In a 2025 FakeParts deepfake study, trained viewers correctly flagged AI-generated and partially manipulated video only 75.3% of the time, and the hardest clips to catch were the temporally coherent text-to-video outputs that modern tools now produce by default.
Realistic AI video is no longer a model problem.
It is a craft problem.
The frontier systems can already render skin, light, and motion that survive a casual scroll, so the gap between a clip that reads as real and a clip that screams "AI" is now decided by how you prompt, how you control motion, how you fix predictable artifacts, and how you finish the footage in post. This guide breaks down the full workflow, grounded in the research on why synthetic video fails the human eye and what specifically restores believability.
The stakes are commercial, not academic.
The global AI video generator market was valued at roughly 716.8 million dollars in 2025 and is projected to grow toward 847 million dollars in 2026 at an 18.8% compound annual growth rate, according to Fortune Business Insights. Marketing and product teams are pouring spend into AI video, yet most of that output still carries the tell-tale shimmer, the melting face, and the floating object that quietly destroys trust. Tools alone will not save you.
A repeatable realism process will.
Platforms have made the raw capability trivial to reach.
A model hub like the Higgsfield AI Video Generator routes a single prompt through Sora 2, Veo 3.1, Kling 3.0, and Seedance under one workflow, which means the bottleneck has shifted away from access and onto technique. The teams winning with AI video in 2026 are the ones who treat it like cinematography plus quality control, not like a slot machine.
▶️ If your ranked pages are not showing up in AI answers and you want a content system that fixes that, book a SaaS content strategy call.
Table of Contents
What Makes an AI Video Look Fake
An AI video looks fake when its motion, physics, or identity drifts between frames in ways the human visual system flags as impossible, even when any single frame looks photoreal. The problem lives in time, not in resolution.
Your visual system is a consistency-checking machine. You have spent a lifetime watching light bounce off skin, fabric fall under gravity, and hands grip objects with five fingers, so the moment a clip violates those priors your subconscious registers wrongness before you can name it. Most viewers describe a fake clip as "off" without being able to articulate why, which is the fingerprint of a temporal failure rather than a static one.
The recurring tells cluster into a short, predictable set. Naming them is the first step, because every one of them has a known cause and a known fix.
| The Tell | What You See | Why It Happens |
|---|---|---|
| Identity drift | A face subtly morphs, ages, or melts during movement | The model resolves each frame semi-independently with weak identity constraints |
| Texture swimming | Skin, hair, fabric, or grass appears to boil or slide | High-frequency detail is regenerated frame by frame without temporal locking |
| Object drift | Items grow, shrink, vanish, or float across the shot | No persistent 3D scene model anchors object position and scale |
| Physics violations | Water, cloth, and collisions move with the wrong weight | The model has no physics engine, only statistical priors about motion |
| Hand and finger errors | Extra fingers, fused digits, impossible grips | Hands are high-variance in training data and demand precise per-frame consistency |
| Text degradation | On-screen text warps or scrambles after a second or two | Text is rendered as texture, not as symbols, so it cannot stay stable |
| Cadence stutter | Motion judders even at a stated frame rate | Actual frame spacing is irregular and motion blur is missing |
These artifacts are not random.
They follow from how diffusion and diffusion-transformer models build video, which is the subject of the next section. Understand the cause and the fixes stop feeling like guesswork.
For teams thinking about how video fits a broader publishing motion, our B2B SaaS content benchmarks breakdown shows where rich media earns its keep.
Why AI Videos Look Unnatural: Temporal Consistency and Physics
AI videos look unnatural mainly because models lack two things humans take for granted, namely strong temporal coupling between frames and any real model of physics. Resolution rose fast, but coherence over time did not.
Most video generators process frames with only loose links to their neighbors, so identity and texture wander as the clip plays. The result is the flicker and morph you see in longer takes, where shapes, positions, and appearances stop agreeing with themselves. This is why a clip can look flawless as a still and fall apart the instant it moves.
Physics is the harder wall. A 2025 evaluation called PhyWorldBench generated more than a thousand clips per model across twelve leading text-to-video systems and found that physical realism, namely correct gravity, momentum, friction, and collision behavior, remained the weakest axis even for top models. Humans are exquisitely tuned to physics violations, so a ball that floats or water that defies surface tension reads as fake instantly, regardless of how sharp the pixels are.
