Cross Initialization for Personalized Text-to-Image Generation

Recently, there has been a surge in face personalization techniques, benefiting from the advanced capabilities of pretrained text-to-image diffusion models. Among these, a notable method is Textual Inversion, which generates personalized images by inverting given images into textual embeddings. However, methods based on Textual Inversion still struggle with balancing the trade-off between reconstruction quality and editability. In this study, we examine this issue through the lens of initialization. Upon closely examining traditional initialization methods, we identified a significant disparity between the initial and learned embeddings in terms of both scale and orientation. The scale of the learned embedding can be up to 100 times greater than that of the initial embedding. Such a significant change in the embedding could increase the risk of overfitting, thereby compromising the editability. Driven by this observation, we introduce a novel initialization method, termed Cross Initialization, that significantly narrows the gap between the initial and learned embeddings. This method not only improves both reconstruction and editability but also reduces the optimization steps from 5000 to 320. Furthermore, we apply a regularization term to keep the learned embedding close to the initial embedding. We show that when combined with Cross Initialization, this regularization term can effectively improve editability. We provide comprehensive empirical evidence to demonstrate the superior performance of our method compared to the baseline methods. Notably, in our experiments, Cross Initialization is the only method that successfully edits an individual's facial expression. Additionally, a fast version of our method allows for capturing an input image in roughly 26 seconds, while surpassing the baseline methods in terms of both reconstruction and editability. Code will be made publicly available.

Synthesizing Black-box Anti-forensics DeepFakes with High Visual Quality

DeepFake, an AI technology for creating facial forgeries, has garnered global attention. Amid such circumstances, forensics researchers focus on developing defensive algorithms to counter these threats. In contrast, there are techniques developed for enhancing the aggressiveness of DeepFake, e.g., through anti-forensics attacks, to disrupt forensic detectors. However, such attacks often sacrifice image visual quality for improved undetectability. To address this issue, we propose a method to generate novel adversarial sharpening masks for launching black-box anti-forensics attacks. Unlike many existing arts, with such perturbations injected, DeepFakes could achieve high anti-forensics performance while exhibiting pleasant sharpening visual effects. After experimental evaluations, we prove that the proposed method could successfully disrupt the state-of-the-art DeepFake detectors. Besides, compared with the images processed by existing DeepFake anti-forensics methods, the visual qualities of anti-forensics DeepFakes rendered by the proposed method are significantly refined.

UVAGaze: Unsupervised 1-to-2 Views Adaptation for Gaze Estimation

Gaze estimation has become a subject of growing interest in recent research. Most of the current methods rely on single-view facial images as input. Yet, it is hard for these approaches to handle large head angles, leading to potential inaccuracies in the estimation. To address this issue, adding a second-view camera can help better capture eye appearance. However, existing multi-view methods have two limitations. 1) They require multi-view annotations for training, which are expensive. 2) More importantly, during testing, the exact positions of the multiple cameras must be known and match those used in training, which limits the application scenario. To address these challenges, we propose a novel 1-view-to-2-views (1-to-2 views) adaptation solution in this paper, the Unsupervised 1-to-2 Views Adaptation framework for Gaze estimation (UVAGaze). Our method adapts a traditional single-view gaze estimator for flexibly placed dual cameras. Here, the "flexibly" means we place the dual cameras in arbitrary places regardless of the training data, without knowing their extrinsic parameters. Specifically, the UVAGaze builds a dual-view mutual supervision adaptation strategy, which takes advantage of the intrinsic consistency of gaze directions between both views. In this way, our method can not only benefit from common single-view pre-training, but also achieve more advanced dual-view gaze estimation. The experimental results show that a single-view estimator, when adapted for dual views, can achieve much higher accuracy, especially in cross-dataset settings, with a substantial improvement of 47.0%. Project page: https://github.com/MickeyLLG/UVAGaze.

