Rule-based lip-syncing algorithm for virtual character in voice chatbot

Virtual characters changed the way we interact with computers. The underlying key for a believable virtual character is accurate synchronization between the visual (lip movements) and the audio (speech) in real-time. This work develops a 3D model for the virtual character and implements the rule-based lip-syncing algorithm for the virtual character's lip movements. We use the Jacob voice chatbot as the platform for the design and implementation of the virtual character. Thus, audio-driven articulation and manual mapping methods are considered suitable for real-time applications such as Jacob. We evaluate the proposed virtual character using hedonic motivation system adoption model (HMSAM) with 70 users. The HMSAM results for the behavioral intention to use is 91.74%, and the immersion is 72.95%. The average score for all aspects of the HMSAM is 85.50%. The rule-based lip-syncing algorithm accurately synchronizes the lip movements with the Jacob voice chatbot's speech in real-time

Instruct-NeuralTalker: Editing Audio-Driven Talking Radiance Fields with Instructions

Recent neural talking radiance field methods have shown great success in photorealistic audio-driven talking face synthesis. In this paper, we propose a novel interactive framework that utilizes human instructions to edit such implicit neural representations to achieve real-time personalized talking face generation. Given a short speech video, we first build an efficient talking radiance field, and then apply the latest conditional diffusion model for image editing based on the given instructions and guiding implicit representation optimization towards the editing target. To ensure audio-lip synchronization during the editing process, we propose an iterative dataset updating strategy and utilize a lip-edge loss to constrain changes in the lip region. We also introduce a lightweight refinement network for complementing image details and achieving controllable detail generation in the final rendered image. Our method also enables real-time rendering at up to 30FPS on consumer hardware. Multiple metrics and user verification show that our approach provides a significant improvement in rendering quality compared to state-of-the-art methods.Comment: 11 pages, 8 figure

Hierarchical Cross-Modal Talking Face Generationwith Dynamic Pixel-Wise Loss

We devise a cascade GAN approach to generate talking face video, which is robust to different face shapes, view angles, facial characteristics, and noisy audio conditions. Instead of learning a direct mapping from audio to video frames, we propose first to transfer audio to high-level structure, i.e., the facial landmarks, and then to generate video frames conditioned on the landmarks. Compared to a direct audio-to-image approach, our cascade approach avoids fitting spurious correlations between audiovisual signals that are irrelevant to the speech content. We, humans, are sensitive to temporal discontinuities and subtle artifacts in video. To avoid those pixel jittering problems and to enforce the network to focus on audiovisual-correlated regions, we propose a novel dynamically adjustable pixel-wise loss with an attention mechanism. Furthermore, to generate a sharper image with well-synchronized facial movements, we propose a novel regression-based discriminator structure, which considers sequence-level information along with frame-level information. Thoughtful experiments on several datasets and real-world samples demonstrate significantly better results obtained by our method than the state-of-the-art methods in both quantitative and qualitative comparisons

Talking Face Generation by Adversarially Disentangled Audio-Visual Representation

Talking face generation aims to synthesize a sequence of face images that correspond to a clip of speech. This is a challenging task because face appearance variation and semantics of speech are coupled together in the subtle movements of the talking face regions. Existing works either construct specific face appearance model on specific subjects or model the transformation between lip motion and speech. In this work, we integrate both aspects and enable arbitrary-subject talking face generation by learning disentangled audio-visual representation. We find that the talking face sequence is actually a composition of both subject-related information and speech-related information. These two spaces are then explicitly disentangled through a novel associative-and-adversarial training process. This disentangled representation has an advantage where both audio and video can serve as inputs for generation. Extensive experiments show that the proposed approach generates realistic talking face sequences on arbitrary subjects with much clearer lip motion patterns than previous work. We also demonstrate the learned audio-visual representation is extremely useful for the tasks of automatic lip reading and audio-video retrieval.Comment: AAAI Conference on Artificial Intelligence (AAAI 2019) Oral Presentation. Code, models, and video results are available on our webpage: https://liuziwei7.github.io/projects/TalkingFace.htm