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Found 22 papers, 7 papers with code
SegTalker: Segmentation-based Talking Face Generation with Mask-guided Local Editing
Ultimately, we inject the previously generated talking segmentation and style codes into a mask-guided StyleGAN to synthesize video frame.
Landmark-guided Diffusion Model for High-fidelity and Temporally Coherent Talking Head Generation
Audio-driven talking head generation is a significant and challenging task applicable to various fields such as virtual avatars, film production, and online conferences.
Adapter-X: A Novel General Parameter-Efficient Fine-Tuning Framework for Vision
Parameter-efficient fine-tuning (PEFT) has become increasingly important as foundation models continue to grow in both popularity and size.
Multi-Channel Multi-Step Spectrum Prediction Using Transformer and Stacked Bi-LSTM
The advantage of this fusion mode is that it can deeply capture the long-term dependence of multichannel spectrum data.
Co-Speech Gesture Video Generation via Motion-Decoupled Diffusion Model
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.
Syllable based DNN-HMM Cantonese Speech to Text System
This paper reports our work on building up a Cantonese Speech-to-Text (STT) system with a syllable based acoustic model.
Partial Fine-Tuning: A Successor to Full Fine-Tuning for Vision Transformers
We first validate eight manually-defined partial fine-tuning strategies across kinds of datasets and vision transformer architectures, and find that some partial fine-tuning strategies (e. g., ffn only or attention only) can achieve better performance with fewer tuned parameters than full fine-tuning, and selecting appropriate layers is critical to partial fine-tuning.
TransFace: Unit-Based Audio-Visual Speech Synthesizer for Talking Head Translation
However, talking head translation, converting audio-visual speech (i. e., talking head video) from one language into another, still confronts several challenges compared to audio speech: (1) Existing methods invariably rely on cascading, synthesizing via both audio and text, resulting in delays and cascading errors.
Practical Deep Dispersed Watermarking with Synchronization and Fusion
Compared with state-of-the-art approaches, our blind watermarking can achieve better performance: averagely improve the bit accuracy by 5. 28\% and 5. 93\% against single and combined attacks, respectively, and show less file size increment and better visual quality.
UnifiedGesture: A Unified Gesture Synthesis Model for Multiple Skeletons
The automatic co-speech gesture generation draws much attention in computer animation.
The DiffuseStyleGesture+ entry to the GENEA Challenge 2023
In this paper, we introduce the DiffuseStyleGesture+, our solution for the Generation and Evaluation of Non-verbal Behavior for Embodied Agents (GENEA) Challenge 2023, which aims to foster the development of realistic, automated systems for generating conversational gestures.
The RoboDepth Challenge: Methods and Advancements Towards Robust Depth Estimation
In this paper, we summarize the winning solutions from the RoboDepth Challenge -- an academic competition designed to facilitate and advance robust OoD depth estimation.
QPGesture: Quantization-Based and Phase-Guided Motion Matching for Natural Speech-Driven Gesture Generation
Levenshtein distance based on audio quantization as a similarity metric of corresponding speech of gestures helps match more appropriate gestures with speech, and solves the alignment problem of speech and gestures well.
Co-Speech Gesture Synthesis by Reinforcement Learning With Contrastive Pre-Trained Rewards
There is a growing demand of automatically synthesizing co-speech gestures for virtual characters.
The ReprGesture entry to the GENEA Challenge 2022
This paper describes the ReprGesture entry to the Generation and Evaluation of Non-verbal Behaviour for Embodied Agents (GENEA) challenge 2022.
LMTurk: Few-Shot Learners as Crowdsourcing Workers in a Language-Model-as-a-Service Framework
We propose LMTurk, a novel approach that treats few-shot learners as crowdsourcing workers.
A Signal Detection Scheme Based on Deep Learning in OFDM Systems
Simulation results show that the DDLSD scheme outperforms the existing traditional methods in terms of improving channel estimation and signal detection performance.
Fake News Detection Through Multi-Perspective Speaker Profiles
This paper proposes a novel method to incorporate speaker profiles into an attention based LSTM model for fake news detection.
Are Manually Prepared Affective Lexicons Really Useful for Sentiment Analysis
In this paper, we investigate the effectiveness of different affective lexicons through sentiment analysis of phrases.
A Cognition Based Attention Model for Sentiment Analysis
Evaluations show the CBA based method outperforms the state-of-the-art local context based attention methods significantly.
Emotion Corpus Construction Based on Selection from Hashtags
Secondly, a SVM based classifier is used to select the data whose natural labels are consistent with the predicted labels.