AgentAvatar: Disentangling Planning, Driving and Rendering for Photorealistic Avatar Agents

In this study, our goal is to create interactive avatar agents that can autonomously plan and animate nuanced facial movements realistically, from both visual and behavioral perspectives. Given high-level inputs about the environment and agent profile, our framework harnesses LLMs to produce a series of detailed text descriptions of the avatar agents' facial motions. These descriptions are then processed by our task-agnostic driving engine into motion token sequences, which are subsequently converted into continuous motion embeddings that are further consumed by our standalone neural-based renderer to generate the final photorealistic avatar animations. These streamlined processes allow our framework to adapt to a variety of non-verbal avatar interactions, both monadic and dyadic. Our extensive study, which includes experiments on both newly compiled and existing datasets featuring two types of agents -- one capable of monadic interaction with the environment, and the other designed for dyadic conversation -- validates the effectiveness and versatility of our approach. To our knowledge, we advanced a leap step by combining LLMs and neural rendering for generalized non-verbal prediction and photo-realistic rendering of avatar agents.

Multimodal Group Emotion Recognition In-the-wild Using Privacy-Compliant Features

This paper explores privacy-compliant group-level emotion recognition ''in-the-wild'' within the EmotiW Challenge 2023. Group-level emotion recognition can be useful in many fields including social robotics, conversational agents, e-coaching and learning analytics. This research imposes itself using only global features avoiding individual ones, i.e. all features that can be used to identify or track people in videos (facial landmarks, body poses, audio diarization, etc.). The proposed multimodal model is composed of a video and an audio branches with a cross-attention between modalities. The video branch is based on a fine-tuned ViT architecture. The audio branch extracts Mel-spectrograms and feed them through CNN blocks into a transformer encoder. Our training paradigm includes a generated synthetic dataset to increase the sensitivity of our model on facial expression within the image in a data-driven way. The extensive experiments show the significance of our methodology. Our privacy-compliant proposal performs fairly on the EmotiW challenge, with 79.24% and 75.13% of accuracy respectively on validation and test set for the best models. Noticeably, our findings highlight that it is possible to reach this accuracy level with privacy-compliant features using only 5 frames uniformly distributed on the video.

CoIE: Chain-of-Instruct Editing for Multi-Attribute Face Manipulation

Current text-to-image editing models often encounter challenges with smoothly manipulating multiple attributes using a single instruction. Taking inspiration from the Chain-of-Thought prompting technique utilized in language models, we present an innovative concept known as Chain-of-Instruct Editing (CoIE), which enhances the capabilities of these models through step-by-step editing using a series of instructions. In particular, in the context of face manipulation, we leverage the contextual learning abilities of a pretrained Large Language Model (LLM), such as GPT-4, to generate a sequence of instructions from the original input, utilizing a purpose-designed 1-shot template. To further improve the precision of each editing step, we conduct fine-tuning on the editing models using our self-constructed instruction-guided face editing dataset, Instruct-CelebA. And additionally, we incorporate a super-resolution module to mitigate the adverse effects of editability and quality degradation. Experimental results across various challenging cases confirm the significant boost in multi-attribute facial image manipulation using chain-of-instruct editing. This is evident in enhanced editing success rates, measured by CLIPSim and Coverage metrics, improved by 17.86% and 85.45% respectively, and heightened controllability indicated by Preserve L1 and Quality metrics, improved by 11.58% and 4.93% respectively.

Automatic Tooth Arrangement with Joint Features of Point and Mesh Representations via Diffusion Probabilistic Models

