mlsanityData to 14 Oct 2026

MagicAvatar: Multimodal Avatar Generation and Animation

arXiv 2308.14748 ↗First posted 28 Aug 2023cs.GR cs.CVcited by 17

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Abstract (arXiv)

This report presents MagicAvatar, a framework for multimodal video generation and animation of human avatars. Unlike most existing methods that generate avatar-centric videos directly from multimodal inputs (e.g., text prompts), MagicAvatar explicitly disentangles avatar video generation into two stages: (1) multimodal-to-motion and (2) motion-to-video generation. The first stage translates the multimodal inputs into motion/ control signals (e.g., human pose, depth, DensePose); while the second stage generates avatar-centric video guided by these motion signals. Additionally, MagicAvatar supports avatar animation by simply providing a few images of the target person. This capability enables the animation of the provided human identity according to the specific motion derived from the first stage. We demonstrate the flexibility of MagicAvatar through various applications, including text-guided and video-guided avatar generation, as well as multimodal avatar animation.

Key facts. Cited by 17 papers in the map (data through 14 Oct 2026). Builds on 5 of its 6 references that are in the map. First posted on arXiv 28 Aug 2023.

Builds on

5 of 6 references.

References · 5

  1. 2022DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generationcited by 3,300
  2. 2019Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfercited by 1,495
  3. 2022Human Motion Diffusion Modelcited by 1,080
  4. 2016Realtime Multi-Person 2D Pose Estimation using Part Affinity Fieldscited by 1,064
  5. 2018DensePose: Dense Human Pose Estimation In The Wildcited by 575

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Citing papers per year: 2023: 6; 2024: 8; 2025: 3.

17 papers.

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