Which AI Can Make Videos?

Major AI Models and Platforms That Generate Video

Artificial intelligence can synthesize video through deep generative models capable of interpreting prompts, reference still images, or source video clips. The landscape of AI video generators spans distinct architectures, ranging from cinematic world-simulation engines to automated digital avatar presenters and video-to-video style transformers. Runway Resources | How does AI video generation work? 10 Best AI Video Generators in 2025: Tested & Ranked by Experts | AIVeed.io

Among cinematic and photorealistic generative engines, several leading systems define current production workflows:

• OpenAI Sora: A large-scale model designed as a "world simulator," generating high-definition video sequences with complex camera motions, multiple character interactions, and persistent 3D world geometry.
• Runway (Gen-2, Gen-3 Alpha): A creative production suite offering text-to-video, image-to-video, and video-to-video generation alongside fine-grained controls such as motion brush tracking, explicit camera trajectory controls, and multi-asset keyframing. Runway Resources | How does AI video generation work?
• Google Veo: A generative video architecture developed by Google DeepMind that accepts detailed natural language prompts and cinematic terminology (e.g., lens focal lengths, cinematic pacing) to produce photorealistic 1080p footage. 10 Best AI Video Generators in 2025: Tested & Ranked by Experts | AIVeed.io
• Luma Dream Machine: An accessible generative model built on scalable multimodal transformers that specializes in rapid rendering, dynamic camera movements, and smooth temporal transitions from reference images. 10 Best AI Video Generators in 2025: Tested & Ranked by Experts | AIVeed.io
• Kling AI: Developed by Kuaishou, this system is recognized for generating extended video clips (up to several tens of seconds), preserving structural anatomy during complex physical motions and extreme dynamic action. 10 Best AI Video Generators in 2025: Tested & Ranked by Experts | AIVeed.io
• Open-Source Foundations (HunyuanVideo, CogVideoX, Stable Video Diffusion): Self-hostable model weights distributed for local deployment or programmatic fine-tuning, allowing developers and VFX studios to integrate generative pipelines without external vendor lock-in. Section 20.6: Video Diffusion Transformers (DiTs)

Alongside general cinematic generators exists a specialized category of talking-head and corporate avatar platforms, such as Synthesia, HeyGen, and D-ID. Rather than calculating 3D physical interactions or freeform camera paths, these engines use specialized facial reenactment networks to synchronize photorealistic digital avatars with synthetic speech transcripts across dozens of languages. HeyGen vs Synthesia vs D-ID: AI Avatars Compared (2026) | HeyGen

Core Generation Modalities: How Video Models Receive Input

Generative video systems differ significantly in how they interpret input data. Creative control depends heavily on selecting the appropriate input modality rather than relying purely on text descriptions. Runway Resources | How does AI video generation work? Synthetic Media Formats: A Systematic Review | AI Video Generation Blog — Guides & Tips | VidRegen

Text-to-Video (T2V)

In text-to-video synthesis, the user supplies a written descriptive prompt detailing the scene's subject, environment, lighting, artistic style, and movement dynamics. The neural network's language encoder translates text tokens into continuous semantic embeddings that condition a diffusion or transformer backbone. T2V offers maximum creative ideation but provides the lowest mechanical control, as the model must extrapolate layout, character identity, and camera framing from ambiguous text descriptions alone. Synthetic Media Formats: A Systematic Review | AI Video Generation Blog — Guides & Tips | VidRegen AI-Generated Content (AIGC) for Various Data Modalities: A Survey | ACM Computing Surveys

Image-to-Video (I2V)

Image-to-video synthesis conditions the generation process on a static reference frame—either a real photograph, an illustration, or an image generated by a 2D diffusion model such as Midjourney or FLUX. The AI preserves the aesthetic identity, color palette, and composition of the source image while predicting plausible forward or backward temporal movement. I2V provides significantly greater production predictability than T2V because the visual aesthetics are locked into the initial frame before temporal motion is applied. Runway Resources | How does AI video generation work? Synthetic Media Formats: A Systematic Review | AI Video Generation Blog — Guides & Tips | VidRegen

