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Talking face generation has historically struggled to produce head movements and natural facial expressions without guidance from additional reference videos. Recent developments in diffusion-based generative models allow for more realistic…

Computer Vision and Pattern Recognition · Computer Science 2023-08-01 Michał Stypułkowski , Konstantinos Vougioukas , Sen He , Maciej Zięba , Stavros Petridis , Maja Pantic

The advent of stereoscopic videos has opened new horizons in multimedia, particularly in extended reality (XR) and virtual reality (VR) applications, where immersive content captivates audiences across various platforms. Despite its growing…

Computer Vision and Pattern Recognition · Computer Science 2025-06-17 Qiao Jin , Xiaodong Chen , Wu Liu , Tao Mei , Yongdong Zhang

Diffusion models have shown impressive potential on talking head generation. While plausible appearance and talking effect are achieved, these methods still suffer from temporal, 3D or expression inconsistency due to the error accumulation…

Computer Vision and Pattern Recognition · Computer Science 2024-11-26 Haijie Yang , Zhenyu Zhang , Hao Tang , Jianjun Qian , Jian Yang

Diffusion models have recently advanced photorealistic human synthesis, although practical talking-head generation (THG) remains constrained by high inference latency, temporal instability such as flicker and identity drift, and imperfect…

Computer Vision and Pattern Recognition · Computer Science 2026-03-09 Soumya Mazumdar , Vineet Kumar Rakesh

Talking head generation with arbitrary identities and speech audio remains a crucial problem in the realm of the virtual metaverse. Recently, diffusion models have become a popular generative technique in this field with their strong…

Graphics · Computer Science 2025-08-11 Xinyang Li , Gen Li , Zhihui Lin , Yichen Qian , GongXin Yao , Weinan Jia , Aowen Wang , Weihua Chen , Fan Wang

We propose a novel 3D-aware diffusion-based method for generating photorealistic talking head videos directly from a single identity image and explicit control signals (e.g., expressions). Our method generates Multiplane Images (MPIs) that…

Computer Vision and Pattern Recognition · Computer Science 2025-04-30 Yuan Li , Ziqian Bai , Feitong Tan , Zhaopeng Cui , Sean Fanello , Yinda Zhang

As a combination of visual and audio signals, video is inherently multi-modal. However, existing video generation methods are primarily intended for the synthesis of visual frames, whereas audio signals in realistic videos are disregarded.…

Computer Vision and Pattern Recognition · Computer Science 2023-06-16 Jiawei Liu , Weining Wang , Sihan Chen , Xinxin Zhu , Jing Liu

The generation of stylistic 3D facial animations driven by speech presents a significant challenge as it requires learning a many-to-many mapping between speech, style, and the corresponding natural facial motion. However, existing methods…

Computer Vision and Pattern Recognition · Computer Science 2024-05-15 Zhiyao Sun , Tian Lv , Sheng Ye , Matthieu Lin , Jenny Sheng , Yu-Hui Wen , Minjing Yu , Yong-Jin Liu

Talking Head Generation aims at synthesizing natural-looking talking videos from speech and a single portrait image. Previous 3D talking head generation methods have relied on domain-specific heuristics such as warping-based facial motion…

Computer Vision and Pattern Recognition · Computer Science 2026-01-27 Tong Shi , Melonie de Almeida , Daniela Ivanova , Nicolas Pugeault , Paul Henderson

Portrait animation aims to generate photo-realistic videos from a single source image by reenacting the expression and pose from a driving video. While early methods relied on 3D morphable models or feature warping techniques, they often…

Computer Vision and Pattern Recognition · Computer Science 2025-09-23 Mallikarjun B. R. , Fei Yin , Vikram Voleti , Nikita Drobyshev , Maksim Lapin , Aaryaman Vasishta , Varun Jampani