Tooth arrangement is a crucial step in orthodontics treatment, in which aligning teeth could improve overall well-being, enhance facial aesthetics, and boost self-confidence. To improve the efficiency of tooth arrangement and minimize errors associated with unreasonable designs by inexperienced practitioners, some deep learning-based tooth arrangement methods have been proposed. Currently, most existing approaches employ MLPs to model the nonlinear relationship between tooth features and transformation matrices to achieve tooth arrangement automatically. However, the limited datasets (which to our knowledge, have not been made public) collected from clinical practice constrain the applicability of existing methods, making them inadequate for addressing diverse malocclusion issues. To address this challenge, we propose a general tooth arrangement neural network based on the diffusion probabilistic model. Conditioned on the features extracted from the dental model, the diffusion probabilistic model can learn the distribution of teeth transformation matrices from malocclusion to normal occlusion by gradually denoising from a random variable, thus more adeptly managing real orthodontic data. To take full advantage of effective features, we exploit both mesh and point cloud representations by designing different encoding networks to extract the tooth (local) and jaw (global) features, respectively. In addition to traditional metrics ADD, PA-ADD, CSA, and ME_{rot}, we propose a new evaluation metric based on dental arch curves to judge whether the generated teeth meet the individual normal occlusion. Experimental results demonstrate that our proposed method achieves state-of-the-art tooth alignment results and satisfactory occlusal relationships between dental arches. We will publish the code and dataset.

A Powerful Face Preprocessing For Robust Kinship Verification based Tensor Analyses

Kinship verification using facial photographs captured in the wild is difficult area of research in the science of computer vision. It might be used for a variety of applications, including image annotation and searching for missing children, etc. The largest challenge to kinship verification in practice is the fact that parent and child photos frequently differ significantly from one another. How to effectively respond to such a challenge is important improving the efficiency of kinship verification. For this purpose, we introduce a system to check relatedness that starts with a pair of face images of a child and a parent, after which it is revealed whether two people are related or not. The first step in our approach is face preprocessing with two methods, a Retinex filter and an ellipse mask, then a feature extraction step based on hist-Gabor wavelets, which is used before an efficient dimensionality reduction method called TXQDA. Finally, determine if there is a relationship. By using Cornell KinFace benchmark database, we ran a number of tests to show the efficacy of our strategy. Our findings show that, in comparison to other strategies currently in use, our system is robust.

A Dual-way Enhanced Framework from Text Matching Point of View for Multimodal Entity Linking

Multimodal Entity Linking (MEL) aims at linking ambiguous mentions with multimodal information to entity in Knowledge Graph (KG) such as Wikipedia, which plays a key role in many applications. However, existing methods suffer from shortcomings, including modality impurity such as noise in raw image and ambiguous textual entity representation, which puts obstacles to MEL. We formulate multimodal entity linking as a neural text matching problem where each multimodal information (text and image) is treated as a query, and the model learns the mapping from each query to the relevant entity from candidate entities. This paper introduces a dual-way enhanced (DWE) framework for MEL: (1) our model refines queries with multimodal data and addresses semantic gaps using cross-modal enhancers between text and image information. Besides, DWE innovatively leverages fine-grained image attributes, including facial characteristic and scene feature, to enhance and refine visual features. (2)By using Wikipedia descriptions, DWE enriches entity semantics and obtains more comprehensive textual representation, which reduces between textual representation and the entities in KG. Extensive experiments on three public benchmarks demonstrate that our method achieves state-of-the-art (SOTA) performance, indicating the superiority of our model. The code is released on https://github.com/season1blue/DWE

Effective Adapter for Face Recognition in the Wild

In this paper, we tackle the challenge of face recognition in the wild, where images often suffer from low quality and real-world distortions. Traditional heuristic approaches-either training models directly on these degraded images or their enhanced counterparts using face restoration techniques-have proven ineffective, primarily due to the degradation of facial features and the discrepancy in image domains. To overcome these issues, we propose an effective adapter for augmenting existing face recognition models trained on high-quality facial datasets. The key of our adapter is to process both the unrefined and the enhanced images by two similar structures where one is fixed and the other trainable. Such design can confer two benefits. First, the dual-input system minimizes the domain gap while providing varied perspectives for the face recognition model, where the enhanced image can be regarded as a complex non-linear transformation of the original one by the restoration model. Second, both two similar structures can be initialized by the pre-trained models without dropping the past knowledge. The extensive experiments in zero-shot settings show the effectiveness of our method by surpassing baselines of about 3%, 4%, and 7% in three datasets. Our code will be publicly available at https://github.com/liuyunhaozz/FaceAdapter/.