Video-to-Video (V2V) and Motion Transfer

Video-to-video workflows take an existing motion capture or recorded camera clip and alter its stylistic representation while preserving temporal trajectories. Common applications include turning live-action actors into anime, claymation, or 3D digital renders. By leveraging dense optical flow or structural depth maps extracted from the source footage, V2V forces the model to respect frame-by-frame structural consistency, bypassing the chaotic motion drift often seen in purely text-prompted pipelines. Runway Resources | How does AI video generation work? Synthetic Media Formats: A Systematic Review | AI Video Generation Blog — Guides & Tips | VidRegen

Underlying Architecture: How AI Generates Video

Early AI video tools relied on Generative Adversarial Networks (GANs) or standard 2D Recurrent Convolutional Networks, which frequently produced unstable, morphing, and low-resolution clips. Modern high-fidelity generators use Diffusion Transformers (DiTs) and Latent Spatio-Temporal Architectures. Section 20.6: Video Diffusion Transformers (DiTs) VDT: General-purpose Video Diffusion Transformers via Mask Modeling

   Raw Video Frames (Time x Height x Width x Channels)
                           │
                           ▼
          Spatial/Temporal VAE Encoder
                           │
                           ▼
        Latent Spatio-Temporal Patches + Positional Embeddings
                           │
                           ▼
     ┌──────────────────────────────────────────────────┐
     │           Diffusion Transformer (DiT)            │
     │  - Spatial Self-Attention (within frames)        │
     │  - Temporal Self-Attention (across time frames)  │
     │  - Cross-Attention (Conditioning: Text / Image)  │
     └──────────────────────────────────────────────────┘
                           │
             Iterative Denoising Steps ($T \to 0$)
                           │
                           ▼
          Spatial/Temporal VAE Decoder
                           │
                           ▼
                 Final High-Definition Video

1. Spatio-Temporal Latent Space Compression

Processing uncompressed video directly at $1920 \times 1080$ resolution across dozens of frames is computationally intractable for consumer or even standard enterprise hardware. Modern generators first compress the raw video tensor into a lower-dimensional latent representation using a 3D Variational Autoencoder (VAE). The 3D VAE compresses both spatial dimensions ($H \times W$) and the temporal dimension ($T$), converting raw pixel grids into condensed latent tokens that capture color, edges, and motion trajectories. Section 20.6: Video Diffusion Transformers (DiTs) Adaptive Caching for Faster Video Generation with Diffusion Transformers

2. Spatio-Temporal Attention Mechanisms

While 2D image models apply standard self-attention across an $(x, y)$ coordinate grid, video synthesis requires reasoning over time:

Video DiTs flatten the latent video tensor into discrete spacetime patches, treating a video as a sequence of tokens analogous to words in a large language model. Within each transformer block, two decoupled attention layers typically operate:

• Spatial Attention: Evaluates relationships between visual elements within the same static frame, enforcing anatomical correctness, sharp edges, and coherent lighting. VDT: General-purpose Video Diffusion Transformers via Mask Modeling Accelerating Spatio-Temporal Attention for Video Diffusion on TPUs - Google Developers Blog
• Temporal Attention: Evaluates the path of visual elements across sequential time steps, calculating where a walking leg, a moving vehicle, or a panning background must travel in frame $t+1$ relative to frame $t$. VDT: General-purpose Video Diffusion Transformers via Mask Modeling Accelerating Spatio-Temporal Attention for Video Diffusion on TPUs - Google Developers Blog

3. Progressive Denoising and Guidance

During inference, the latent representation begins as pure Gaussian noise. Conditioned on cross-attention text tokens or image reference vectors, the model performs iterative denoising across a fixed schedule of steps. At each step, the neural network predicts and subtracts the noise component, gradually bringing coherent objects, physical momentum, and camera parallax out of the static. Section 20.6: Video Diffusion Transformers (DiTs) AI-Generated Content (AIGC) for Various Data Modalities: A Survey | ACM Computing Surveys

Comparison of Leading Video AI Platforms

Different video generation tools serve distinct commercial and artistic objectives. The table below compares the primary categories and tools available:

Platform / Tool	Primary Modalities	Core Architecture	Typical Use Case	Key Strength
OpenAI Sora	Text-to-Video, Image-to-Video	Diffusion Transformer (DiT)	High-end cinematic world generation	Extended temporal coherence, complex dynamic framing
Runway (Gen-3 Alpha)	T2V, I2V, V2V	Latent Diffusion / Spatio-Temporal Transformer	Creative directing, commercial VFX, filmmaking	Robust camera controls, motion brush, keyframe pacing [1](#source-1 "Runway Resources
Google Veo	T2V, I2V	Multimodal Diffusion Transformer	Film production, advertising, visual concepts	Understands cinematic terminology, high visual fidelity [2](#source-2 "10 Best AI Video Generators in 2025: Tested & Ranked by Experts
Kling AI	T2V, I2V	Large Spatio-Temporal Diffusion	Action sequences, extended narrative shots	High anatomical stability during intense motion [2](#source-2 "10 Best AI Video Generators in 2025: Tested & Ranked by Experts
Luma Dream Machine	T2V, I2V	Scalable Multimodal Transformer	Prototyping, dynamic camera moves	Rapid inference speed, smooth camera transitions [2](#source-2 "10 Best AI Video Generators in 2025: Tested & Ranked by Experts
HeyGen / Synthesia	Text-to-Speech Avatar	Neural Facial Reenactment & Audio-Visual Alignment	Corporate training, multilingual marketing, presentations	Photorealistic lip-syncing and synthetic speech [4](#source-4 "HeyGen vs Synthesia vs D-ID: AI Avatars Compared (2026)
HunyuanVideo / CogVideoX	T2V, I2V (Open Weights)	Open-Source DiT Backbones	Local deployment, specialized studio fine-tuning	Self-hostable, customizable, zero subscription lock-in Section 20.6: Video Diffusion Transformers (DiTs)

Technical Limitations and Edge Cases

Despite rapid advancements in spatio-temporal modeling, AI video generators are constrained by fundamental architectural limitations:

1. Physics Engine Hallucinations

Generative models do not contain explicit physics simulators; they rely on probabilistic visual patterns learned from vast training corpora. Consequently, models frequently violate real-world physical laws during complex interactions:

• Object Permanence: An object occluded behind a tree or turning away from the camera may reappear with altered colors, different geometry, or entirely disappear.
• Fluid and Particle Dynamics: Pouring water, breaking glass, or smoke dispersion often degenerates into non-Newtonian melting or morphing artifacts.
• Collision Response: High-speed impacts (e.g., a car collision or athletic tackle) often exhibit rubberized deformation or mutual clipping rather than realistic kinetic energy transfer.

2. Anatomical and Articulation Errors

Human and animal locomotion requires precise skeletal constraints. When characters perform rapid or non-standard movements—such as running across uneven ground, dancing, or gesturing—AI models often introduce extra limbs, fused fingers, or feet that slide across ground textures without friction (the "moonwalking" artifact).

3. Temporal Consistency and Identity Drift

Generating a short 4- to 10-second shot is technically distinct from generating a coherent scene. As inference extends over hundreds of frames, small errors in the latent attention map compound. This drift causes facial features, clothing details, and environmental landmarks to morph gradually, posing a significant hurdle for multi-shot narrative continuity.

4. Computational Overhead and Cost

Generating a 5-second video clip at 24 frames per second requires rendering 120 unique latent representations. The computational demand scales quadratically with video length and token count when using standard full spatio-temporal attention. Inference often requires specialized data-center GPUs (such as NVIDIA H100s or enterprise TPUs), making commercial generation substantially more expensive than static image generation. Enterprise subscriptions and API costs often scale on a per-second credit basis, running anywhere from several cents to several dollars per rendered generation depending on resolution and model depth. Adaptive Caching for Faster Video Generation with Diffusion Transformers Accelerating Spatio-Temporal Attention for Video Diffusion on TPUs - Google Developers Blog

Workflow Integration and Creative Control Methods

Professional visual artists rarely rely on a single text prompt to produce finished video. Advanced workflows combine multiple steering mechanisms to achieve deterministic results:

1. First-Frame and Last-Frame Keyframing: Instead of letting the model freely guess where a scene ends, editors provide both a starting image and an ending target image. The diffusion model then computes the mathematically coherent interpolation between the two states, ensuring that camera and actor positions align with adjacent edits. Runway Resources | How does AI video generation work?
2. Explicit Camera Trajectory Commands: Modern interfaces allow directors to specify exact virtual camera paths—including panning (horizontal sweep), tilting (vertical sweep), dollying (moving closer or further), and roll rotation. This separates scene action from camera movement, preventing the visual distortions that occur when a model confuses character motion with camera displacement. Runway Resources | How does AI video generation work?
3. Regional Motion Brushes: Creators can paint over specific regions of an image (such as flowing water, a fluttering flag, or an actor's eyes) and assign independent motion vectors. Unpainted areas remain static, eliminating unwanted background warp and localized artifacts. Runway Resources | How does AI video generation work?
4. Hybrid Upscaling and Frame Interpolation: Because native high-resolution generation remains compute-intensive, production pipelines frequently generate footage at 720p or lower frame rates (12 to 16 fps), subsequently passing the output through dedicated temporal frame-interpolation models (e.g., RIFE) and spatial super-resolution upscalers to reach broadcast-ready 4K 60 fps standards.

The state of AI video generation

Several AI systems can make video today, and they fall into three distinct families: frontier generative video models that synthesize footage from a text prompt or image (Google's Gemini Omni Flash, Runway Gen-4.5, Kuaishou's Kling 4.0, ByteDance's Seedance, and open-weight models like Wan and HunyuanVideo); avatar and presenter platforms that turn a script into a talking-head video (Synthesia, HeyGen); and AI-assisted editors that assemble, caption, and restyle footage you already have. Which one you want depends entirely on whether you need invented footage, a spokesperson reading a script, or help cutting real material — these are genuinely different products that happen to share a label.

One important piece of context: this field moves fast enough that lineups shift within months. OpenAI's Sora, for a long stretch the most widely recognized name in text-to-video, was discontinued as a product on April 26, 2026, with its API scheduled for shutdown later that year. Launching Sora responsibly | OpenAI Sora (text-to-video model) Google, meanwhile, moved from the Veo line toward its Gemini Omni series, deprecating the Veo 3.1 preview models in the Gemini API. Generate videos with Veo 3.1 in Gemini API  |  Google AI for Developers Any list of "the best AI video generator" is a snapshot, not a stable ranking.

Who makes what

What "generating a video" actually means

Modern generative video models are diffusion or diffusion-transformer systems trained on enormous video corpora. Rather than drawing frames one at a time, they denoise a compressed latent representation of an entire clip at once, which is why temporal coherence — the thing that made early AI video flicker and morph — is a property of the architecture rather than a post-processing fix. The compression is done by a video autoencoder; Alibaba's Wan 2.2, for instance, publishes a VAE achieving a 16×16×4 spatial-temporal compression ratio, which is what makes high-definition generation tractable on consumer hardware. GitHub - Wan-Video/Wan2.2: Wan: Open and Advanced Large-Scale Video Generative Models · GitHub

Three input modes dominate, and knowing the difference matters more than knowing brand names:

1. Text-to-video. You write a prompt; the model invents everything. Maximum freedom, minimum control. Best for exploration and for shots where nothing specific has to match.
2. Image-to-video. You supply a starting frame — often made with an image model — and the video model animates it. This is how most professional work gets done, because you can iterate cheaply on a still until the composition is right, then spend the expensive generation on motion only.
3. Reference and keyframe control. You give the model specific characters, objects, first and last frames, or even a source video whose camera motion and pacing you want borrowed. Kling 4.0's "Omni Reference" accepts combinations totaling up to 15 reference assets (up to 10 images, 5 videos, and 7 subjects) alongside up to 10 keyframe inputs. Kling AI Officially Launches Next-Generation Kling 4.0, Raising the Bar for Professional-Grade Video Models | Kuaishou Technology Google's Omni Flash supports interpolation between a specified first and last frame. Generate and edit videos with Gemini Omni Flash  |  Gemini API  |  Google AI for Developers

