China is racing fast in the AI game – after DeepSeek and Qwen models, ByteDance has just released an impressive research paper! The OmniHuman-1 paper introduces OmniHuman, a new framework that uses Diffusion Transformer-based architecture to push the boundaries of human animation. This model can create ultra-realistic human videos in any aspect ratio and body proportion, all from just a single image and some audio. No more worrying about complex setups or limitations of existing models- OmniHuman simplifies it all and does it better than anything I’ve seen so far. Find more about model architechture and working here!
Table of contents
Limitations of Existing Models
Current human animation models often depend on small datasets and are tailored to specific scenarios, which can lead to subpar quality in the generated animations. These constraints hinder the ability to create versatile and high-quality outputs, making it essential to explore new methodologies.
Many existing models struggle to generalize across diverse contexts, resulting in animations that lack realism and fluidity. The reliance on single input modalities, i.e. the model only receives information from one source to create the video, rather than combining multiple sources like text and image simultaneously, limits their capacity to capture the complexities of human motion and expression, which are crucial for producing lifelike animations.
As the demand for more sophisticated and engaging digital content grows, it becomes increasingly important to develop frameworks that can effectively integrate multiple data sources and enhance the overall quality of human animation.
The OmniHuman 1 Solution
Multi-Conditioning Signals
To overcome these challenges, OmniHuman incorporates multiple conditioning signals, including text, audio, and pose. This multifaceted approach allows for a more comprehensive and versatile method of video generation, enabling the model to produce animations that are not only realistic but also contextually rich.
Omni-Conditions Designs
The paper details the Omni-Conditions Designs, which integrate various driving conditions while ensuring that the subject’s identity and background details from reference images are preserved. This design choice is crucial for maintaining consistency and realism in the generated animations.
Unique Training Strategy
The authors propose a unique training strategy that enhances data utilization by leveraging stronger conditioned tasks. This method allows the model to improve performance without the risk of overfitting, making it a significant advancement in the field of human animation.
Videos Generated by OmniHuman 1
OmniHuman generates realistic human videos using a single image and audio input. It supports various visual and audio styles, producing videos at any aspect ratio and body proportion (portrait, half, or full body). Detailed motion, lighting, and texture achieve realism. We omit reference images (typically the first video frame) for brevity, but provide them upon request. A separate demo showcases the video with combined driving signals.
Talking
Singing
Diversity
Halfbody Cases with Hands
Also Read: Top 8 AI Video Generators for 2025
Model Training and Working
The OmniHuman 1 framework’s training process optimizes human animation generation using a multi-condition diffusion model. It focuses on two key components: the OmniHuman Model and the Omni-Conditions Training Strategy.
OmniHuman Model Working
At the core of the OmniHuman framework is a pretrained Seaweed model that uses the MMDiT architecture. It is initially trained on general text-video pairs for text-to-video and text-to-image tasks. This model is then adapted to generate human videos by incorporating text, audio, and pose signals. Integrating these modalities is key to capturing human motion and expression.
The model uses a causal 3D Variational Autoencoder (3DVAE) to project videos into a latent space. This helps with the video denoising process through flow matching. The architecture handles the complexities of human animation, ensuring realistic and contextually relevant outputs.
To preserve the subject’s identity and background from a reference image, the model reuses the denoising architecture. It encodes the reference image into a latent representation and allows interaction between reference and video tokens through self-attention. This approach incorporates appearance features without extra parameters, streamlining the training process and improving scalability as the model size grows.
Model Architecture
This image shows the OmniHuman model architecture and how it processes multiple input modalities to generate human animations. It starts with text, image, noise, audio, and pose inputs, each representing a key aspect of human motion and appearance. The model feeds these inputs into transformer blocks that extract relevant features, with separate pathways for frame-level audio and pose heatmap features. The features fuse and pass through more transformer blocks, allowing the model to understand the relationships between the modalities. Finally, the model outputs a prediction, likely a video or sequence of frames, representing the generated human animation based on all the inputs.
Omni-Conditions Training Strategy
The Omni-Conditions Training Strategy uses a three-stage mixed condition post-training approach to progressively transform the diffusion model from a general text-to-video generator into a specialized multi-condition human video generation model. Each stage introduces the driving modalities—text, audio, and pose—based on their motion correlation strength, from weak to strong. This careful sequencing ensures that the model balances the contributions of each modality effectively, enhancing the overall quality of the generated animations.
Audio Conditioning
The wav2vec model extracts acoustic features, which align with the hidden size of the MMDiT through a multi-layer perceptron (MLP). These audio features concatenate with those from adjacent timestamps to create audio tokens, which the model injects via cross-attention mechanisms. This enables dynamic interaction between the audio tokens and the noisy latent representations, enriching the generated animations with synchronized audio-visual elements.
Pose Conditioning
A pose guider encodes the driving pose heatmap sequence. The resulting pose features are concatenated with those of adjacent frames to form pose tokens, which are then integrated into the unified multi-condition diffusion model. This integration enables the model to accurately capture the dynamics of human motion as specified by the pose information.
