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ByteDance’s OmniHuman is a real human-video generation research system, but it is not a photo-only magic button or a freely downloadable consumer app. A still image supplies the person’s appearance; audio, a pose signal, or a driving video supplies speech and movement. The original research project has no official public download or standalone service, while OmniHuman 1.5 is available through BytePlus documentation and hosted services such as fal, subject to account, region and platform terms.
The official project page shows portrait, half-body and full-body examples driven by audio, video or combined signals. The original system is described in the OmniHuman-1 paper and its ICCV 2025 paper.
What viewers are actually seeing
OmniHuman can make a person in one image appear to speak, sing, gesture or perform. The crucial distinction is that the image is an appearance reference, not a complete performance instruction.
Photo = who the character looks like.
Audio, pose or driving video = what the character does.
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With audio, the system can coordinate mouth movement and expression with speech or music. With a driving video or pose information, it can transfer movement to the person in the reference image. Some hosted implementations also accept text guidance for broad behavior or camera direction.
What OmniHuman is
OmniHuman is a human-centric video-generation framework developed by ByteDance researchers. The original OmniHuman-1 uses a Diffusion Transformer conditioned by multiple kinds of motion information. Instead of training only on narrowly matched talking-head clips, the approach mixes audio, pose, video and related motion conditions so one model can learn relationships among appearance, movement, sound, lighting and texture. The technical description is in the original paper and the open-access ICCV paper.
The name can refer to several related things:
- OmniHuman-1: the original research model announced in February 2025.
- OmniHuman 1.5: a later model whose paper describes higher-level semantic guidance for more coherent, expressive motion.
- OmniHuman: a generic family name or the name of an older hosted endpoint.
- OmniHumanoid: a separate 2026 human-to-robot motion-transfer project; it is not the same system. See the OmniHumanoid repository.
How the generation pipeline works
A simplified workflow looks like this:
Reference image + audio, pose or driving video + optional text guidance → generated human video
Reference image
The image establishes identity, clothing, visible body shape and the initial framing. Portrait, half-body and full-body references can be used, but the model cannot reliably infer information hidden outside the frame. A face turned away, covered mouth or missing lower body gives it less dependable material to animate.
Motion signal
Audio can drive speech, singing and rhythm. A driving video or pose sequence can provide body movement. Combining signals can give the model both vocal timing and physical direction. This is why “from one photo” is technically true as an appearance description but misleading if it implies that no other input is needed.
Semantic guidance in OmniHuman 1.5
The OmniHuman-1.5 paper describes structured text guidance generated with a multimodal language model, a multimodal Diffusion Transformer and a “Pseudo Last Frame” design for combining modalities. The stated goal is motion that reflects emotion, intent, context and character behavior, rather than merely following low-level audio rhythm. The paper also discusses multi-person and non-human extensions in its research setting; those capabilities should not be assumed for every public endpoint.
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Why the demonstrations look convincing
The system is optimized for human performance rather than general scenes. Mixed-condition training gives it examples of how voices, gestures, facial movement, lighting and texture co-occur. A Diffusion Transformer then generates a sequence that attempts to keep those elements temporally consistent.
That design helps explain demonstrations of talking, singing and full-body movement across different aspect ratios. It does not prove that every input will look equally good: research showcases are selected examples, not a guarantee of universal performance.
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Official research project
The official OmniHuman site explicitly says it currently offers no services or downloads and warns about fraudulent websites. A page using the OmniHuman name is not automatically connected to ByteDance.
BytePlus enterprise route
BytePlus documentation lists OmniHuman 1.5 as a BytePlus Vision product, with separate video-generation documentation. Access can depend on country, account eligibility, onboarding and product availability; the documentation does not establish a universal consumer service or a public price.
Hosted third-party route
fal’s OmniHuman 1.5 page provides a playground and paid API. This is a hosted service, not the official research download. Its endpoint identifier is fal-ai/bytedance/omnihuman/v1.5.
A practical fal test
- Create or sign in to a fal account and open the OmniHuman 1.5 model page.
- Provide a human reference image URL and an audio URL.
- Optionally add a prompt describing broad movement, expression or camera behavior.
- Select 720p or 1080p, then generate and inspect the MP4 output.
- Check the billed duration and the result for identity drift, lip-sync and motion artifacts.
The documented JavaScript call is:
import { fal } from "@fal-ai/client";
const result = await fal.subscribe("fal-ai/bytedance/omnihuman/v1.5", {
input: {
image_url: "https://example.com/portrait.png",
audio_url: "https://example.com/speech.mp3",
prompt: "The camera slowly pushes in. The subject speaks thoughtfully with a slight smile.",
resolution: "1080p",
turbo_mode: false
},
logs: true
});
console.log(result.data);
console.log(result.requestId);See the API documentation for current schemas. Hosted image and audio URLs generally must be publicly reachable unless uploaded through fal’s supported file handling. Keep the API key on a server, not in browser code.