Where viewers look confirms the diagnosis. An eye-tracking study on how people watch AI-generated videos of physical scenes recorded tens of thousands of fixations and found that gaze concentrates on motion boundaries and interacting objects, which is exactly where temporal and physics errors surface. In other words, the eye hunts for the seams, so realism work has to win precisely at those seams.
The practical takeaway reframes the whole task. You are not trying to make a prettier frame. You are trying to make motion that holds its story across time, which means controlling clip length, motion complexity, and the specific actions you ask a model to perform.
Can Viewers Actually Tell a Video Is AI-Generated
Often they cannot, and that is the core opportunity and risk. Across large studies, untrained viewers detect AI media at rates close to a coin flip, while even purpose-built detectors top out well short of certainty.
The detection picture splits between humans and machines, and neither is reliable in the way audiences assume. Human accuracy swings with clip difficulty, while automated tools degrade as generators improve. The numbers below are the ones worth internalizing before you publish.
| Who Is Judging | Accuracy | Source and Year |
|---|---|---|
| Untrained human viewers (mixed AI media) | Near 50%, close to chance | University of Southern California perceptual experiment, 2025 |
| Human viewers on manipulated video | 75.3% on FakeParts clips | FakeParts study, 2025 |
| Automated diffusion-video detector | Up to 93.7% on a benchmark set | Columbia Engineering DIVID, 2026 |
Columbia's DIVID detector reached up to 93.7% accuracy on diffusion-generated clips from systems including Sora and Pika, yet its own authors are candid that the work is a step in an arms race rather than a solution. PhD researcher Yun-Yun Tsai described the framework as "a significant leap forward in detecting AI-generated content," which is the language of progress, not of a settled problem. Detectors that hit 95% on older GAN fakes have been measured dropping toward 60% on modern diffusion video, and adversarial noise can push a flagged clip back under the threshold.
For a marketer, the lesson is twofold. First, realism is achievable enough that careless fakes are common, so the bar for "believable" is lower than fear-mongering suggests. Second, because audiences cannot reliably self-detect, the burden of honesty shifts onto the creator, which is why disclosure becomes a strategic choice rather than an afterthought. We return to that in the provenance section.
How to Make AI Video Look Realistic: The Core Workflow
You make AI video realistic by controlling six layers in order, namely model choice, prompt and cinematography, motion and frame settings, artifact remediation, consistency locking, and post-production finishing. Skip a layer and the seams reappear.
Most failed AI clips fail because the creator treated generation as a single step. Realistic output is a pipeline where each stage closes a specific category of tell. The table below is the spine of everything that follows in this guide.
| Step | Action | What It Fixes |
|---|---|---|
| 1. Model selection | Match the model to the shot type and motion demand | Physics and coherence failures from a wrong-tool choice |
| 2. Prompt and cinematography | Specify lens, motion, lighting, and a single clear action | Vague guessing that produces drift and morph |
| 3. Motion and frame settings | Lock cinematic frame rate, shutter, and modest camera moves | Cadence stutter and missing motion blur |
| 4. Artifact remediation | Regenerate, segment, and mask known failure points | Faces, hands, text, and object drift |
| 5. Consistency locking | Use reference frames and seeds to hold identity | Identity drift across clips and cuts |
| 6. Post-production finishing | Grade, add grain, upscale, and conform cadence | The residual plastic, over-clean AI sheen |
Each step has a dedicated section below. Treat them as a checklist, not a menu. A clip that passes one stage and skips another will still betray itself on a high-density screen, which is exactly where most reviewers eventually watch.
Teams that operationalize this kind of repeatable production discipline tend to borrow the same logic they use for written assets, which our SaaS content marketing approach treats as a system rather than a series of one-off posts.
How to Write Prompts for Realistic AI Video
Write prompts the way a director writes a shot list, naming the lens, camera move, lighting, subject, single action, and style, because specificity removes the guesswork that produces drift. Vague prompts force the model to invent, and invention is where realism dies.
When a prompt fails to give clear spatial and temporal cues, the model fills the gaps probabilistically, and that is when faces distort and objects wander. The fix is to supply the cues a cinematographer would. A reliable structure stacks five components in a deliberate order.
Cinematography: Lead with the shot grammar, for example "slow dolly-in, 35mm lens, shallow depth of field," so the model anchors framing and movement before it renders anything.