A fourth mode is increasingly significant: native audio. Earlier generations produced silent clips that needed sound design bolted on afterward. Google's Veo 3.1 generated dialogue and sound effects as part of the same pass, and the Omni line continues this, accepting audio as an input modality as well. Generate videos with Veo 3.1 in Gemini API  |  Google AI for Developers Generate and edit videos with Gemini Omni Flash  |  Gemini API  |  Google AI for Developers Some open-weight models have moved the same direction, with LTX-2.3 generating synchronized audio and video jointly. ltx-video-docs/docs/comparisons/open-source-ai-video-models.md at main · quanluo/ltx-video-docs · GitHub

The frontier closed models

Google's Gemini Omni Flash (model ID gemini-omni-1.1-flash) represents a design shift worth understanding, because it reframes video generation as a conversation rather than a one-shot request. It is natively multimodal — text, image, audio, and video go in together — and supports iterative refinement through an Interactions API, so you describe a change in plain language and the model applies it while preserving what you wanted kept. Google also pitches it as grounded in Gemini's broader world knowledge, meaning physical plausibility plus historical and cultural context. Output supports 16:9 and 9:16 aspect ratios at 360p through upscaled 4K, with 720p as the default. Generate and edit videos with Gemini Omni Flash  |  Gemini API  |  Google AI for Developers Gemini Omni — Google DeepMind It is surfaced to consumers through Gemini and to business users through Google Vids, where it also powers personal avatars. Google Vids gets powerful upgrades with Gemini Omni

Runway has been shipping video models longer than almost anyone — Gen-1 was the first publicly available video generation model — and its arc shows what the industry has been chasing. Gen-4 was pitched specifically at world consistency: keeping the same character and object appearance across multiple shots, given reference images. Runway Gen-4: AI Video Generation with World Consistency With new Gen-4 model, Runway claims to have finally achieved consistency in AI videos - Ars Technica Gen-4.5, released December 1, 2025, took the top spot on the Artificial Analysis Text-to-Video leaderboard with 1,247 Elo points, ahead of Google Veo 3 (1,226), Kling 2.5 (1,225), and OpenAI Sora 2 Pro (1,206) — rankings determined by blind human pairwise preference voting. Runway Gen-4.5: State-of-the-Art AI Video Generation Runway's platform also distributes third-party models alongside its own and supports control modes like image-to-video, keyframes, and video-to-video.

Kling 4.0, launched October 15, 2026, is aimed explicitly at professional production rather than casual creation. Its headline features are native single-pass generation of up to 30 seconds — long enough for genuine long takes rather than the roughly 8-second clips typical of earlier models — plus 10-bit HDR output, 4K and 21:9 ultrawide formats, prompt inputs up to 8,000 tokens, and planned video extension to around two minutes. Kuaishou frames the competitive question as having shifted from can AI generate video to can it do so reliably and with precise control, which is a fair summary of where the field is. Kling AI Officially Launches Next-Generation Kling 4.0, Raising the Bar for Professional-Grade Video Models | Kuaishou Technology Kling AI Launches 3.0 Model, Ushering in an Era Where Everyone Can Be a Director | Kuaishou Technology

Open-weight models you can run yourself

If you need to avoid per-clip costs, keep footage off third-party servers, or fine-tune on your own material, open-weight models are the alternative. Alibaba's Wan series includes a 5B hybrid text-and-image-to-video model sized for consumer GPUs, Tencent's HunyuanVideo was published as an open video foundation model with performance its authors position as comparable to closed systems, and Lightricks' LTX-Video line emphasizes speed and, more recently, joint audio-video generation. GitHub - Wan-Video/Wan2.2: Wan: Open and Advanced Large-Scale Video Generative Models · GitHub HunyuanVideo: A Systematic Framework For Large Video Generative Models ltx-video-docs/docs/comparisons/open-source-ai-video-models.md at main · quanluo/ltx-video-docs · GitHub

The tradeoffs are real. Self-hosting demands substantial GPU capacity, and output quality at the top of the closed-model range generally remains ahead. Critically, "open weights" does not reliably mean "free for commercial use" — license terms vary across releases, and a meaningful share of open-weight models carry commercial conditions such as non-commercial clauses or revenue and user caps. Open-Weight AI Licenses for Commercial Use | OpenWeights Read the license for the specific model and version you intend to ship with.

