Audio-driven talking head generation has drawn much attention in recent years, and many efforts have been made in lip-sync, facial motion, head pose generation, and video quality. However, no model has yet led or tied on all these metrics due to the one-to-many mapping between audio and motion. In this paper, we propose VividTalk, a two-stage generic framework that supports generating high-visual quality talking head videos with all the above properties. Specifically, in the first stage, we map the audio to mesh by learning two motions, including non-rigid facial motion and rigid head motion. For facial motion, both blendshape and vertex are adopted as the intermediate representation to maximize the representation ability of the model. For head motion, a novel learnable head pose codebook with a two-phase training mechanism is proposed. In the second stage, we proposed a dual branch motion-vae and a generator to transform the meshes into dense motion and synthesize high-quality video frame-by-frame. Extensive experiments show that the proposed VividTalk can generate high-visual quality talking head videos with lip-sync and realistic enhanced by a large margin, and outperforms previous state-of-the-art works in objective and subjective comparisons. The code will be publicly released upon publication.

The widespread adoption of face recognition has raised privacy concerns regarding the collection and use of facial data. To address this, researchers have explored "unlearnable examples" by adding imperceptible perturbations during model training to prevent the model from learning target features. However, current methods are inefficient and cannot guarantee transferability and robustness at the same time, causing impracticality in the real world. To remedy it, we introduce Side-information Guided Generative Unlearnable Examples (Segue). Using a once-trained multiple-used model to generate perturbations, Segue avoids the time-consuming gradient-based approach. To improve transferability, we introduce side information such as true or pseudo labels, which are inherently consistent across different scenarios. For robustness enhancement, a distortion layer is integrated into the training pipeline. Experiments show Segue is 1000× faster than previous methods, transferable across datasets and models, and resistant to JPEG compression, adversarial training, and standard augmentations.

In the realm of image generation, the quest for realism and customization has never been more pressing. While existing methods like concept sliders have made strides, they often falter when it comes to non-AIGC images, particularly images captured in real-world settings. To bridge this gap, we introduce Beyond Sliders, an innovative framework that integrates GANs and diffusion models to facilitate sophisticated image manipulation across diverse image categories. Improved upon concept sliders, our method refines the image through fine-grained guidance—both textual and visual—in an adversarial manner, leading to a marked enhancement in image quality and realism. Extensive experimental validation confirms the robustness and versatility of Beyond Sliders across a spectrum of applications.

Removing unwanted concepts from large-scale text-to-image (T2I) diffusion models while maintaining their overall generative quality remains an open challenge. This difficulty is especially pronounced in emerging paradigms, such as Stable Diffusion (SD) v3 and Flux, which incorporate flow matching and transformer-based architectures. These advancements limit the transferability of existing concept-erasure techniques that were originally designed for the previous T2I paradigm (e.g., SD v1.4). In this work, we introduce EraseAnything, the first method specifically developed to address concept erasure within the latest flow-based T2I framework. We formulate concept erasure as a bi-level optimization problem, employing LoRA-based parameter tuning and an attention map regularizer to selectively suppress undesirable activations. Furthermore, we propose a self-contrastive learning strategy to ensure that removing unwanted concepts does not inadvertently harm performance on unrelated ones. Experimental results demonstrate that EraseAnything successfully fills the research gap left by earlier methods in this new T2I paradigm, achieving state-of-the-art performance across a wide range of concept erasure tasks.

Fabricating and designing 3D garments has become extremely demanding with the increasing need for synthesizing realistic dressed persons for a variety of applications, e.g. 3D virtual try-on, digitalization of 2D clothes into 3D apparel, and cloth animation. It thus necessitates a simple and straightforward pipeline to obtain high-quality texture from simple input, such as 2D reference images. Since traditional warping-based texture generation methods require a significant number of control points to be manually selected for each type of garment, which can be a time-consuming and tedious process. We propose a novel method, called Cloth2Tex, which eliminates the human burden in this process. Cloth2Tex is a self-supervised method that generates texture maps with reasonable layout and structural consistency. Another key feature of Cloth2Tex is that it can be used to support high-fidelity texture inpainting. This is done by combining Cloth2Tex with a prevailing latent diffusion model. We evaluate our approach both qualitatively and quantitatively and demonstrate that Cloth2Tex can generate high-quality texture maps and achieve the best visual effects in comparison to other methods. For more details and animated results, please see https://tomguluson92. github.io/projects/cloth2tex/.

