# TANGO: Co-Speech Gesture Video Reenactment with Hierarchical Audio Motion Embedding and Diffusion Interpolation

arXiv: https://arxiv.org/abs/2410.04221 · HTML: https://mlsanity.com/paper/2410.04221/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 55 of its 59 references that are in the map.
- First posted on arXiv 5 Oct 2024.

## Builds on (most cited first, 30 of 55)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Improved Baselines with Momentum Contrastive Learning](https://mlsanity.com/paper/2003.04297/) (2020)
- [Expressive Body Capture: 3D Hands, Face, and Body from a Single Image](https://mlsanity.com/paper/1904.05866/) (2019)
- [AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning](https://mlsanity.com/paper/2307.04725/) (2023)
- [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018)
- [FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks](https://mlsanity.com/paper/1612.01925/) (2016)
- [Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation](https://mlsanity.com/paper/2311.17117/) (2023)
- [A Lip Sync Expert Is All You Need for Speech to Lip Generation In The Wild](https://mlsanity.com/paper/2008.10010/) (2020)
- [Video Enhancement with Task-Oriented Flow](https://mlsanity.com/paper/1711.09078/) (2017)
- [MediaPipe: A Framework for Building Perception Pipelines](https://mlsanity.com/paper/1906.08172/) (2019)
- [MotionCLIP: Exposing Human Motion Generation to CLIP Space](https://mlsanity.com/paper/2203.08063/) (2022)
- [TM2T: Stochastic and Tokenized Modeling for the Reciprocal Generation of 3D Human Motions and Texts](https://mlsanity.com/paper/2207.01696/) (2022)
- [Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation](https://mlsanity.com/paper/1712.00080/) (2017)
- [Everybody Dance Now](https://mlsanity.com/paper/1808.07371/) (2018)
- [MCVD: Masked Conditional Video Diffusion for Prediction, Generation, and Interpolation](https://mlsanity.com/paper/2205.09853/) (2022)
- [Softmax Splatting for Video Frame Interpolation](https://mlsanity.com/paper/2003.05534/) (2020)
- [Real-Time Intermediate Flow Estimation for Video Frame Interpolation](https://mlsanity.com/paper/2011.06294/) (2020)
- [Video Frame Synthesis using Deep Voxel Flow](https://mlsanity.com/paper/1702.02463/) (2017)
- [Speech Gesture Generation from the Trimodal Context of Text, Audio, and Speaker Identity](https://mlsanity.com/paper/2009.02119/) (2020)
- [Learning Individual Styles of Conversational Gesture](https://mlsanity.com/paper/1906.04160/) (2019)
- [Generating Holistic 3D Human Motion from Speech](https://mlsanity.com/paper/2212.04420/) (2022)
- [AMT: All-Pairs Multi-Field Transforms for Efficient Frame Interpolation](https://mlsanity.com/paper/2304.09790/) (2023)
- [BEAT: A Large-Scale Semantic and Emotional Multi-Modal Dataset for Conversational Gestures Synthesis](https://mlsanity.com/paper/2203.05297/) (2022)
- [GestureDiffuCLIP: Gesture Diffusion Model with CLIP Latents](https://mlsanity.com/paper/2303.14613/) (2023)