Subject and single action: Describe one clear subject performing one clear action, because a single intention per clip stays inside the motion budget and avoids competing movements that break coherence.
Lighting and environment: Name a concrete lighting setup such as "soft window light, late afternoon, warm key," since believable light and shadow are among the strongest realism cues the eye accepts.
Physical and material detail: Specify materials and textures, for example "worn denim, brushed steel, condensation on glass," to push the model toward grounded surfaces rather than generic plastic.
Style and reference: Close with a stylistic anchor like "shot on 16mm film, natural grain," which biases output toward the imperfections audiences read as authentic.
The counterintuitive rule is restraint.
The more chaotic the requested motion, the faster coherence collapses, so the most realistic prompts ask for understated movement. A static subject with subtle ambient motion, a gentle camera push, and one deliberate gesture will almost always outperform a prompt packed with action verbs. If you need complexity, build it across several controlled clips rather than one overloaded generation, then assemble them in an edit.
For prompt structure that mirrors how AI engines parse content more broadly, our guide to AEO-ready SaaS content covers the same discipline applied to text.
Which Frame Rate and Motion Settings Make AI Video Look Real
Realistic AI video almost always conforms to cinema convention, namely 24 frames per second with a 180-degree shutter and natural motion blur, because that cadence is what audiences subconsciously equate with professional footage. Wrong cadence is an instant tell even when the image is clean.
The motion judder common in raw AI clips comes from irregular frame spacing and the absence of motion blur, both of which the human eye reads as cheap or artificial. Veo 3.1, for instance, outputs at a cinema-standard 24 frames per second precisely to land in this trusted zone, and conforming all of your footage to a single consistent cadence is one of the cheapest realism wins available.
| Setting | Realistic Target | Why It Matters |
|---|---|---|
| Frame rate | 24 fps for cinematic, 30 fps for broadcast or social | Matches the cadence audiences associate with real production |
| Motion blur | Present and consistent, equivalent to a 180-degree shutter | Smooths motion and hides micro-jitter between frames |
| Camera movement | Slow, motivated moves such as a gentle push or pan | Reduces the motion the model must keep coherent |
| Clip length | 3 to 8 seconds per generation | Stays inside the coherence window before drift sets in |
| Slow motion | Add in post from a clean base, not in the prompt | AI rarely produces the clean cadence true slow motion needs |
The longer the take and the faster the movement, the more the model has to keep consistent, and the likelier it is to fail. Generate in short segments, keep camera moves motivated and slow, and stitch the pieces together in an editor where you control cadence, transitions, and timing.
This single habit, namely thinking in short cuts rather than one long generation, eliminates a large share of the artifacts that mark a clip as synthetic.
How to Fix Flickering, Morphing Faces, and Drifting Objects
Fix flicker and morph by shortening clips, regenerating from a strong first frame, and locking identity with reference inputs, because these artifacts all stem from the model resolving frames semi-independently. The cure is to give it something stable to hold onto.
Each major artifact maps to a concrete remediation. Memorize the pairings and review becomes faster, because you stop asking "why is this wrong" and start applying the known fix.
| Artifact | Primary Fix | Practical Tactic |
|---|---|---|
| Flickering and shimmer | Shorten the clip and enable temporal stabilization | Generate 3 to 5 second segments, then run a temporal-consistency or De-AI pass |
| Morphing or melting faces | Anchor identity to a reference frame | Use a clean first frame as a reference for regeneration, hold the same seed |
| Drifting or scaling objects | Reduce scene complexity and motion | Simplify the background, request one moving subject, regenerate variants and pick the stable take |
| Texture boiling | Avoid aggressive upscaling, regenerate detail in post | Use context-aware enhancement rather than sharpening, which worsens swimming |
| Background instability | Separate subject and background | Generate or composite the subject over a stable plate |
The most reliable single technique is the regenerate-and-select loop. Frontier output is probabilistic, so the same prompt yields different stability each time, and producing several variants then choosing the cleanest is faster than fighting one bad generation. Practitioners running large volumes report that only a minority of first-try clips are usable, which makes batching and selection a core part of the craft rather than a sign of failure.
When a face reads well in the opening frame, reuse that frame as the regeneration reference so the model has a fixed identity to defend across the take.