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Endpoint limits documented by fal
| Item | Documented detail |
|---|---|
| Required inputs | Image URL and audio URL |
| Image formats | JPG, JPEG, PNG, WebP, GIF and AVIF |
| Audio formats | MP3, OGG, WAV, M4A and AAC |
| 1080p duration | Audio limited to 30 seconds |
| 720p duration | Audio limited to 60 seconds |
| Optional control | Text prompt for behavior and camera movement |
| Turbo mode | Faster generation with a stated quality trade-off |
| Output | Generated MP4 and duration metadata |
These are fal endpoint limits, not universal limits of every OmniHuman deployment.
What it costs on fal
Prices below are the amounts listed on fal’s pages on August 18, 2026, and can change:
| Endpoint | Listed rate | Illustrative generation |
|---|---|---|
| OmniHuman 1.5 | $0.16 per second | 10 seconds ≈ $1.60; 30 seconds ≈ $4.80; 60 seconds ≈ $9.60 where the selected resolution permits it |
| Older OmniHuman endpoint | $0.14 per second | Rate shown on the older model page |
These are prices for fal’s hosted endpoints, not a universal ByteDance price. Taxes, account terms and future changes may apply. fal labels the 1.5 endpoint “Commercial use,” but that does not remove your responsibility for consent, copyright, publicity rights, voice rights and platform rules.
How to get better results
- Use a sharp, well-lit, front-facing image with the face and intended body area visible.
- Match the framing to the intended portrait, half-body or full-body output.
- Use clean speech or music with little background noise.
- Start with a short, simple performance before attempting complex choreography.
- Use prompts for broad behavior and camera direction, not frame-by-frame instructions.
- Keep a record of permission for the reference image and audio before uploading them.
Failure modes and limitations
The ICCV paper identifies several technical weaknesses:
- Audio and movement are not always strongly correlated, producing uncoordinated or overly expressive motion.
- Interactions with objects can look unrealistic.
- A relatively high classifier-free-guidance scale improves stability but can introduce overfitting.
- More controls for style, intensity and intention remain an open direction.
In practical use, watch for warped teeth, tongues, hands, fingers, jewelry, glasses and hair; identity drift during fast turns or profile views; gestures that match rhythm but not the literal words; and temporal inconsistencies visible when the clip is paused. Full-body motion and hidden body parts are harder than a straightforward talking head.
Common fixes on a hosted endpoint
- Invalid URL: confirm the URL is publicly reachable and returns the expected file type.
- Duration error: shorten audio to 30 seconds at 1080p or 60 seconds at 720p.
- Poor lip-sync: try cleaner audio and a better-lit, front-facing image.
- Unnatural movement: simplify the prompt, shorten the clip and avoid demanding object interaction.
- Identity changes: use a more representative image and reduce extreme camera movement.
- Queue or generation failure: retry with smaller inputs and follow the host’s current status and rate-limit guidance.
When OmniHuman is a good fit
- Short talking or singing avatars driven by an existing voice track.
- Presenter-style explainers and social clips.
- Character performances where a single reference image is valuable.
- Developers who want an API rather than a complete editing suite.
It is a weaker choice for long-form productions requiring stable identity across many shots, exact choreography, complex hand-object interaction, strict multi-person continuity, or a free local open-source download.
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Alternatives by workflow
HeyGen Avatar V
HeyGen’s Avatar V uses a single reference video plus driving audio. That requires more capture material but can suit reusable branded presenters and workflows prioritizing consistency. HeyGen’s preference comparison is vendor-published research, not an independent benchmark; it reports a preference over several systems, including OmniHuman 1.5.
General image-to-video models
General video models may offer broader camera, scene and object control, while OmniHuman is specialized for audio-driven human performance. Compare lip-sync, identity preservation, hand stability, prompt adherence, duration, resolution, commercial rights, watermarking and provenance rather than assuming one model is universally best.
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Traditional avatar platforms
Business-oriented avatar suites often add scripts, synthetic voices, captions, templates, team permissions and moderation. They can be easier to operate but may be less expressive, more template-bound and more expensive than paying per short generation.
Consent, deepfakes and provenance
A convincing synthetic performance can be harmless creative work or a deceptive impersonation. Obtain permission before animating a real person’s likeness or using their voice, disclose synthetic media where viewers could reasonably be misled, and retain the original image, audio and permission records. Be especially cautious with fabricated statements, scams, political material and content involving people who did not agree to be represented.
Do not assume that a particular watermark, detector or retention policy applies to every OmniHuman deployment. Review the current terms of BytePlus or the host you use before uploading sensitive faces or voices.
Bottom line
OmniHuman is technically significant because it lowers the barrier from a still image to a convincing human performance. It does not invent a complete performance from a photograph alone, and the original ByteDance research release is not an open consumer app. For a quick, paid experiment, fal currently offers the clearest public route to OmniHuman 1.5; for enterprise integration, BytePlus is the documented route. Treat both access and pricing as deployment-specific, and judge the result—and the ethics—clip by clip.
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