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Top 10 Best AI Brand Fashion Video Generator of 2026
Top 10 ranking of ai brand fashion video generator tools for fashion teams, with technical comparisons of Rawshot, D-ID, and Runway.
How we ranked these tools
Core product claims cross-referenced against official documentation, changelogs, and independent technical reviews.
Analyzed video reviews and hundreds of written evaluations to capture real-world user experiences with each tool.
AI persona simulations modeled how different user types would experience each tool across common use cases and workflows.
Final rankings reviewed and approved by our editorial team with authority to override AI-generated scores based on domain expertise.
Score: Features 40% · Ease 30% · Value 30%
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Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
Rawshot
Purpose-built generation of fashion brand video assets from product visuals with brand-aligned creative direction.
Built for fashion brands and ecommerce teams producing frequent short video creatives from product imagery..
D-ID
Editor pickScene-based generation requests that combine character and asset inputs into a programmable video spec.
Built for fits when brand teams need API automation for fashion video variants with governance controls..
Runway
Editor pickProgrammable API lets teams launch and manage video generation jobs from internal systems.
Built for fits when fashion teams need automated, API-led video generation within controlled creative workflows..
Related reading
Comparison Table
This comparison table evaluates AI brand fashion video generator tools across integration depth, data model design, and automation plus API surface. Each entry is also mapped to admin and governance controls such as RBAC, audit log coverage, and provisioning workflows, with notes on configuration and extensibility for production throughput. Tools like Rawshot, D-ID, Runway, Pika, and Luma AI are included to show how vendor choices change schema, automation options, and rollout constraints.
Rawshot
AI video generation for fashion marketingRawshot generates AI fashion brand videos from product images and brand-style direction.
Purpose-built generation of fashion brand video assets from product visuals with brand-aligned creative direction.
Rawshot focuses on producing fashion-ready video assets suitable for brand and ecommerce use, converting starting imagery into motion content that supports product storytelling. It’s aimed at fashion brands, creative teams, and marketers who want consistent, on-brand visuals at speed. The workflow emphasizes turning existing assets into multiple video variations for campaigns and catalog-style content.
A key tradeoff is that results depend on the quality and suitability of the input visuals and the clarity of the style direction you provide. Rawshot is especially useful when you need multiple short video creatives for launches, seasonal updates, or rapid ad testing. Instead of scheduling new shoots, you can iterate quickly and update creative assets as trends and campaign needs shift.
- +Fashion-focused video generation workflow for marketing-ready outputs
- +Quick transformation of product imagery into motion creatives for campaign iteration
- +Brand-alignment via style/creative direction to maintain visual consistency
- –Video quality is constrained by the input images and provided creative direction
- –Generative output may require additional review/tuning for perfect brand matching
- –Best suited to fashion/ecommerce use cases rather than general-purpose video creation
Ecommerce marketing teams
Create product ad videos from catalog images
Faster creative production cycles
Fashion brand creative teams
Iterate launch concepts without reshoots
More concepts in less time
Show 2 more scenarios
Social media managers
Produce on-brand fashion clips for Reels
More consistent social output
Turns fashion visuals into short, brand-consistent motion content suitable for social posting schedules.
Content ops at fashion retailers
Refresh seasonal creative quickly
Timely seasonal updates
Recreates seasonal product video content efficiently using existing imagery and updated style direction.
Best for: Fashion brands and ecommerce teams producing frequent short video creatives from product imagery.
More related reading
D-ID
API-first video generationProvides an API and studio tooling to generate fashion-style video from images and scripts using configurable input templates and model options.
Scene-based generation requests that combine character and asset inputs into a programmable video spec.
D-ID fits teams that need integration depth across asset ingestion, prompt configuration, and generation orchestration for repeated fashion content. The data model supports scene and character inputs so video output can be generated from structured requests rather than manual steps. The automation and API surface enable throughput planning by running batch jobs and reusing stable configuration for edits.
A concrete tradeoff is that high creative variance still depends on prompt and input quality, so governance needs review checkpoints for brand and model drift. D-ID fits situations where an in-house brand system already stores images, wardrobe variants, and scripts in a structured schema that can be mapped to generation fields. For teams that want measurable control, the admin workflow and audit log practices should be aligned to RBAC roles that separate creation from approval.