As for human avatar reconstruction, contemporary techniques commonly necessitate the acquisition of costly data and struggle to achieve satisfactory results from a small number of casual images. In this paper, we investigate this task from a few-shot unconstrained photo album. The reconstruction of human avatars from such data sources is challenging because of limited data amount and dynamic articulated poses. For handling dynamic data, we integrate a skinning mechanism with deep marching tetrahedra (DMTet) to form a drivable tetrahedral representation, which drives arbitrary mesh topologies generated by the DMTet for the adaptation of unconstrained images. To effectively mine instructive information from few-shot data, we devise a two-phase optimization method with few-shot reference and few-shot guidance. The former focuses on aligning avatar identity with reference images, while the latter aims to generate plausible appearances for unseen regions. Overall, our framework, called HaveFun, can undertake avatar reconstruction, rendering, and animation. Extensive experiments on our developed benchmarks demonstrate that HaveFun exhibits substantially superior performance in reconstructing the human body and hand.

Abstract This paper studies compositional 3D-aware image synthesis for both single-object and multi-object scenes. We observe that two challenges remain in this field: existing approaches (1) lack geometry constraints and thus compromise the multi-view consistency of the single object, and (2) can not scale to multi-object scenes with complex backgrounds. To address these challenges coherently, we propose multi-view consistent generative adversarial networks (MVCGAN) for compositional 3D-aware image synthesis. First, we build the geometry constraints on the single object by leveraging the underlying 3D information. Specifically, we enforce the photometric consistency between pairs of views, encouraging the model to learn the inherent 3D shape. Second, we adapt MVCGAN to multi-object scenarios. In particular, we formulate the multi-object scene generation as a “decompose and compose” process. During training, we adopt the top-down strategy to decompose training images into objects and backgrounds. When rendering, we deploy a reverse bottom-up manner by composing the generated objects and background into the holistic scene. Extensive experiments on both single-object and multi-object datasets show that the proposed method achieves competitive performance for 3D-aware image synthesis.

Face swapping has drawn a lot of attention for its compelling performance. However, current deepfake methods suffer the effects of obscure workflow and poor performance. To solve these problems, we present DeepFaceLab, the current dominant deepfake framework for practical face-swapping. It provides the necessary tools as well as an easy-to-use way to conduct high-quality face-swapping. It also offers a flexible and loose coupling structure for people who need to strengthen their pipeline with other features without writing complicated boilerplate code. We detail the principles that drive the implementation of DeepFaceLab and introduce its pipeline. DeepFaceLab could achieve cinema-level results with high fidelity as our supplemental video shows. We also demonstrate the advantage of our system by comparing our approach with other face-swapping methods. Deepfake defense not only requires the research of detection but also requires the efforts of generation methods. As for a popular and practical toolkit, we encourage users to promote harmless deepfake-entertainment content on social media, reminding the public of the existence of deepfake when they are looking for entertainment.

3D-aware image synthesis aims to generate images of objects from multiple views by learning a 3D representation. However, one key challenge remains: existing approaches lack geometry constraints, hence usually fail to generate multi-view consistent images. To address this challenge, we propose Multi-View Consistent Generative Adversarial Networks (MVCGAN) for high-quality 3D-aware image synthesis with geometry constraints. By leveraging the underlying 3D geometry information of generated images, i.e., depth and camera transformation matrix, we explicitly establish stereo correspondence between views to perform multi-view joint optimization. In particular, we enforce the photometric consistency between pairs of views and integrate a stereo mixup mechanism into the training process, encouraging the model to reason about the correct 3D shape. Besides, we design a two-stage training strategy with feature-level multi-view joint optimization to improve the image quality. Extensive experiments on three datasets demonstrate that MVCGAN achieves the state-of-the-art performance for 3D-aware image synthesis.

Current methods for 3D hand pose estimation fail to generalize well to in-the-wild new scenarios due to varying camera viewpoints, self-occlusions, and complex environments. To address this problem, we propose CycleHand to improve the generalization ability of the model in a self-supervised manner. Our motivation is based on an observation: if one globally rotates the whole hand and reversely rotates it back, the estimated 3D poses of fingers should keep consistent before and after the rotation because the wrist-relative hand poses stay unchanged during global 3D rotation. Hence, we propose arbitrary-rotation self-supervised consistency learning to improve the model's robustness for varying viewpoints. Another innovation of CycleHand is that we propose a high-fidelity texture map to render the photorealistic rotated hand with different lighting conditions, backgrounds, and skin tones to further enhance the effectiveness of our self-supervised task. To reduce the potential negative effects brought by the domain shift of synthetic images, we use the idea of contrastive learning to learn a synthetic-real consistent feature extractor in extracting domain-irrelevant hand representations. Experiments show that CycleHand can largely improve the hand pose estimation performance in both canonical datasets and real-world applications.