## Built on by (most cited first, 30 of 32)

- [OmniAvatar: Efficient Audio-Driven Avatar Video Generation with Adaptive Body Animation](https://mlsanity.com/paper/2506.18866/) (2025)
- [EchoMimicV2: Towards Striking, Simplified, and Semi-Body Human Animation](https://mlsanity.com/paper/2411.10061/) (2024)
- [GestureLSM: Latent Shortcut based Co-Speech Gesture Generation with Spatial-Temporal Modeling](https://mlsanity.com/paper/2501.18898/) (2025)
- [HumanDiT: Pose-Guided Diffusion Transformer for Long-form Human Motion Video Generation](https://mlsanity.com/paper/2502.04847/) (2025)
- [OmniPaint: Mastering Object-Oriented Editing via Disentangled Insertion-Removal Inpainting](https://mlsanity.com/paper/2503.08677/) (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)
- [EchoMask: Speech-Queried Attention-based Mask Modeling for Holistic Co-Speech Motion Generation](https://mlsanity.com/paper/2504.09209/) (2025)
- [FloodDiffusion: Tailored Diffusion Forcing for Streaming Motion Generation](https://mlsanity.com/paper/2512.03520/) (2025)
- [Contextual Gesture: Co-Speech Gesture Video Generation through Context-aware Gesture Representation](https://mlsanity.com/paper/2502.07239/) (2025)
- [Motion-example-controlled Co-speech Gesture Generation Leveraging Large Language Models](https://mlsanity.com/paper/2507.20220/) (2025)
- [AudCast: Audio-Driven Human Video Generation by Cascaded Diffusion Transformers](https://mlsanity.com/paper/2503.19824/) (2025)
- [Apollo: Unified Multi-Task Audio-Video Joint Generation](https://mlsanity.com/paper/2601.04151/) (2026)
- [AceVFI: A Comprehensive Survey of Advances in Video Frame Interpolation](https://mlsanity.com/paper/2506.01061/) (2025)
- [Intentional Gesture: Deliver Your Intentions with Gestures for Speech](https://mlsanity.com/paper/2505.15197/) (2025)
- [Social Agent: Mastering Dyadic Nonverbal Behavior Generation via Conversational LLM Agents](https://mlsanity.com/paper/2510.04637/) (2025)
- [TalkCuts: A Large-Scale Dataset for Multi-Shot Human Speech Video Generation](https://mlsanity.com/paper/2510.07249/) (2025)
- [Video Motion Graphs](https://mlsanity.com/paper/2503.20218/) (2025)
- [DyStream: Streaming Dyadic Talking Heads Generation via Flow Matching-based Autoregressive Model](https://mlsanity.com/paper/2512.24408/) (2025)
- [LiveGesture Streamable Co-Speech Gesture Generation Model](https://mlsanity.com/paper/2604.10927/) (2026)
- [Bringing Your Portrait to 3D Presence](https://mlsanity.com/paper/2511.22553/) (2025)
- [MMGT: Motion Mask Guided Two-Stage Network for Co-Speech Gesture Video Generation](https://mlsanity.com/paper/2505.23120/) (2025)
- [Multi-human Interactive Talking Dataset](https://mlsanity.com/paper/2508.03050/) (2025)
- [SARAH: Spatially Aware Real-time Agentic Humans](https://mlsanity.com/paper/2602.18432/) (2026)
- [U-Mind: A Unified Framework for Real-Time Multimodal Interaction with Audiovisual Generation](https://mlsanity.com/paper/2602.23739/) (2026)
- [Audio-driven Gesture Generation via Deviation Feature in the Latent Space](https://mlsanity.com/paper/2503.21616/) (2025)
- [MotionRAG-Diff: A Retrieval-Augmented Diffusion Framework for Long-Term Music-to-Dance Generation](https://mlsanity.com/paper/2506.02661/) (2025)
- [Input-Aware Sparse Attention for Real-Time Co-Speech Video Generation](https://mlsanity.com/paper/2510.02617/) (2025)
- [VividAnimator: An End-to-End Audio and Pose-driven Half-Body Human Animation Framework](https://mlsanity.com/paper/2510.10269/) (2025)
- [GestureFAR: Streaming Co-Speech Gesture Generation with Flow Autoregression](https://mlsanity.com/paper/2609.21576/) (2026)
- [Co-speech Gesture Video Generation via Motion-Based Graph Retrieval](https://mlsanity.com/paper/2512.02576/) (2025)

## Abstract (arXiv)

We present TANGO, a framework for generating co-speech body-gesture videos. Given a few-minute, single-speaker reference video and target speech audio, TANGO produces high-fidelity videos with synchronized body gestures. TANGO builds on Gesture Video Reenactment (GVR), which splits and retrieves video clips using a directed graph structure - representing video frames as nodes and valid transitions as edges. We address two key limitations of GVR: audio-motion misalignment and visual artifacts in GAN-generated transition frames. In particular, (i) we propose retrieving gestures using latent feature distance to improve cross-modal alignment. To ensure the latent features could effectively model the relationship between speech audio and gesture motion, we implement a hierarchical joint embedding space (AuMoCLIP); (ii) we introduce the diffusion-based model to generate high-quality transition frames. Our diffusion model, Appearance Consistent Interpolation (ACInterp), is built upon AnimateAnyone and includes a reference motion module and homography background flow to preserve appearance consistency between generated and reference videos. By integrating these components into the graph-based retrieval framework, TANGO reliably produces realistic, audio-synchronized videos and outperforms all existing generative and retrieval methods. Our codes and pretrained models are available: urlhttps://pantomatrix.github.io/TANGO/