- +API-driven video generation supports automation and repeatable fashion workflows
- +Structured inputs let teams map wardrobe assets to scene parameters consistently
- +Extensibility via configuration supports batch runs and variant generation
- –Creative outcomes can vary with input quality and prompt precision
- –Governance requires deliberate review loops to avoid brand drift
Brand marketing operations
Automate campaign fashion video variants
Faster campaign production cycles
Creative technology teams
Integrate D-ID into asset pipelines
Lower manual editing workload
Show 1 more scenario
Agency production teams
Enforce approvals on generation outputs
Consistent brand governance
Use RBAC roles and audit log review to route drafts to brand approvers.
Best for: Fits when brand teams need API automation for fashion video variants with governance controls.
Runway
Creator-to-API studioOffers video generation and editing workflows with an API for automation, plus role-based access controls for team governance.
Programmable API lets teams launch and manage video generation jobs from internal systems.
Runway’s core capability is producing short brand video variations from text prompts, then refining results through iterative editing passes that keep visual intent aligned. Integration breadth is strongest where teams treat generation as a pipeline step that feeds downstream review, rendering, and distribution systems. Automation and extensibility show up through an API and programmatic job control that can be paired with internal asset stores. The data model supports prompt, media references, and generation parameters in a way that maps cleanly to schema-based provisioning.
A tradeoff appears in governance granularity for brand studios that need deep per-user controls across every stage of generation and editing. Teams with strict RBAC and audit log requirements may need to add external controls around access to projects, prompt libraries, and generated artifacts. Runway fits best when the fashion team runs repeatable creative direction cycles and needs predictable throughput for batch generation. It is less ideal when the workflow requires fully offline processing or tight local data residency without external service dependencies.
- +API-driven generation enables job-based automation for video production pipelines
- +Structured parameters for prompt and media support repeatable fashion storytelling iterations
- +Workflow fit for review handoff reduces manual steps between edits and exports
- +Extensibility supports integrating asset management and downstream rendering systems
- –Fine-grained admin controls across all stages can be limited for strict governance
- –Workflow latency and throughput tuning depends on how jobs are batched
- –Hard requirements for local-only processing may not match typical service usage
Brand creative operations teams
Batch-generate seasonal product teaser variants
Faster variant approval throughput
Studio production engineers
Integrate Runway into asset pipelines
Fewer manual handoffs
Show 2 more scenarios
Fashion marketing teams
Maintain visual continuity across takes
Consistent brand visuals
Iterative editing preserves scene direction while producing new motion takes for campaigns.
Compliance-minded creative teams
Control access to prompt libraries
Tracked creative approvals
External RBAC and audit logging can gate who triggers generation and edits projects.
Best for: Fits when fashion teams need automated, API-led video generation within controlled creative workflows.
Pika
Prompt-to-videoDelivers AI video generation with a prompt-to-video workflow and project controls that support batch output creation for fashion brand assets.
Prompt reuse with configurable generation settings for repeatable fashion motion across brand campaigns
Pika is a brand-focused AI fashion video generator that converts text prompts into short fashion motion clips with style consistency. The main distinction is the workflow control around generated outputs, including reusable prompts and repeatable scene direction for brand campaigns.
Pika’s core capabilities center on video synthesis, prompt-based iteration, and asset-like reuse through configurable generation settings. For brand teams, value comes from how generation inputs map into a repeatable data model that can be driven by automation and integrated tooling.
- +Prompt-to-video generation supports repeatable fashion scene direction
- +Generation settings provide controllable output constraints for brand consistency
- +Reusable prompt patterns reduce manual iteration across campaigns
- +Fast turnaround supports rapid storyboard testing for fashion edits
- –Integration depth depends heavily on available automation surface
- –Data model visibility is limited for teams needing strict schema governance
- –Versioning and audit controls are not explicit for enterprise RBAC needs
- –Throughput can be uneven during high-demand generation runs
Best for: Fits when brand teams need repeatable prompt-driven fashion video iterations under light automation.
Luma AI
3D to videoEnables 3D capture and video generation from real product imagery with an API surface for automating scene-to-video pipelines.
