scene fusion and subject enhancement. The first and last stages are performed by TDM and SDM respectively. The subject-scene fusion stage that is the collaboration achieved through a novel and highly effective mechanism Saliency-adaptive Noise Fusion (SNF). Specifically it is based on our key observation that there exists a robust link between classifier-free guidance responses and the saliency of generated images. In each time step SNF leverages the unique strengths of each model and allows for the spatial blending of predicted noises from both models automatically in a saliency-aware manner all of which can be seamlessly integrated into the DDIM sampling process. Extensive experiments confirm the impressive effectiveness and robustness of the Face-diffuser in generating high-fidelity person images depicting multiple unseen persons with varying contexts. Code is available at https://github.com/CodeGoat24/Face-diffuser.
The existing facial datasets while having plentiful images at near frontal views lack images with extreme head poses leading to the downgraded performance of deep learning models when dealing with profile or pitched faces. This work aims to address this gap by introducing a novel dataset named Extreme Pose Face High-Quality Dataset (EFHQ) which includes a maximum of 450k high-quality images of faces at extreme poses. To produce such a massive dataset we utilize a novel and meticulous dataset processing pipeline to curate two publicly available datasets VFHQ and CelebV-HQ which contain many high-resolution face videos captured in various settings. Our dataset can complement existing datasets on various facial-related tasks such as facial synthesis with 2D/3D-aware GAN diffusion-based text-to-image face generation and face reenactment. Specifically training with EFHQ helps models generalize well across diverse poses significantly improving performance in scenarios involving extreme views confirmed by extensive experiments. Additionally we utilize EFHQ to define a challenging cross-view face verification benchmark in which the performance of SOTA face recognition models drops 5-37% compared to frontal-to-frontal scenarios aiming to stimulate studies on face recognition under severe pose conditions in the wild.
Computer vision techniques play a central role in the perception stack of autonomous vehicles. Such methods are employed to perceive the vehicle surroundings given sensor data. 3D LiDAR sensors are commonly used to collect sparse 3D point clouds from the scene. However compared to human perception such systems struggle to deduce the unseen parts of the scene given those sparse point clouds. In this matter the scene completion task aims at predicting the gaps in the LiDAR measurements to achieve a more complete scene representation. Given the promising results of recent diffusion models as generative models for images we propose extending them to achieve scene completion from a single 3D LiDAR scan. Previous works used diffusion models over range images extracted from LiDAR data directly applying image-based diffusion methods. Distinctly we propose to directly operate on the points reformulating the noising and denoising diffusion process such that it can efficiently work at scene scale. Together with our approach we propose a regularization loss to stabilize the noise predicted during the denoising process. Our experimental evaluation shows that our method can complete the scene given a single LiDAR scan as input producing a scene with more details compared to state-of-the-art scene completion methods. We believe that our proposed diffusion process formulation can support further research in diffusion models applied to scene-scale point cloud data.
Modern video generation models like Sora have achieved remarkable success in producing high-quality videos. However a significant limitation is their inability to offer interactive control to users a feature that promises to open up unprecedented applications and creativity. In this work we introduce the first solution to equip diffusion-based video generation models with spatio-temporal control. We present Peekaboo a novel masked attention module which seamlessly integrates with current video generation models offering control without the need for additional training or inference overhead. To facilitate future research we also introduce a comprehensive benchmark for interactive video generation. This benchmark offers a standardized framework for the community to assess the efficacy of emerging interactive video generation models. Our extensive qualitative and quantitative assessments reveal that Peekaboo achieves up to a 3.8x improvement in mIoU over baseline models all while maintaining the same latency. Code and benchmark are available on the webpage.
Towards a Simultaneous and Granular Identity-Expression Control in Personalized Face Generation
PDF ↗In human-centric content generation the pre-trained text-to-image models struggle to produce user-wanted portrait images which retain the identity of individuals while exhibiting diverse expressions. This paper introduces our efforts towards personalized face generation. To this end we propose a novel multi-modal face generation framework capable of simultaneous identity-expression control and more fine-grained expression synthesis. Our expression control is so sophisticated that it can be specialized by the fine-grained emotional vocabulary. We devise a novel diffusion model that can undertake the task of simultaneously face swapping and reenactment. Due to the entanglement of identity and expression separately and precisely controlling them within one framework is a nontrivial task thus has not been explored yet. To overcome this we propose several innovative designs in the conditional diffusion model including balancing identity and expression encoder improved midpoint sampling and explicitly background conditioning. Extensive experiments have demonstrated the controllability and scalability of the proposed framework in comparison with state-of-the-art text-to-image face swapping and face reenactment methods.
Existing Siamese or transformer trackers commonly pose visual object tracking as a one-shot detection problem i.e. locating the target object in a single forward evaluation scheme. Despite the demonstrated success these trackers may easily drift towards distractors with similar appearance due to the single forward evaluation scheme lacking self-correction. To address this issue we cast visual tracking as a point set based denoising diffusion process and propose a novel generative learning based tracker dubbed DiffusionTrack. Our DiffusionTrack possesses two appealing properties: 1) It follows a novel noise-to-target tracking paradigm that leverages multiple denoising diffusion steps to localize the target in a dynamic searching manner per frame. 2) It models the diffusion process using a point set representation which can better handle appearance variations for more precise localization. One side benefit is that DiffusionTrack greatly simplifies the post-processing e.g. removing window penalty scheme. Without bells and whistles our DiffusionTrack achieves leading performance over the state-of-the-art trackers and runs in real-time. The code is in https://github.com/VISION-SJTU/DiffusionTrack.
Draw Step by Step: Reconstructing CAD Construction Sequences from Point Clouds via Multimodal Diffusion.
PDF ↗Reconstructing CAD construction sequences from raw 3D geometry serves as an interface between real-world objects and digital designs. In this paper we propose CAD-Diffuser a multimodal diffusion scheme aiming at integrating top-down design paradigm into generative reconstruction. In particular we unify CAD point clouds and CAD construction sequences at the token level guiding our proposed multimodal diffusion strategy to understand and link between the geometry and the design intent concentrated in construction sequences. Leveraging the strong decoding abilities of language models the forward process is modeled as a random walk between the original token and the [MASK] token while the reverse process naturally fits the masked token modeling scheme. A volume-based noise schedule is designed to encourage outline-first generation decomposing the top-down design methodology into a machine-understandable procedure. For tokenizing CAD data of multiple modalities we introduce a tokenizer with a self-supervised face segmentation task to compress local and global geometric information for CAD point clouds and the CAD construction sequence is transformed into a primitive token string. Experimental results show that our CAD-Diffuser can perceive geometric details and the results are more likely to be reused by human designers.
Utilizing pre-trained 2D large-scale generative models recent works are capable of generating high-quality novel views from a single in-the-wild image. However due to the lack of information from multiple views these works encounter difficulties in generating controllable novel views. In this paper we present DreamComposer a flexible and scalable framework that can enhance existing view-aware diffusion models by injecting multi-view conditions. Specifically DreamComposer first uses a view-aware 3D lifting module to obtain 3D representations of an object from multiple views. Then it renders the latent features of the target view from 3D representations with the multi-view feature fusion module. Finally the target view features extracted from multi-view inputs are injected into a pre-trained diffusion model. Experiments show that DreamComposer is compatible with state-of-the-art diffusion models for zero-shot novel view synthesis further enhancing them to generate high-fidelity novel view images with multi-view conditions ready for controllable 3D object reconstruction and various other applications.