Avatar platforms are a separate category

When someone asks which AI can create videos for training, onboarding, or product explainers, the answer is often not a generative video model at all. Platforms like Synthesia and HeyGen take a script and render a photorealistic presenter delivering it, with multi-language voice and lip-sync, brand templates, and team workflows built for producing hundreds of near-identical videos at scale. Synthesia Enterprise - Create AI Videos at Scale Enterprise AI Video Creation Platform for Global Teams | HeyGen Google has brought comparable avatar functionality into Vids. Google Vids gets powerful upgrades with Gemini Omni

The distinction matters practically: a frontier video model will happily invent a person who never existed in an unpredictable setting, which is wrong for corporate communication. An avatar platform gives you a consistent, approved presenter and predictable output — at the cost of being able to generate essentially nothing else.

What still goes wrong

Every current system shares a set of failure modes, and Runway publishes an unusually candid list for Gen-4.5 that generalizes well across models:

• Causal reasoning errors — effects sometimes precede causes, such as a door swinging open before the handle is pressed.
• Object permanence failures — objects vanish or appear across frames, especially after being occluded.
• Success bias — actions disproportionately succeed, so a badly aimed kick still scores. Runway Gen-4.5: State-of-the-Art AI Video Generation

Beyond these, expect difficulty with legible text inside the frame, hands and fine manipulation, precise multi-shot continuity without reference inputs, and anything requiring exact counts. Clip length remains a hard constraint for narrative work: even with Kling's 30-second passes and extension features, long-form output is assembled from generated segments, not produced whole. Budget for high rejection rates — the honest unit of cost is not "per generation" but "per usable clip," which can be several times higher.

Provenance, consent, and legal exposure

Generated video increasingly ships with embedded provenance signals. Google's SynthID adds an imperceptible watermark to AI-generated images and video segments, and Google states that clips generated with Gemini Omni in Vids carry one. Google SynthID — Google DeepMind Google Vids gets powerful upgrades with Gemini Omni The related SynthID-Image system has been described as having watermarked over ten billion images and video frames. SynthID-Image: Image watermarking at internet scale Separately, the open C2PA Content Credentials standard attaches a signed, cryptographically verifiable provenance record to a file; OpenAI documents both C2PA and SynthID signals in its generated content and offers verification tooling. Content provenance | OpenAI API

Disclosure is also becoming a regulatory matter rather than a voluntary one. Article 50 of the EU AI Act, which imposes marking obligations on providers of generative AI content, took effect on August 2, 2026 — though researchers note that practical verification of such marks remains an unsolved problem. Watermarks Without Verification: AI Text Watermarking After the EU AI Act Watermarks can also be degraded by re-encoding, cropping, or deliberate removal, so treat them as a provenance signal, not proof of origin.

Two further cautions apply regardless of jurisdiction. First, likeness and voice: generating a recognizable real person, or cloning a voice, requires that person's consent and may implicate publicity, data-protection, or defamation law. Second, copyright status of output varies by country and remains genuinely unsettled in several jurisdictions, as does the question of training-data liability. If you are producing commercial work, paid advertising, or anything involving real people, get a qualified lawyer in your jurisdiction to review your workflow rather than relying on a platform's terms of service alone.

Choosing among them

Start from the output you need, not the model leaderboard. If you want invented cinematic footage and have time to iterate, pick one frontier model and learn it deeply — prompt craft and reference discipline affect results more than the choice between the top three. If you need a presenter reading approved copy in twelve languages, use an avatar platform. If your footage already exists, an AI-assisted editor will serve you better than any generator. And if data residency, cost at volume, or fine-tuning on proprietary material is the binding constraint, accept somewhat lower peak quality and run open weights locally.

Finally, treat the stack as impermanent. Models are deprecated, products get shut down, pricing changes, and the ranking at the top reshuffles every few months. Generate videos with Veo 3.1 in Gemini API  |  Google AI for Developers Launching Sora responsibly | OpenAI Build workflows that keep source assets, prompts, and reference images under your own control so that switching generators is a configuration change rather than a rebuild.