Reference image conditioning with motion configuration for brand-consistent fashion video generation.
Luma AI generates brand fashion video outputs from AI image and text inputs for campaign-ready visuals. Production value comes from controllable generation settings that map inputs into a repeatable data model of prompts, reference imagery, and motion parameters.
Integration depth matters for repeat runs because Luma AI supports workflow automation via an API surface designed for external systems. Automation and extensibility center on programmable input provisioning, iteration control, and downstream asset handling.
- +API-driven generation enables scripted batch runs across campaigns
- +Prompt and reference inputs support repeatable output configurations
- +Reference-based motion parameters help maintain brand visual continuity
- +Structured generation settings reduce manual iteration cycles
- –Schema for outputs can require custom mapping for production pipelines
- –Fine-grained motion control depends on available parameter coverage
- –Audit and governance controls are limited by external orchestration needs
- –Throughput can bottleneck during high parallel batch generation
Best for: Fits when fashion teams need automated, repeatable video generation wired to existing asset pipelines.
HeyGen
Scripted video APISupports scripted video generation and avatar-based video creation through an API while providing admin tooling for account management.
Template-driven avatar video generation with job automation for batch production at scale.
HeyGen fits brand teams that need repeatable fashion video variations with controllable casting, outfits, and scene inputs. The workflow centers on avatar and media generation using a defined asset and script pipeline, then exporting finished video files for campaign use.
HeyGen’s differentiation comes from its automation surface around video jobs and reusable templates, which reduces manual re-editing across SKU or lookbook iterations. Integration depth matters most for production lines that must provision assets, control user access, and run high-throughput render jobs.
- +Script to video pipeline supports repeatable fashion look variations
- +Avatar and media workflows reuse assets across multiple campaigns
- +Automation via video job creation improves throughput for batch renders
- +Extensibility supports embedding into production workflows through integrations
- –Automation and API surface depth depends on specific workflow objects
- –Governance coverage like RBAC granularity can be limited for large orgs
- –Audit logging visibility may not cover every edit and asset mutation
- –Template configuration can require careful data schema alignment
Best for: Fits when fashion teams need controlled avatar video generation with automation and workflow integration.
Synthesia
Enterprise video APIProvides a video generation platform with API access for producing brand videos from scripts and assets with enterprise governance features.
API-driven video generation with asset and character provisioning for brand-consistent fashion outputs.
Synthesia centers on brand-controlled video generation where the data model maps scripts, scenes, and assets into reproducible outputs. The authoring flow combines a structured prompt and media inputs with character selection so teams can standardize fashion brand deliverables like campaign intros and product walk-throughs.
Integration depth is driven by an API and automation hooks that support programmatic provisioning of projects, assets, and render runs. Governance is handled through workspace controls and identity features that enable repeatable production with auditability for operational teams.
- +API supports scripted video generation from external systems
- +Character and brand assets can be configured per workspace
- +Automation surface supports batch rendering workflows
- +Workflow outputs stay consistent via a structured data model
- +Extensibility via integrations with internal content pipelines
- –Complex brand kits require careful asset and naming governance
- –Custom scene logic depends on how templates are authored
- –Higher throughput needs queue planning to avoid render backlogs
- –Template updates can require coordination across teams
Best for: Fits when fashion teams need repeatable brand videos driven by automation and governed access.
InVideo AI
Template-driven generationUses AI-based video generation and editing templates with an automation-friendly workflow for producing short fashion brand clips from prompts.
Template-based fashion clip generation that reuses brand assets for consistent campaign output.
InVideo AI is a fashion-focused video generator that turns brand assets and prompts into short-form clips with controllable visuals. It supports video generation plus editing workflows, including template-style production and layered media for consistent brand output.
Integration depth depends on its automation features, where API-driven provisioning and batch generation matter more than UI-only creativity. For brand fashion use cases, the value centers on repeatable configuration, reuse of assets, and governance-ready production pipelines.