How to Keep Characters and Scenes Consistent Across Clips
Keep characters consistent by combining reference images, fixed seeds, and image-to-video pipelines, so the model reproduces the same identity, wardrobe, and setting across separate generations. Consistency is what turns isolated clips into a believable sequence.
Identity drift is the artifact that most often exposes a multi-clip project, because audiences forgive a slightly odd single shot but never forgive a character whose face changes between cuts. The remedy is to remove as much randomness as the tool allows and feed it explicit anchors.
| Technique | What It Locks | How To Apply |
|---|---|---|
| Reference image or character | Face, body, and wardrobe identity | Supply a consistent portrait or character reference to every clip in the set |
| Fixed seed | Overall look and randomness | Reuse the same seed across generations for the same character or scene |
| Image-to-video start frame | Composition and first-frame identity | Generate a strong still, then animate it rather than generating from text alone |
| Continuity prompt block | Wardrobe, lighting, and environment | Repeat an identical descriptive block across every prompt in the sequence |
| Scene plate | Background and set consistency | Reuse the same generated or filmed background plate behind the subject |
Modern tools increasingly support multi-shot or storyboard modes that carry identity and audio across cuts, which reduces the manual work, but the underlying principle is unchanged. The less you leave to chance, the more the model behaves. Build a small reference kit for any recurring character, namely a fixed portrait, a seed, and a continuity block, and reuse it for every shot.
This is the same asset-reuse logic that makes a content library compound, a pattern explored in our analysis of blog versus paid ads for SaaS growth.
How to Fix AI Hands, Text, and Broken Physics
Fix hands, text, and physics by avoiding the shots that expose them and remediating the rest in post, because these are the three weakest points in every current model. Designing around them is more reliable than hoping a regeneration lands.
Hands carry enormous variance in training data and demand frame-to-frame precision the models rarely achieve, which produces extra fingers, fused digits, and impossible grips. On-screen text is rendered as texture rather than as symbols, so it warps and scrambles within a second or two. Physics has no simulator behind it, so fluids, fast collisions, and dense interactions read as wrong because the eye is tuned to exactly those dynamics.
Hands: Avoid tight close-ups of hands and intricate manipulation, frame hands lower or partially out of view, and regenerate until an acceptable take appears, since there is no in-prompt switch that guarantees correct fingers.
On-screen text: Add text in an editor after generation rather than asking the model to render it, which gives you crisp, stable, on-brand typography instead of a warping smear.
Physics-heavy action: Steer prompts away from splashing liquids, shattering glass, and crowd collisions, and when the story requires them, generate multiple variants and select the most physically plausible, or composite practical elements over the AI base.
The strategic move is shot design. Choose subjects and actions that play to model strengths, namely steady human presence, simple motivated motion, and grounded materials, and route the rest to post-production or to traditional footage. A realistic project is as much about what you choose not to generate as about what you do.
How to Remove the AI Look in Post-Production
Remove the residual AI look in post by grading, adding film grain, conforming cadence, and upscaling with context-aware detail, because raw AI footage is usually too clean, too flat, and too uniform to read as captured. Post is where believable becomes indistinguishable.
The paradox of modern generators is that their output can be too perfect, lacking the sensor noise, lens character, and tonal variation that real cameras impose. Audiences read that uniform sheen as synthetic. A short finishing pipeline reintroduces the imperfections of real capture and conforms everything to a consistent look.
| Stage | Action | Effect On Realism |
|---|---|---|
| Color grade | Apply a unified film-style grade across all clips | Replaces the flat AI palette with motivated, consistent color |
| Film grain | Add subtle, consistent grain | Masks micro-flicker and mimics sensor noise audiences expect |
| Cadence conform | Standardize frame rate and motion blur across the timeline | Removes stutter and unifies clips from different models |
| Context-aware upscale | Reconstruct detail rather than sharpen | Restores hair, pores, and fabric without texture swimming |
| Sound design | Add grounded ambient audio and foley | Sells physical presence the silent base clip cannot |
A critical and overlooked step is reviewing on a high-density display. Lower-resolution monitors act as a low-pass filter that smooths over micro-flicker and texture swimming, creating false confidence, so a clip that looks polished on a laptop can break down on a 4K audience screen. Review your finished footage frame by frame at full resolution before you ship, because the artifacts you cannot see are the ones your audience will.
