MemFace: Alleviating One-to-Many Mapping in Talking Face Generation

核心概念

MemFace proposes an innovative approach to alleviate the one-to-many mapping challenge in talking face generation by incorporating implicit and explicit memories into the audio-to-expression and neural-rendering models, respectively.

摘要

Memories play a crucial role in improving the lip-sync and rendering quality of talking face generation. MemFace introduces an implicit memory to capture high-level semantics in the audio-expression shared space and an explicit memory to synthesize pixel-level details. By complementing missing information with memories, MemFace surpasses state-of-the-art results consistently across various scenarios.
The one-to-many mapping challenge in talking face generation is addressed through a two-stage framework that decomposes the mapping difficulty into sub-problems. However, learning a deterministic mapping brings ambiguity during training, making it harder to yield realistic visual results.
MemFace's experimental results demonstrate superior performance in lip-sync and rendering quality compared to baseline methods. The proposed model adapts flexibly to new speakers with minimal adaptation data, showcasing its effectiveness in diverse scenarios.

統計資料

Our proposed MemFace achieves a relative improvement of 37.52% in subjective evaluation on the Obama dataset.
Extensive experiments verify that MemFace surpasses all state-of-the-art results across multiple scenarios consistently and significantly.
MemFace uses M = 1000 and N = 300 key-value pairs for optimal performance.

引述

"Learning a deterministic mapping brings ambiguity during training, making it harder to yield realistic visual results."
"By enabling end-to-end training, the implicit memory is encouraged to relate high-level semantics in the audio-expression shared space."
"Our experimental results show that our proposed MemFace surpasses all the state-of-the-art results across multiple scenarios consistently and significantly."

從以下內容提煉的關鍵洞見

Memories are One-to-Many Mapping Alleviators in Talking Face Generation

by Anni Tang,Ti... 於 arxiv.org 03-06-2024

https://arxiv.org/pdf/2212.05005.pdf

深入探究

How can MemFace's approach be applied to other one-to-many mapping tasks beyond talking face generation?

MemFace's approach of incorporating memories, both implicit and explicit, can be applied to various other one-to-many mapping tasks in the field of AI-driven content synthesis. For instance, in text-to-image generation, where a single textual description can correspond to multiple visual interpretations, memories can help alleviate this challenge. By using an implicit memory to capture high-level semantics from the input text and an explicit memory to provide personalized visual details based on past data or training examples, models can generate more accurate and diverse image outputs. Similarly, in machine translation tasks where a sentence in one language may have several valid translations in another language, memories could assist in producing more contextually relevant and accurate translations by complementing missing information.

What are potential ethical considerations when utilizing advanced technologies like MemFace for synthetic content creation?

When utilizing advanced technologies like MemFace for synthetic content creation, several ethical considerations must be taken into account:

Misinformation: There is a risk of misuse leading to the creation of deepfake videos that could spread misinformation or manipulate public opinion.
Privacy Concerns: Generating realistic video portraits without consent raises privacy concerns as individuals' likeness could be used without permission.
Identity Theft: The ability to create highly realistic fake videos poses a threat of identity theft or impersonation.
Bias and Discrimination: If not carefully monitored and controlled during training data selection and model development phases, biases present in the data could perpetuate harmful stereotypes or discriminatory practices.

To address these ethical concerns effectively, it is crucial for developers and users of such technologies to prioritize transparency about their use cases, ensure informed consent when creating synthetic content involving individuals' likenesses, implement safeguards against malicious uses (such as deepfake detection tools), adhere strictly to data privacy regulations (like GDPR), promote diversity and inclusivity within datasets used for training models.

How can incorporating memories into generative models impact future advancements in AI-driven content synthesis?

Incorporating memories into generative models has significant implications for future advancements in AI-driven content synthesis:

Improved Realism: Memories enable models to retain contextual information over time which enhances the realism of generated outputs across various domains like image generation or speech synthesis.
Enhanced Diversity: By leveraging both implicit and explicit memories during generation processes allows for greater diversity among output samples even with limited input variations.
Personalization: Explicit memory aids in capturing individual characteristics leading towards personalized outputs tailored specifically towards different identities or styles.
Reduced Ambiguity: Memories help mitigate challenges associated with one-to-many mappings by providing additional context cues resulting in clearer predictions with reduced ambiguity.

Overall, integrating memory mechanisms into generative models paves the way for more sophisticated AI systems capable of producing higher quality synthesized content while addressing complex challenges related to variability inherent within many real-world applications such as natural language processing or computer vision tasks.