Manipulating latent code in generative adversarial networks (GANs) for facial image synthesis mainly focuses on continuous attribute synthesis (e.g., age, pose and emotion), while discrete attribute synthesis (like face mask and eyeglasses) receives less attention. Directly applying existing works to facial discrete attributes may cause inaccurate results. In this work, we propose an innovative framework to tackle challenging facial discrete attribute synthesis via semantic decomposing, dubbed SD-GAN. To be concrete, we explicitly decompose the discrete attribute representation into two components, i.e. the semantic prior basis and offset latent representation. The semantic prior basis shows an initializing direction for manipulating face representation in the latent space. The offset latent presentation obtained by 3D-aware semantic fusion network is proposed to adjust prior basis. In addition, the fusion network integrates 3D embedding for better identity preservation and discrete attribute synthesis. The combination of prior basis and offset latent representation enable our method to synthesize photo-realistic face images with discrete attributes. Notably, we construct a large and valuable dataset MEGN (Face Mask and Eyeglasses images crawled from Google and Naver) for completing the lack of discrete attributes in the existing dataset. Extensive qualitative and quantitative experiments demonstrate the state-of-the-art performance of our method. Our code is available at an anonymous website: https://github.com/MontaEllis/SD-GAN.

Hand, the bearer of human productivity and intelligence, is receiving much attention due to the recent fever of digital twins. Among different hand morphable models, MANO has been widely used in vision and graphics community. However, MANO disregards textures and accessories, which largely limits its power to synthesize photorealistic hand data. In this paper, we extend MANO with Diverse Accessories and Rich Textures, namely DART. DART is composed of 50 daily 3D accessories which varies in appearance and shape, and 325 hand-crafted 2D texture maps covers different kinds of blemishes or make-ups. Unity GUI is also provided to generate synthetic hand data with user-defined settings, e.g., pose, camera, background, lighting, textures, and accessories. Finally, we release DARTset, which contains large-scale (800K), high-fidelity synthetic hand images, paired with perfect-aligned 3D labels. Experiments demonstrate its superiority in diversity. As a complement to existing hand datasets, DARTset boosts the generalization in both hand pose estimation and mesh recovery tasks. Raw ingredients (textures, accessories), Unity GUI, source code and DARTset are publicly available at dart2022.github.io.

Virtual character has been widely adopted in many areas, such as virtual assistant, virtual customer service, robotics and etc. In this paper, we focus on its application in e-commerce live streaming. Particularly, we propose a virtual character generation and animation system that supports e-commerce live streaming with virtual characters as anchors. The system offers a virtual character face generation tool based on a weakly supervised 3D face reconstruction method. The method takes a single photo as input and generates a 3D face model with both similarity and aesthetics considered. It does not require 3D face annotation data due to the assist of differentiable neural rendering technique which seamlessly integrates rendering into a deep learning based 3D face reconstruction framework. Moreover, the system provides two animation approaches which support two different ways of live stream respectively. The first approach is based on real-time motion capture. An actor's performance is captured in real-time via a monocular camera, and then utilized for animating a virtual anchor. The second approach is text driven animation, in which the human-like animation is automatically generated based on a text script. The relationship between text script and animation is learned based on the training data which can be accumulated via the motion capture based animation. To our best knowledge, the presented work is the first sophisticated virtual character generation and animation system that is designed for e-commerce live streaming and actually deployed on an online shopping platform with millions of daily audiences.

Pedestrian attribute analysis, which is a vital component in the intelligent video surveillance area, can facilitate person retrieval, searching and indexing. However, the resolution of the surveillance video is relatively low. The ability of neural networks to learn abstract features (age, gender) and solid features (hat, backpack, clothes) on low-resolution images is limited due to the inability to accurately locate the human body and the disturbance of background noise. In this study, we adopt a Semantic Parsing Technique (SPT) as a pedestrian extractor to localize informative regions of human effectively. Experiments are conducted on the RAP, PA-100k and other datasets, we show that our proposed SAFE-Net is capable of capturing abstract features and solid features, and produces competitive performance with the state-of-the-art methods.