EchoVideo: Identity-Preserving Human Video Generation by Multimodal Feature Fusion (2501.13452v2)

Abstract: 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.

• The paper presents EchoVideo, a framework using multimodal image-text fusion (IITF) and a two-stage training strategy for identity-preserving human video generation.
• EchoVideo achieves superior video quality and significantly reduces "copy-paste" artifacts compared to state-of-the-art methods by relying on semantic features over superficial image cues.
• The EchoVideo framework and its modular IITF component are plug-and-play, enabling potential integration into various creative tools and applications like AR/VR and interactive media.

Insights into EchoVideo: Identity-Preserving Human Video Generation

The paper "EchoVideo: Identity-Preserving Human Video Generation by Multimodal Feature Fusion" addresses pertinent challenges in the domain of identity-preserving video generation (IPT2V). Traditional models, as noted, are often limited by "copy-paste" artifacts and low facial identity similarity, attributed to their dependency on superficial facial imagery. EchoVideo advances the field by proposing a new framework that integrates high-level semantic data from both text and image modalities.

Methodological Innovation

EchoVideo introduces two key strategies to mitigate the limitations of existing methods:

1. Identity Image-Text Fusion Module (IITF): This module is a core innovation designed to fuse high-level semantic features from text and image, thereby capturing more precise identity representations. The architecture efficiently filters out occlusions, lighting, and pose variances that typically introduce artifacts. Unlike existing dual-guidance models, IITF achieves a pre-fusion integration, reducing the complexity associated with multimodal information fusion in pre-trained models like DiTs. It also ensures seamless adaptability, enhancing its applicability to other generative tasks.
2. Two-Stage Training Strategy: The methodology includes an initial phase focusing on general training followed by a stochastic second phase utilizing shallow facial information sporadically. This approach balances detail fidelity with robustness, fostering the model's reliance on broader semantic features over narrow, potentially misleading cues.

Results and Evaluations

EchoVideo underwent extensive empirical testing to evaluate its performance against state-of-the-art models like ConsisID and ID-Animator. It demonstrated superior results in achieving high video quality while maintaining identity consistency. Key performance indicators included:

• CLIPScore and DynamicDegree: These metrics indicated improved adherence to input text descriptions and superior motion realism.
• FaceSim Scores: Although EchoVideo did not outperform ConsisID in facial similarity scores, which highlights a reduced identity consistency, the reduction in "copy-paste" issues suggests better holistic integration.
• FID and AestheticQuality: It outperformed ID-Animator in video realism and appeal, although closely trailing ConsisID on some counts, yet overcoming semantic mismatch significantly.

Speculations on Future Applications

EchoVideo sets a precedent for multimodal content generation, applicable beyond video to any context needing fidelity in generated character retention, potentially influencing fields such as AR/VR, interactive media, and beyond. Its plug-and-play model also opens prospects for integration into varied systems, enhancing creative tools used in media and entertainment.

The work suggests ongoing development in using diffusion models for complex content creation. The limitations it addresses, coupled with its robust architectural innovations, position it as a significant resource for enhancing generative realism while preserving identity integrity.

The implications for AI are manifold, offering a pathway to more nuanced, context-sensitive generation systems that actively reconcile variant modalities. The plug-and-play nature of its IITF module further hints at an era where modular insights drive advancements in AI creativity. While EchoVideo excels in current benchmarks, future explorations might focus on optimizing identity preservation to further level with advanced perceptive fidelity without compromising motion dynamics or general video quality.