Speech-driven talking heads have recently emerged and enable interactive avatars. However, real-world applications are limited, as current methods achieve high visual fidelity but slow or fast yet temporally unstable. Diffusion methods…

Computer Vision and Pattern Recognition · Computer Science 2025-12-12 Madhav Agarwal , Mingtian Zhang , Laura Sevilla-Lara , Steven McDonagh

Different people speak with diverse personalized speaking styles. Although existing one-shot talking head methods have made significant progress in lip sync, natural facial expressions, and stable head motions, they still cannot generate…

Computer Vision and Pattern Recognition · Computer Science 2023-06-13 Yifeng Ma , Suzhen Wang , Zhipeng Hu , Changjie Fan , Tangjie Lv , Yu Ding , Zhidong Deng , Xin Yu

State-of-the-art Text-to-Video (T2V) diffusion models can generate visually impressive results, yet they still frequently fail to compose complex scenes or follow logical temporal instructions. In this paper, we argue that many errors,…

Computer Vision and Pattern Recognition · Computer Science 2026-03-26 Mariam Hassan , Bastien Van Delft , Wuyang Li , Alexandre Alahi

Sounding Video Generation (SVG) is an audio-video joint generation task challenged by high-dimensional signal spaces, distinct data formats, and different patterns of content information. To address these issues, we introduce a novel…

Computer Vision and Pattern Recognition · Computer Science 2024-10-03 Mingzhen Sun , Weining Wang , Yanyuan Qiao , Jiahui Sun , Zihan Qin , Longteng Guo , Xinxin Zhu , Jing Liu

Audio-driven talking head synthesis strives to generate lifelike video portraits from provided audio. The diffusion model, recognized for its superior quality and robust generalization, has been explored for this task. However, establishing…

Multimedia · Computer Science 2024-09-17 Fa-Ting Hong , Yunfei Liu , Yu Li , Changyin Zhou , Fei Yu , Dan Xu

Joint audio-video generation models have shown that unified generation yields stronger cross-modal coherence than cascaded approaches. However, existing models couple modalities throughout denoising via pervasive attention, treating…

Computer Vision and Pattern Recognition · Computer Science 2026-04-28 Zhen Ye , Xu Tan , Aoxiong Yin , Hongzhan Lin , Guangyan Zhang , Peiwen Sun , Yiming Li , Chi-Min Chan , Wei Ye , Shikun Zhang , Wei Xue

Leveraging text, images, structure maps, or motion trajectories as conditional guidance, diffusion models have achieved great success in automated and high-quality video generation. However, generating smooth and rational transition videos…

Computer Vision and Pattern Recognition · Computer Science 2025-08-05 Zuhao Yang , Jiahui Zhang , Yingchen Yu , Shijian Lu , Song Bai

Despite previous success in generating audio-driven talking heads, most of the previous studies focus on the correlation between speech content and the mouth shape. Facial emotion, which is one of the most important features on natural…

Computer Vision and Pattern Recognition · Computer Science 2021-05-21 Xinya Ji , Hang Zhou , Kaisiyuan Wang , Wayne Wu , Chen Change Loy , Xun Cao , Feng Xu

Diffusion models have revolutionized the field of talking head generation, yet still face challenges in expressiveness, controllability, and stability in long-time generation. In this research, we propose an EmotiveTalk framework to address…

Computer Vision and Pattern Recognition · Computer Science 2024-12-17 Haotian Wang , Yuzhe Weng , Yueyan Li , Zilu Guo , Jun Du , Shutong Niu , Jiefeng Ma , Shan He , Xiaoyan Wu , Qiming Hu , Bing Yin , Cong Liu , Qingfeng Liu

The task of audio-driven portrait animation involves generating a talking head video using an identity image and an audio track of speech. While many existing approaches focus on lip synchronization and video quality, few tackle the…

Computer Vision and Pattern Recognition · Computer Science 2024-09-12 Jian Zhang , Weijian Mai , Zhijun Zhang