- +Asset-driven generation supports brand consistency across fashion video batches
- +Editing workflows enable iterative revisions without rebuilding projects
- +Reusable templates reduce variance across recurring campaign formats
- +Automation for batch output supports higher throughput for social publishing
- –Automation and API surface can limit deep custom pipelines without workarounds
- –Data model visibility for schemas is limited for strict internal governance
- –Role-based controls and audit log details are not clearly surfaced for admins
- –Complex multi-shot scenes may require manual correction to match brand rules
Best for: Fits when teams need repeatable fashion video generation with controlled inputs and light workflow automation.
Kaiber
Reference-guided videoGenerates videos from text, images, and references and supports production-style iteration for fashion brand visuals.
API-driven generation runs that take prompt and reference parameters for controlled batch production.
Kaiber generates brand fashion videos from text and visual prompts, then applies style consistency across scenes. It emphasizes a repeatable data model for prompts, references, and output settings used during generation.
Integration depth centers on automation via API endpoints, webhook workflows, and parameterized job runs that support batch throughput. Governance coverage includes project scoping and access controls, with auditability tied to job history and admin-managed resources.
- +Parameterized generation jobs support repeatable fashion-specific output settings
- +API-oriented workflow enables automated prompt runs and batch throughput
- +Style and reference inputs improve scene-to-scene visual consistency
- +Project scoping supports RBAC-style separation across teams
- –Complex prompt and reference schemas can raise onboarding overhead
- –Governance controls rely on job history rather than fine-grained policy tooling
- –Extensibility depends on API surface coverage for niche pipeline steps
Best for: Fits when fashion teams need automated video generation with API-controlled job parameters.
Pixverse
Image-to-videoCreates image-to-video and text-to-video outputs with batch-friendly controls for generating sets of fashion marketing clips.
Schema-based generation inputs that standardize brand asset usage across fashion video runs.
Pixverse fits teams that need AI brand fashion video generation with production controls and repeatable outputs. The core workflow focuses on transforming brand assets into video sequences using a defined generation schema and configurable prompts.
Integration depth matters because automation is more valuable when Pixverse supports a documented API surface, consistent asset inputs, and predictable parameters for batch throughput. Governance matters because brand consistency improves when Pixverse can enforce access controls and track generation activity through auditable logs.
- +Configurable generation schema for consistent brand fashion video outputs
- +Asset-driven workflow that keeps brand inputs explicit across runs
- +Automation-friendly parameterization for repeatable batch generation
- +Extensibility via API inputs that map to a structured prompt model
- –Integration depth is limited if API lacks granular video controls
- –Data model clarity can be weak if parameters lack strict versioning
- –Governance controls are constrained if RBAC and audit logs are minimal
- –Throughput is harder to predict when queue behavior is opaque
Best for: Fits when brand teams need controlled, repeatable fashion video generation via automation and API.
How to Choose the Right ai brand fashion video generator
This buyer's guide covers AI brand fashion video generator tools across Rawshot, D-ID, Runway, Pika, Luma AI, HeyGen, Synthesia, InVideo AI, Kaiber, and Pixverse. It maps integration depth, data model, automation and API surface, and admin and governance controls to concrete capabilities like programmable generation jobs and scene-based specs. It also highlights who each tool fits, where outputs typically drift, and which controls matter for brand consistency.
AI generators that turn fashion brand assets into controlled marketing motion
An AI brand fashion video generator creates short fashion marketing clips from product imagery, reference images, and scripts, then applies brand look constraints through a repeatable input spec. Tools like Rawshot focus on product-image to fashion-ready motion with style and creative direction, while D-ID shifts control to scene-based requests that combine character and asset inputs into a programmable video spec.
These tools solve high-volume iteration problems like producing consistent SKU or lookbook variants without repeated reshoots. Teams also use them to reduce manual re-editing by launching generation as structured jobs and templates, which Runway and HeyGen support with job-based automation.
Integration, data model, automation surface, and governance controls to evaluate
The best fit depends on how a tool represents inputs and outputs in a schema you can automate. Rawshot aligns to fashion workflows using brand look direction, while Luma AI and Pixverse standardize generation inputs with reference conditioning and schema-based parameters.
Operational fit depends on automation throughput and admin controls, since many systems require deliberate review loops to prevent brand drift. D-ID, Runway, and Synthesia score higher where API-led generation and workspace controls can support repeatable brand deliverables.