This image illustrates the OmniHuman training process, a three-stage approach for generating human animations using text, image, audio, and pose inputs. It shows how the model progresses from general text-to-video pre-training to specialized multi-condition training. Each stage gradually incorporates new modalities, starting with text and image, then adding audio, and finally pose, to enhance the realism and complexity of the generated animations. The training strategy emphasizes a shift from weak to strong motion-related conditioning, optimizing the model’s performance in generating diverse and realistic human videos.
Inference Strategy
The inference strategy of the OmniHuman framework optimizes human animation generation by activating conditions based on the driving scenario. In audio-driven scenarios, the system activates all conditions except pose, while pose-related combinations activate all conditions. Pose-only driving disables audio. When a condition is activated, it also activates lower influence conditions unless they are unnecessary.
To balance expressiveness and computational efficiency, classifier-free guidance (CFG) is applied to audio and text. However, increased CFG can cause artifacts like wrinkles, while decreased CFG may compromise lip synchronization. To mitigate these issues, a CFG annealing strategy progressively reduces CFG magnitude during inference.
OmniHuman can generate video segments of arbitrary length, constrained by memory, and ensures temporal coherence by utilizing the last five frames of the previous segment as motion frames, maintaining continuity and identity consistency.
OmniHuman 1 Experimental Validation
In the experimental section, the paper outlines the implementation details, including a robust dataset comprising 18.7K hours of human-related data. This extensive dataset is filtered for quality, ensuring that the model is trained on high-quality inputs.
Model Performance
The performance of OmniHuman is compared against existing methods, demonstrating superior results across various metrics.
Table 1 showcases OmniHuman’s performance against other audio-conditioned animation models across CelebV-HQ and RAVDESS datasets, evaluating metrics like IQA, ASE, Sync-C, FID, and FVD.
This explains that OmniHuman achieves the best overall results by averaging metrics across the dataset, demonstrating its effectiveness. It also highlights OmniHuman’s superior performance across most individual dataset metrics. Unlike existing methods tailored for specific body proportions and input sizes, OmniHuman uses a single model to support various input configurations and achieves satisfactory results through its omni-conditions training. This training leverages a large-scale, diverse dataset with varying sizes.
Ablation Study
An ablation study is a set of experiments that remove or replace parts of a machine learning model to understand how those parts contribute to the model’s performance. This primarily investigates the principles of Omni-Conditions Training within OmniHuman. It examines the impact of varying training data ratios for different modalities, with a focus on the influence of audio and pose condition ratios on the model’s performance.
Audio Condition Ratios
One key experiment compares training with data exclusively meeting strict audio and pose animation requirements (100% audio training ratio) against training incorporating weaker condition data, such as text. The results revealed that:
- High Proportion of Audio-Specific Training Data: Limited the dynamic range and hindered performance with complex input images.
- Incorporating Weaker Condition Data (50% ratio): Improved results, such as accurate lip-syncing and natural motion.
- Excess of Weaker Condition Data: Negatively impacted training, reducing the correlation with the audio.
Subjective evaluations confirmed these findings, leading to the selection of a balanced training ratio.
Pose Condition Ratios
The study also investigates the influence of pose condition ratios. Experiments with varying pose data proportions showed:
- Low Pose Condition Ratio: When tested with only audio, the model generated intense, frequent co-speech gestures.
- High Pose Condition Ratio: Made the model overly reliant on pose conditions, leading to results that maintained the same pose regardless of input audio.
A 50% pose ratio was determined to be optimal.
Reference Image Ratio
- Lower Reference Ratios: Led to error accumulation, resulting in increased noise and color shifts.
- Higher Reference Ratios: Ensured better alignment with the original image’s quality and details. This was because lower ratios allowed the audio to dominate video generation, compromising identity information from the reference image.
Visualizations and Findings
The study’s visualizations showcase the results of different audio condition ratios. Models were trained with 10%, 50%, and 100% audio data ratios and tested with the same input image and audio. These comparisons helped determine the optimal balance of audio data for generating realistic and dynamic human videos.
Extended Visual Results
The extended visual results presented in the paper highlight OmniHuman’s capabilities in generating diverse and realistic human animations. These visuals serve as compelling evidence of the model’s effectiveness and versatility.
The results highlight aspects difficult to quantify with metrics or compare with existing methods. OmniHuman effectively handles diverse input images while preserving the original motion style, even replicating distinct anime mouth movements. It also excels in object interaction, generating videos of activities like singing with instruments or making natural gestures while holding objects. Furthermore, its compatibility with pose conditions enables both pose-driven and combined pose and audio-driven video generation. More video samples are available on the project page.
Also Read:
- I Tested OpenAI Sora and Here’s What I Discovered
- Google Veo 2 Just Shocked Everyone!
- Qwen2.5-VL Vision Model
Conclusion
The paper emphasizes the significant contributions of OmniHuman to the field of human video generation. The framework’s ability to produce high-quality animations from weak signals and its support for multiple input formats mark a substantial advancement.
I am excited to try this model! Are you? Let me know in the comment section below!
Stay tuned to Analytics Vidhya Blog for more such awesome content!
Harsh Mishra is an AI/ML Engineer who spends more time talking to Large Language Models than actual humans. Passionate about GenAI, NLP, and making machines smarter (so they don’t replace him just yet). When not optimizing models, he’s probably optimizing his coffee intake. 🚀☕
Free Courses