Executing this kind of end-to-end production system, where every asset is planned, structured, and finished to a consistent standard, is exactly the gap The Rank Masters closes for B2B SaaS teams, building an ICP-led content system that maps each topic cluster to a money page and to pipeline rather than publishing media that never converts.
Video is one more asset inside that system, and it earns its place only when it is discoverable and tied to revenue.
Which AI Video Generator Produces the Most Realistic Output
The most realistic AI video in 2026 comes from matching the model to the shot, with Sora 2 leading on physics, Veo 3.1 on cinematic polish and audio, and Kling 3.0 on human motion and complex materials. No single model wins every scene.
Production teams increasingly route between two or three models depending on the shot type, because each system has a distinct realism strength. Sora 2 was released by OpenAI in late September 2025 with a diffusion-transformer architecture and synchronized audio, and it is widely regarded as the benchmark for object, fluid, and gravity simulation. Veo 3.1 leads on prompt adherence, native audio, and high-resolution cinematic output, while Kling holds an edge on human performance and the motion of hair, liquids, and fabric.
| Model | Realism Strength | Best For | Note |
|---|---|---|---|
| Sora 2 | Physics, weight, and fluid behavior | Simulation-heavy and object-interaction shots | Diffusion-transformer base with synchronized audio |
| Veo 3.1 | Cinematic polish, prompt adherence, native audio | Narrative and establishing shots, 24 fps output | Strong all-rounder for high-fidelity scenes |
| Kling 3.0 | Human motion and complex materials | Character performance, hair, cloth, liquids | Multi-shot storyboard mode aids continuity |
| Runway Gen-4 | Granular creative control | Camera moves, motion brush, reference-driven consistency | Pro favorite for precise direction |
Independent leaderboards have ranked the top closed models closely on text-to-video quality, with Sora 2's production variant reported tying for first against Veo on a public comparison arena ([SEARCH_QUERY: "Sora 2 Pro text-to-video Arena leaderboard ranking 2025"]). The practical guidance is to stop searching for one perfect tool.
Pick the model whose strength matches your shot, accept that you will route between several, and standardize everything in post so the audience never sees the seams between models. Hubs that expose multiple models behind one workflow make this routing painless, which is why the access layer matters less than the realism craft layered on top.
How to QC AI Video Before You Publish
QC AI video with a fixed frame-by-frame checklist on a high-resolution display, because the artifacts that pass on a phone screen are precisely the ones that expose a clip to a discerning audience. Quality control is the difference between believable and embarrassing.
Treat review as a gate, not a glance. Run every finished clip through the same checks, in the same order, on a display dense enough to reveal high-frequency errors. The list below catches the failures that most often slip through.
| QC Check | What To Look For | Pass Condition |
|---|---|---|
| Identity stability | Face, body, and wardrobe across the full clip | No morph, age shift, or wardrobe change |
| Object permanence | Items hold size, position, and presence | Nothing drifts, scales, or vanishes |
| Physics plausibility | Weight, contact, and fluid behavior | Motion obeys gravity and momentum |
| Texture stability | Skin, hair, fabric under motion | No boiling or swimming surfaces |
| Hands and text | Fingers and any on-screen type | Five-finger anatomy, stable legible text |
| Cadence and blur | Motion smoothness across cuts | Consistent frame rate and motion blur |
| Edge and background | Outlines and environment stability | No warping edges or background churn |
If a clip fails any single check, the correct move is usually to regenerate or to remediate that specific element rather than to ship and hope. Build the checklist into your workflow as a literal step with a sign-off, the same way a publishing team runs an editorial QA pass before a post goes live.
Discipline at this gate is what separates teams whose AI video quietly works from teams whose audience screenshots the glitch.
Should You Disclose AI-Generated Video? Provenance and Trust
Disclose AI-generated video when it could be mistaken for real footage of real events or people, because trust, platform policy, and emerging law increasingly require it, and because honest labeling protects the brand more than a hidden fake ever could. Transparency is now a feature, not a confession.
The disclosure question is no longer purely ethical. Provenance infrastructure has matured into a real standard. The Content Authenticity Initiative reported in 2026 that interoperable provenance moved from principle to practice across the year, with Content Credentials reaching point-of-capture hardware such as Sony's professional broadcast cameras. Regulation is converging on the same point, with the EU AI Act introducing transparency obligations for AI-generated media. Platforms including TikTok and YouTube already surface AI-content and provenance labels.
