# MegActor-Sigma: Unlocking Flexible Mixed-Modal Control in Portrait Animation with Diffusion Transformer

arXiv: https://arxiv.org/abs/2408.14975 · HTML: https://mlsanity.com/paper/2408.14975/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Placed in Audio, speech and music, one of 14 directions in Diffusion models.
- Builds on 53 of its 71 references that are in the map.
- First posted on arXiv 27 Aug 2024.

## Placed in

- Diffusion models / [Audio, speech and music](https://mlsanity.com/diffusion/audio-speech-and-music/)

## Builds on (most cited first, 30 of 53)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets](https://mlsanity.com/paper/2311.15127/) (2023)
- [AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning](https://mlsanity.com/paper/2307.04725/) (2023)
- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [VoxCeleb: a large-scale speaker identification dataset](https://mlsanity.com/paper/1706.08612/) (2017)
- [Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation](https://mlsanity.com/paper/2311.17117/) (2023)
- [Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators](https://mlsanity.com/paper/2303.13439/) (2023)
- [MediaPipe: A Framework for Building Perception Pipelines](https://mlsanity.com/paper/1906.08172/) (2019)
- [One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing](https://mlsanity.com/paper/2011.15126/) (2020)
- [SadTalker: Learning Realistic 3D Motion Coefficients for Stylized Audio-Driven Single Image Talking Face Animation](https://mlsanity.com/paper/2211.12194/) (2022)
- [VideoFusion: Decomposed Diffusion Models for High-Quality Video Generation](https://mlsanity.com/paper/2303.08320/) (2023)
- [MagicAnimate: Temporally Consistent Human Image Animation using Diffusion Model](https://mlsanity.com/paper/2311.16498/) (2023)
- [Effective Whole-body Pose Estimation with Two-stages Distillation](https://mlsanity.com/paper/2307.15880/) (2023)
- [Champ: Controllable and Consistent Human Image Animation with 3D Parametric Guidance](https://mlsanity.com/paper/2403.14781/) (2024)
- [LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control](https://mlsanity.com/paper/2407.03168/) (2024)
- [Hallo: Hierarchical Audio-Driven Visual Synthesis for Portrait Image Animation](https://mlsanity.com/paper/2406.08801/) (2024)
- [EchoMimic: Lifelike Audio-Driven Portrait Animations through Editable Landmark Conditions](https://mlsanity.com/paper/2407.08136/) (2024)
- [AniPortrait: Audio-Driven Synthesis of Photorealistic Portrait Animation](https://mlsanity.com/paper/2403.17694/) (2024)
- [CodeTalker: Speech-Driven 3D Facial Animation with Discrete Motion Prior](https://mlsanity.com/paper/2301.02379/) (2023)
- [VFHQ: A High-Quality Dataset and Benchmark for Video Face Super-Resolution](https://mlsanity.com/paper/2205.03409/) (2022)
- [Follow-Your-Emoji: Fine-Controllable and Expressive Freestyle Portrait Animation](https://mlsanity.com/paper/2406.01900/) (2024)
- [Nonlinear 3D Face Morphable Model](https://mlsanity.com/paper/1804.03786/) (2018)
- [EAMM: One-Shot Emotional Talking Face via Audio-Based Emotion-Aware Motion Model](https://mlsanity.com/paper/2205.15278/) (2022)
- [Diffused Heads: Diffusion Models Beat GANs on Talking-Face Generation](https://mlsanity.com/paper/2301.03396/) (2023)
- [Pixel Codec Avatars](https://mlsanity.com/paper/2104.04638/) (2021)
- [Seeing What You Said: Talking Face Generation Guided by a Lip Reading Expert](https://mlsanity.com/paper/2303.17480/) (2023)

## Built on by (most cited first, 13 of 13)

- [EchoMimicV2: Towards Striking, Simplified, and Semi-Body Human Animation](https://mlsanity.com/paper/2411.10061/) (2024)
- [Long-Term TalkingFace Generation via Motion-Prior Conditional Diffusion Model](https://mlsanity.com/paper/2502.09533/) (2025)
- [DreamActor-M1: Holistic, Expressive and Robust Human Image Animation with Hybrid Guidance](https://mlsanity.com/paper/2504.01724/) (2025)
- [EchoMimicV3: 1.3B Parameters are All You Need for Unified Multi-Modal and Multi-Task Human Animation](https://mlsanity.com/paper/2507.03905/) (2025)
- [MagicInfinite: Generating Infinite Talking Videos with Your Words and Voice](https://mlsanity.com/paper/2503.05978/) (2025)
- [PERSE: Personalized 3D Generative Avatars from A Single Portrait](https://mlsanity.com/paper/2412.21206/) (2024)
- [Bind-Your-Avatar: Multi-Talking-Character Video Generation with Dynamic 3D-mask-based Embedding Router](https://mlsanity.com/paper/2506.19833/) (2025)
- [DiTalker: A Unified DiT-based Framework for High-Quality and Speaking Styles Controllable Portrait Animation](https://mlsanity.com/paper/2508.06511/) (2025)
- [FLAP: Fully-controllable Audio-driven Portrait Video Generation through 3D head conditioned diffusion model](https://mlsanity.com/paper/2502.19455/) (2025)
- [CogPortrait: Fine-Grained Eye-Region Control in Portrait Animation via Hierarchical Agent Planning](https://mlsanity.com/paper/2605.28056/) (2026)
- [RASA: Replace Anyone, Say Anything -- A Training-Free Framework for Audio-Driven and Universal Portrait Video Editing](https://mlsanity.com/paper/2503.11571/) (2025)
- [VividAnimator: An End-to-End Audio and Pose-driven Half-Body Human Animation Framework](https://mlsanity.com/paper/2510.10269/) (2025)
- [Vanast: Virtual Try-On with Human Image Animation via Synthetic Triplet Supervision](https://mlsanity.com/paper/2604.04934/) (2026)

## Abstract (arXiv)

Diffusion models have demonstrated superior performance in the field of portrait animation. However, current approaches relied on either visual or audio modality to control character movements, failing to exploit the potential of mixed-modal control. This challenge arises from the difficulty in balancing the weak control strength of audio modality and the strong control strength of visual modality. To address this issue, we introduce MegActor-Sigma: a mixed-modal conditional diffusion transformer (DiT), which can flexibly inject audio and visual modality control signals into portrait animation. Specifically, we make substantial advancements over its predecessor, MegActor, by leveraging the promising model structure of DiT and integrating audio and visual conditions through advanced modules within the DiT framework. To further achieve flexible combinations of mixed-modal control signals, we propose a ``Modality Decoupling Control" training strategy to balance the control strength between visual and audio modalities, along with the ``Amplitude Adjustment" inference strategy to freely regulate the motion amplitude of each modality. Finally, to facilitate extensive studies in this field, we design several dataset evaluation metrics to filter out public datasets and solely use this filtered dataset to train MegActor-Sigma. Extensive experiments demonstrate the superiority of our approach in generating vivid portrait animations, outperforming previous methods trained on private dataset.