EchoVideo: Identity-Preserving Human Video Generation by Multimodal
  Feature Fusion
Recent advancements in video generation have significantly impacted various
downstream applications, particularly in identity-preserving video generation
(IPT2V). However, existing methods struggle with "copy-paste" artifacts and low
similarity issues, primarily due to their reliance on low-level facial image
information. This dependence can result in rigid facial appearances and
artifacts reflecting irrelevant details. To address these challenges, we
propose EchoVideo, which employs two key strategies: (1) an Identity Image-Text
Fusion Module (IITF) that integrates high-level semantic features from text,
capturing clean facial identity representations while discarding occlusions,
poses, and lighting variations to avoid the introduction of artifacts; (2) a
two-stage training strategy, incorporating a stochastic method in the second
phase to randomly utilize shallow facial information. The objective is to
balance the enhancements in fidelity provided by shallow features while
mitigating excessive reliance on them. This strategy encourages the model to
utilize high-level features during training, ultimately fostering a more robust
representation of facial identities. EchoVideo effectively preserves facial
identities and maintains full-body integrity. Extensive experiments demonstrate
that it achieves excellent results in generating high-quality, controllability
and fidelity videos.
6793480ec6fd669f7341cf41
Jiangchuan Wei
extracted_confirmed
2025-02-14T14:24:45.099Z
67af4daa602736dd990c1d43
/avatars/802d512c20ca9a9c6ffa49aa5f98b96c.svg
weijiangchuan
user
weijiangchuan
6793480ec6fd669f7341cf42
Shiyue Yan
6793480ec6fd669f7341cf43
Wenfeng Lin
admin_assigned
2025-01-24T10:18:06.509Z
6676c4f86f2ac48ee6c2f4d4
/avatars/fea4e5be4da7a7047df567a4aa86de0c.svg
linwenfeng
user
linwf
6793480ec6fd669f7341cf44
Boyuan Liu
6793480ec6fd669f7341cf45
Renjie Chen
6793480ec6fd669f7341cf46
Mingyu Guo
EchoVideo: Identity-Preserving Human Video Generation by Multimodal
  Feature Fusion
Recent advancements in video generation have significantly impacted various
downstream applications, particularly in identity-preserving video generation
(IPT2V). However, existing methods struggle with "copy-paste" artifacts and low
similarity issues, primarily due to their reliance on low-level facial image
information. This dependence can result in rigid facial appearances and
artifacts reflecting irrelevant details. To address these challenges, we
propose EchoVideo, which employs two key strategies: (1) an Identity Image-Text
Fusion Module (IITF) that integrates high-level semantic features from text,
capturing clean facial identity representations while discarding occlusions,
poses, and lighting variations to avoid the introduction of artifacts; (2) a
two-stage training strategy, incorporating a stochastic method in the second
phase to randomly utilize shallow facial information. The objective is to
balance the enhancements in fidelity provided by shallow features while
mitigating excessive reliance on them. This strategy encourages the model to
utilize high-level features during training, ultimately fostering a more robust
representation of facial identities. EchoVideo effectively preserves facial
identities and maintains full-body integrity. Extensive experiments demonstrate
that it achieves excellent results in generating high-quality, controllability
and fidelity videos.
EchoVideo generates high-quality control and fidelity videos by integrating high-level semantic features from text and employing a two-stage training strategy with stochastic methods.
Identity Image-Text Fusion Module IITF shallow facial information two-stage training strategy