Loopy: Taming Audio-Driven Portrait Avatar with Long-Term Motion Dependency
On the map: Diffusion models → Audio, speech and music
Abstract (arXiv)
With the introduction of diffusion-based video generation techniques, audio-conditioned human video generation has recently achieved significant breakthroughs in both the naturalness of motion and the synthesis of portrait details. Due to the limited control of audio signals in driving human motion, existing methods often add auxiliary spatial signals to stabilize movements, which may compromise the naturalness and freedom of motion. In this paper, we propose an end-to-end audio-only conditioned video diffusion model named Loopy. Specifically, we designed an inter- and intra-clip temporal module and an audio-to-latents module, enabling the model to leverage long-term motion information from the data to learn natural motion patterns and improving audio-portrait movement correlation. This method removes the need for manually specified spatial motion templates used in existing methods to constrain motion during inference. Extensive experiments show that Loopy outperforms recent audio-driven portrait diffusion models, delivering more lifelike and high-quality results across various scenarios.
Key facts. Cited by 102 papers in the map (data through 7 Oct 2026). Placed in Audio, speech and music, one of 14 directions in Diffusion models. Builds on 44 of its 55 references that are in the map. First posted on arXiv 4 Sep 2024.
Builds on
44 of 55 references.
Closest ancestors · 4
Same direction, newest first
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- 2023Diffused Heads: Diffusion Models Beat GANs on Talking-Face Generation6 Jan
Other references · 40
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- 2023DREAM-Talk: Diffusion-based Realistic Emotional Audio-driven Method for Single Image Talking Face Generationcited by 24
- 2024MobilePortrait: Real-Time One-Shot Neural Head Avatars on Mobile Devicescited by 12
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Citing papers per year: 2024: 12; 2025: 70; 2026: 20.
102 papers.
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