How disclosure affects trust is itself measurable. A peer-reviewed study published in the International Journal of Human-Computer Studies, drawing on nearly 15,000 observations, found that warning users a video was AI-generated changed how they assessed its accuracy, with effects that depended on each viewer's prior attitudes toward AI. The implication for marketers is that disclosure is not a neutral switch, so design it deliberately rather than bolting on a generic label.
| Disclosure Method | What It Does | When To Use |
|---|---|---|
| C2PA Content Credentials | Cryptographic provenance metadata attached at creation | Anywhere authenticity and chain of custody matter |
| Platform AI label | Self-reported or detected on-platform tag | Required by policy on TikTok, YouTube, and similar |
| Visible on-screen notice | A clear, human-readable AI-made statement | Audience-facing content where mistaking it for real is plausible |
| Invisible watermark | Embedded signal such as a model watermark | Internal tracking and downstream verification |
The pragmatic stance is to make believable video and disclose it where it matters. Realism is for production value and storytelling, not for deception. A brand that produces a polished AI explainer and labels it cleanly keeps both the quality and the credibility, while a brand caught passing synthetic footage as real loses far more than it gained.
How B2B SaaS Teams Use Realistic AI Video Without Looking Cheap
B2B SaaS teams use AI video well by treating it as a production tool inside a content system, namely for product explainers, ad variants, and demo b-roll, while routing anything trust-critical to disclosure or real footage. The win is volume without the visible AI tax.
The pull is obvious. AI video collapses production cost and timeline, letting a lean team ship explainer variants, localized cuts, and social clips at a pace traditional production cannot match. The risk is equally obvious. A cheap-looking clip damages a premium brand faster than no clip at all, which is why the realism workflow in this guide is not optional polish but brand protection.
The teams that get this right share a pattern. They use AI video where it is strong, namely abstract product visualization, motion graphics, ambient b-roll, and rapid ad iteration, and they avoid using it where it is weak, namely fake testimonials, fabricated events, or anything implying a real person said something they did not. They finish every clip to a consistent standard, and they tie each asset to a specific stage of the funnel rather than producing video for its own sake.
That last point is where most programs break down. Video that is not mapped to demand and discovery is just expensive motion. The same logic that governs written content applies, namely that the asset has to be findable in AI answers and search, structured for extraction, and pointed at a money page.
Our breakdown of the best AI content generator tools for SaaS and our guide to SaaS content marketing pricing in 2026 both make the same case, namely that tooling is the easy part and the system around it is what produces pipeline. Teams that want their video and written assets to actually surface in AI-driven search should treat answer engine optimization as the layer that connects production to discovery.
If thin BOFU coverage and content that never converts are costing you pipeline, book a SaaS content strategy call and we will map your highest-intent topics, including the video that supports them, to revenue.
Frequently Asked Questions
Costs range widely, from near-zero on free tiers to a few hundred dollars per minute on premium model credits, far below traditional production. Realism cost lives in iteration and post-production time rather than in the generation fee itself, so budget for regeneration and finishing.
Most realistic single generations run 3 to 25 seconds depending on the model, with coherence degrading as length grows. The reliable approach is to generate short, simple-motion segments of a few seconds and assemble longer sequences in an editor rather than asking for one long take.
There is no universal winner. Sora 2 leads on physics and weight, Veo 3.1 on cinematic polish and audio, and Kling on human motion and complex materials. Production teams route between two or three models by shot type and unify the result in post.
The uncanny valley is the unsettling response viewers feel when a synthetic human looks almost but not quite real. In AI video it is triggered most by subtle facial morphing and unnatural motion, which is why identity locking and cinematic cadence matter more than raw sharpness.
Often not reliably. Studies place untrained human detection near chance for mixed AI media, and even specialized detectors top out in the low-to-mid 90s on benchmark sets while degrading on newer models. This is precisely why responsible disclosure has become a strategic decision.
They look fake mainly from weak temporal consistency and absent physics, which cause flicker, morph, drift, and impossible motion. Resolution improved faster than coherence, so the realism gap now closes through craft, namely prompting, motion control, artifact fixing, and post-production, rather than through waiting for better models.
In most markets yes, with growing transparency requirements such as the EU AI Act and platform labeling rules for realistic AI content. Avoid impersonating real people without consent, and disclose where a viewer could reasonably mistake the video for real footage of real events.