Programmable generation requests and job orchestration via API
Runway and D-ID support API-driven generation that teams can launch as structured jobs from internal systems. This matters because fashion production pipelines need repeatable runs for edits, localized variants, and batch output rather than one-off renders.
Scene-based or schema-driven data model for repeatable video specs
D-ID uses scene-based generation requests that combine character and asset inputs into a programmable video spec, which supports consistent campaign edits. Pixverse and InVideo AI emphasize schema-based generation inputs and template-style production, which reduces variance when teams reuse brand assets across runs.
Brand alignment mechanisms using style direction or reference conditioning
Rawshot keeps brand look aligned through style and creative direction while converting product imagery into fashion marketing motion. Luma AI adds reference image conditioning paired with motion configuration, which helps maintain brand visual continuity across reruns.
Template reuse and parameterized generation settings for campaign iteration
Pika focuses on reusable prompt patterns and configurable generation settings for repeatable fashion motion across campaigns. Kaiber and HeyGen also emphasize parameterized job runs and template-driven avatar pipelines that reduce manual re-editing across SKU or look variations.
Admin governance controls such as workspace controls and access management
Synthesia includes enterprise governance features with workspace controls and identity features that support auditable production workflows. Runway offers role-based access controls for governance, while HeyGen and Pixverse mention governance limits where RBAC granularity or audit log coverage can be constrained.
Auditability and review loops for preventing brand drift
D-ID flags governance needs because creative outcomes can vary with input quality and prompt precision, which requires deliberate review loops. HeyGen and InVideo AI note that audit logging visibility or policy coverage can be limited, so governance planning should include where approvals occur in the production workflow.
Match the tool’s automation surface and governance model to the production workflow
Start by mapping the production pipeline to the tool’s input and output schema so automation can stay repeatable. If the workflow needs scene-by-scene control with a programmable video spec, D-ID fits because scene requests combine character and asset inputs into versionable generation parameters.
Next, validate the control points that stop brand drift, including access management and auditability. Synthesia and Runway support governance through workspace controls and role-based access controls, while Rawshot prioritizes brand-aligned fashion output that depends on input image quality and creative direction tuning.
Choose the data model style that matches how teams author fashion shots
If production is organized by scenes, prioritize D-ID because it builds a scene-based generation request that combines character and wardrobe assets into a programmable spec. If production is organized by reusable prompt patterns and motion constraints, prioritize Pika because it pairs prompt reuse with configurable generation settings.
Validate automation and API coverage for batch generation and orchestration
If generation needs to run from internal systems as jobs, prioritize Runway because it provides an API surface for launching and managing generation jobs. If production needs API-driven scripted generation with asset provisioning, prioritize Synthesia because its workflow maps scripts, scenes, and assets into reproducible outputs.
Confirm brand alignment controls and where review tuning happens
If brand alignment is mainly style direction on product imagery, prioritize Rawshot because it is purpose-built for fashion brand videos and aligns output using style and creative direction. If brand alignment depends on reference conditioning, prioritize Luma AI because reference images and motion configuration help keep reruns visually consistent.
Plan governance and admin controls around RBAC and audit log visibility
If governance needs role-based access controls, prioritize Runway because it supports role-based access controls for team governance. If governance needs workspace controls and identity features for repeatable, auditable production, prioritize Synthesia because it includes enterprise governance features.
Stress test throughput constraints using batch runs and queue planning
If high-demand batches are common, model queue behavior before committing to large parallel runs because Pika and HeyGen note uneven throughput during high-demand generation. If batch runs are central, also plan for throughput bottlenecks because Luma AI and Rawshot note constraints tied to input and external orchestration needs.
Which teams benefit from fashion-specific video generation control
Different tools target different control planes, from product-image to motion generation to scene-based programmable specs and template-driven avatar pipelines. The right choice matches the team’s asset structure and how approvals should work across campaigns.
Fashion brands and ecommerce teams producing frequent short product motion
Rawshot fits because it is purpose-built for converting fashion product imagery into short marketing-style video clips with brand-aligned style and creative direction. This works best when campaigns require fast iteration and the input images provide the detail that constrains output quality.
