research-article

TACR-Net: Editing on Deep Video and Voice Portraits

Authors:

Jia-Xuan BaiAuthors Info & Claims

MM '21: Proceedings of the 29th ACM International Conference on Multimedia

Pages 478 - 486

https://doi.org/10.1145/3474085.3475196

Published: 17 October 2021 Publication History

Abstract

Utilizing an arbitrary speech clip to edit the mouth of the portrait in the target video is a novel yet challenging task. Despite impressive results have been achieved, there are still three limitations in the existing methods: 1) since the acoustic features are not completely decoupled from person identity, there is no global speech to facial features (i.e., landmarks, expression blendshape) mapping method. 2) the audio-driven talking face sequences generated by simple cascade structure usually lack of temporal consistency and spatial correlation, which leads to defects in the consistency of changes in details. 3) the operation of forgery is always at the video level, without considering the forgery of the voice, especially the synchronization of the converted voice and the mouth. To address these distortion problems, we propose a novel deep learning framework, named Temporal-Refinement Autoregressive-Cascade Rendering Network (TACR-Net) for audio-driven dynamic talking face editing. The proposed TACR-Net encodes facial expression blendshape based on the given acoustic features without separately training for special video. Then TACR-Net also involves a novel autoregressive cascade structure generator for video re-rendering. Finally, we transform the in-the-wild speech to the target portrait and obtain a photo-realistic and audio-realistic video.

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ZIP File (mfp0211aux.zip)

The video file includes a presentation video of the TACRNet and dynamic visual results. The video includes?1. TACRNet Overview, 2.TEE+ACR+VCN pipeline, 3.Compare with previous methods, 4.Expand work. Each part includes visual and auditory effects, we recommend turning on audio during watching.

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Cited By

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https://dl.acm.org/doi/10.1145/3680528.3687632
Song LLiu PYin GXu C(2024)Adaptive Super Resolution for One-Shot Talking-Head GenerationICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10446837(4115-4119)Online publication date: 14-Apr-2024
https://doi.org/10.1109/ICASSP48485.2024.10446837
Kandaswamy VBalachandern B(2024)FED-AT-VIDEO Nets—A Federated Capsule – Self Gated Learning Architecture For The Multi-View Video Summarization TechniqueSignal, Image and Video Processing10.1007/s11760-024-03601-719:1Online publication date: 6-Dec-2024
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Index Terms

TACR-Net: Editing on Deep Video and Voice Portraits
1. Computing methodologies

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Information & Contributors

Information

Published In

cover image ACM Conferences

MM '21: Proceedings of the 29th ACM International Conference on Multimedia

October 2021

5796 pages

ISBN:9781450386517

DOI:10.1145/3474085

General Chairs:
Heng Tao Shen
University of Electronic Science&Technology of China, China
,
Yueting Zhuang
Zhejiang University, China
,
John R. Smith
IBM, USA
,
Program Chairs:
Yang Yang
University of Electronic Science and Technology of China, China
,
Pablo Cesar
CWI&TU Delft, The Netherlands
,
Florian Metze
FACEBOOK, Inc., USA
,
Balakrishnan Prabhakaran
University of Texas at Dallas, USA

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Published: 17 October 2021

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October 20 - 24, 2021

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Cited By

Song LChen LLiu CLiu PXu C(2024)TextToon: Real-Time Text Toonify Head Avatar from Single VideoSIGGRAPH Asia 2024 Conference Papers10.1145/3680528.3687632(1-11)Online publication date: 3-Dec-2024
https://dl.acm.org/doi/10.1145/3680528.3687632
Song LLiu PYin GXu C(2024)Adaptive Super Resolution for One-Shot Talking-Head GenerationICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10446837(4115-4119)Online publication date: 14-Apr-2024
https://doi.org/10.1109/ICASSP48485.2024.10446837
Kandaswamy VBalachandern B(2024)FED-AT-VIDEO Nets—A Federated Capsule – Self Gated Learning Architecture For The Multi-View Video Summarization TechniqueSignal, Image and Video Processing10.1007/s11760-024-03601-719:1Online publication date: 6-Dec-2024
https://doi.org/10.1007/s11760-024-03601-7
Song LLiu PChen LYin GXu C(2024)Tri$$^{2}$$-plane: Thinking Head Avatar via Feature PyramidComputer Vision – ECCV 202410.1007/978-3-031-72920-1_1(1-20)Online publication date: 1-Oct-2024
https://doi.org/10.1007/978-3-031-72920-1_1
Tran MChang DSiniukov MSoleymani M(2024)DIM: Dyadic Interaction Modeling for Social Behavior GenerationComputer Vision – ECCV 202410.1007/978-3-031-72913-3_27(484-503)Online publication date: 2-Dec-2024
https://doi.org/10.1007/978-3-031-72913-3_27
Yang SWang WLing JPeng BTan XDong JEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Context-Aware Talking-Head Video EditingProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3611765(7718-7727)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3611765
Ling JTan XChen LLi RZhang YZhao SSong L(2023)StableFace: Analyzing and Improving Motion Stability for Talking Face GenerationIEEE Journal of Selected Topics in Signal Processing10.1109/JSTSP.2023.333355217:6(1232-1247)Online publication date: Nov-2023
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Song LYin GJin ZDong XXu C(2023)Emotional Listener Portrait: Realistic Listener Motion Simulation in Conversation2023 IEEE/CVF International Conference on Computer Vision (ICCV)10.1109/ICCV51070.2023.01905(20782-20792)Online publication date: 1-Oct-2023
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Wang XZhang ZZhang J(2023)Structure Invariant Transformation for better Adversarial Transferability2023 IEEE/CVF International Conference on Computer Vision (ICCV)10.1109/ICCV51070.2023.00425(4584-4596)Online publication date: 1-Oct-2023
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Song LLi XFang ZJin ZChen YXu CMagalhães Jdel Bimbo ASatoh SSebe NAlameda-Pineda XJin QOria VToni L(2022)Face Forgery Detection via Symmetric TransformerProceedings of the 30th ACM International Conference on Multimedia10.1145/3503161.3547806(4102-4111)Online publication date: 10-Oct-2022
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