Brand teams that require API automation for fashion video variants with governance
D-ID fits because it uses scene-based generation requests that combine character and asset inputs into a programmable video spec. Runway also fits when automated, API-led video generation must fit controlled creative workflows with job-based orchestration.
Teams running template-driven campaigns with repeatable prompt patterns
Pika fits because it emphasizes prompt reuse with configurable generation settings for repeatable fashion motion across brand campaigns. InVideo AI fits when teams want template-based fashion clip generation that reuses brand assets and supports editing workflows for iterative revisions.
Production lines that need avatar or character pipelines with job automation
HeyGen fits because it supports template-driven avatar video generation with job automation designed for batch renders. Synthesia fits when scripted, brand-controlled outputs need API automation paired with enterprise governance features for reproducible deliverables.
Studios building reference-conditional motion from existing asset pipelines
Luma AI fits when teams need reference image conditioning paired with motion configuration for brand-consistent fashion video generation. Kaiber also fits when the team wants API-oriented workflow with parameterized job runs that take prompt and reference parameters for controlled batch throughput.
Common failure modes when evaluating fashion video generators
Many teams pick tools that look creative in a UI but fail when automation needs schema consistency and governance controls. Output quality can also become constrained by input image quality and how brand direction is represented in parameters.
Treating output variation as acceptable without a review loop
Creative outcomes can vary with input quality and prompt precision in D-ID, which makes governance require deliberate review loops to avoid brand drift. Schedule approvals and re-tuning cycles when using D-ID and InVideo AI, since audit log details may not cover every edit and asset mutation.
Choosing a tool without matching the production’s data model
If teams need schema governance for scene and asset mapping, Pixverse and Kaiber can be a better match because they emphasize configurable generation schemas and parameterized job inputs. If teams need scene-based control, picking Pika only for prompt reuse can miss scene-level specification needs that D-ID handles directly.
Assuming brand alignment controls will work without high-quality references
Rawshot ties output quality to the input images and provided creative direction, so weak product imagery and vague direction will limit results. Luma AI improves brand continuity with reference image conditioning, but throughput can bottleneck during high parallel batch generation, so reference curation matters alongside queue planning.
Underestimating governance gaps in RBAC and audit logging coverage
Runway has role-based access controls but may have limited fine-grained admin controls across all stages, which can be a gap for strict governance. HeyGen and InVideo AI can also have limited RBAC granularity or audit log details, so governance requirements should be mapped to what admins can actually control.
Ignoring throughput variability during high-demand generation runs
Pika notes throughput can be uneven during high-demand generation runs, and HeyGen depends on specific workflow objects for automation depth. For large batch schedules, validate queue behavior and job batching assumptions in Runway and Luma AI before scaling campaign volumes.
How We Selected and Ranked These Tools
We evaluated Rawshot, D-ID, Runway, Pika, Luma AI, HeyGen, Synthesia, InVideo AI, Kaiber, and Pixverse on features, ease of use, and value, with features weighted the most at forty percent to reflect how controllable fashion outputs must be. Ease of use and value each counted for thirty percent to reflect how quickly teams can operationalize generation within existing production workflows.
Each tool’s overall rating reflects criteria-based scoring across those categories using the provided strengths and constraints for each product. Rawshot separated most clearly because fashion-focused product-image to motion generation with brand-aligned style and creative direction earned a very high features score and a strong ease-of-use profile, which lifted it most in the features category.
Frequently Asked Questions About ai brand fashion video generator
Which AI brand fashion video generator tool is easiest to integrate through an API-first workflow?
How do these tools differ in controllability for brand campaigns that require consistent scenes?
What option supports repeatable batch generation for SKU or lookbook variations without manual re-editing?
Which generator is best when the source of truth is product imagery and brand visual references rather than scripts?
What tools support structured automation where assets, scenes, and parameters map into a governed data model?
Which platform is better for teams that need programmatic access control and auditable activity during production?
How do prompt-driven tools handle consistency across multiple scenes in a single fashion video?
What common integration pattern works best for linking generation output to review and approvals?
Which tool is most suitable when the workflow requires asset handoff after generation for downstream editing?
Why would a team choose Rawshot over generic text-to-video tools for fashion content production?
Conclusion
After evaluating 10 tools, Rawshot stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
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