H&M is moving to create AI-powered digital twins of its models to support product imagery and marketing campaigns, coordinating with an AI partner to develop lifelike replicas. The company plans to roll out digital likenesses for use in social media and advertising later this year, with watermarks intended to preserve transparency about AI-generated content. The initiative centers on 30 real-life models and seeks to minimize costly, time-consuming photoshoots while expanding creative possibilities. The approach marks a notable step in how fashion brands might blend human talent with advanced generative AI, aiming to preserve a human-centric aesthetic while leveraging scalable digital assets. As the fashion industry grapples with AI’s implications for work, creativity, and consumer trust, H&M’s plan prompts questions about rights, compensation, governance, and the future role of models in a world where digital replicas can co-create brand storytelling.
H&M’s Digital Twin Initiative: Scope, Partners, and Immediate Goals
H&M is partnering with an AI provider to create digital replicas of thirty of its real-life models. These digital twins are intended to be integrated into product campaigns and marketing content, enabling the company to produce imagery without organizing new shoots in every location or for every season. The concept rests on the ability to generate authentic-looking visuals that align closely with the models’ likenesses, enabling rapid iteration of campaigns across channels, including social media, e-commerce, and traditional advertising. As described in industry reporting, the plan envisions using AI-generated images in a way that complements the human models rather than replacing them outright.
A central feature of the approach is transparency. When AI-generated imagery is deployed, the images will be clearly watermarked to indicate their AI origin. This watermarking serves two purposes: it provides visibility into the nature of the content for audiences and enables the brand to analyze how viewers respond to campaigns that rely on digital twins rather than live models. By signaling the AI dimension up front, H&M seeks to foster informed consumer engagement while preserving brand authenticity. The watermarks are part of a broader governance framework intended to balance marketing innovation with ethical and practical considerations.
The collaboration involves working with models and their agencies to develop the digital twins. The intent is to capture sufficient detail to render the twins realistically, so they function as a flexible, scalable extension of the models’ professional portfolios. The plan is to deploy these digital replicas in social media and in real advertising campaigns later in the year, expanding the brand’s creative toolkit and potentially altering the production lifecycle for fashion imagery. The overarching objective appears to be reducing production costs and lead times while maintaining high standards for visual quality and brand alignment.
foreseeable timeline also includes the digital twins’ deployment across multiple campaigns, with ongoing evaluation of their effectiveness in achieving marketing goals, audience engagement metrics, and return on investment. While the practical benefits are clear—faster turnarounds, broader reach, and adaptable content—the initiative also raises questions about the boundaries between digital and physical representation, as well as how quickly viewers come to accept AI-generated imagery as part of mainstream branding. The strategy emphasizes that the digital twins will be employed as complements to real models, not as wholesale substitutes for human talent.
The project also involves negotiating and clarifying rights beyond H&M’s immediate use. Specifically, the models themselves would own the rights to their AI-generated clones, creating potential opportunities for the models to leverage those digital assets with other brands or projects. In this sense, the digital twins function not only as marketing tools for H&M but also as a potential asset that models can monetize independently, depending on future agreements with agencies and clients. This arrangement underscores an explicit shift in thinking about model rights in an era where digital likenesses can be owned, licensed, and monetized.
In sum, H&M’s digital twin initiative is a strategic maneuver designed to expand creative capabilities, streamline workflow, and maintain brand resonance in a rapidly evolving media landscape. It reflects a broader industry trend toward integrating synthetic media into marketing while addressing practical concerns about cost, speed, and audience response. The plan’s success will hinge on the quality of the digital representations, the effectiveness of watermarking for transparency, and the clarity of rights and compensation arrangements for the models involved.
The Role of Watermarking and Transparency in Brand Communication
Transparency around AI usage is a deliberate element of H&M’s approach. By watermarking AI-generated imagery, the brand signals that not all visuals are derived from real-world shoots, helping to manage audience expectations and promote trust. This practice aligns with a growing demand from consumers for clarity about how content is produced and whether real people are involved in brand campaigns. Watermarks may also facilitate data-driven analysis of how audiences respond to synthetic content, enabling marketers to refine creative strategies based on empirical feedback.
The watermarking approach goes beyond mere labeling; it is part of a broader measurement framework that could inform decisions about when and where AI-generated content is most effective. For example, marketers might compare engagement metrics between AI-driven visuals and traditional photographs to determine optimal uses, or they might study audience reactions to digital twins across different demographics and geographies. The transparency initiative thus serves both ethical and strategic purposes, helping to preserve credibility while enabling experimentation with new creative formats.
The decision to watermark also carries implications for brand safety and authenticity, particularly in markets where audiences are more wary of synthetic media. By clearly distinguishing AI-created images, H&M reduces the risk of misinterpretation or accusations of deception, while inviting scrutiny and feedback that can guide future iterations. The practice can become a differentiator if executed consistently and transparently, reinforcing a perception of responsibility and openness in the brand’s marketing portfolio.
Creating Photoreal Digital Twins: Data Capture, Modelling, and Rights Framework
The process of creating photoreal digital twins begins with extensive data capture of each model. The foundation involves taking large volumes of photos and capturing movement across various scenarios: different locations, lighting conditions, and camera angles. The goal is to document the smallest possible detail—the birthmarks, unique movement patterns, and other distinctive features—that contribute to a faithful digital reconstruction. This level of detail is intended to provide the AI system with enough information to generate accurate and realistic replicas that can perform convincingly in a range of contexts.
To achieve this fidelity, the capture process likely blends still photography with motion data collection. Advanced scanning techniques, high-resolution imaging, and controlled lighting environments can help preserve texture, skin tone, and micro-expressions that contribute to realism. The resulting data set becomes the input for the generative system that synthesizes the digital clone. Throughout this phase, there is a critical emphasis on consent, model rights, and agency involvement, ensuring that the models retain control over how their likenesses are used and licensed.
The intention is for the digital twins to be authentic extensions of the models rather than mere digital novelties. This requires an in-depth synthesis of appearance, gait, facial expressions, and wardrobe compatibility across campaigns. The process is designed to produce a stable and reusable digital asset with high fidelity to the living counterpart. It also involves establishing robust data governance practices to manage the storage, retrieval, and licensing of the digital twins across platforms and projects.
Once created, the AI-generated clones become assets with defined usage rights. The models themselves would own the rights to their digital clones, a framework that allows them to negotiate licensing terms and pursue opportunities beyond H&M if desired. This arrangement signals a shift in how talent rights are structured in a world where digital likenesses can be independently monetized. The models’ agencies would play a crucial role in negotiating deals, ensuring fair compensation, and overseeing the expansion of use to other brands or channels while protecting the models’ interests.
The end-to-end pipeline—from data capture to AI synthesis, licensing, and deployment—must balance quality control with efficiency. Quality control ensures that the synthetic visuals remain consistent with brand standards, while efficiency enables faster content production cycles and more agile marketing responses to market trends. The approach envisions a synergistic relationship between human performers and digital avatars, where the latter provide scalable support for the former’s ongoing careers and creative legacies.
Rights, Ownership, and Future Licensing
A key element of the model rights framework is the assertion that the human model retains ownership or control over the digital clone itself. This means the model could license the AI-generated likeness to other brands, agencies, or campaigns beyond H&M. The exact terms of licensing, compensation structures, and duration would be negotiated through standard industry practices, with agents typically overseeing usage rights and ensuring fair remuneration. In the H&M plan, compensation for the digital twin’s use would need to be defined clearly, because it represents a new revenue stream created by the digital asset, separate from traditional model fees.
H&M has publicly indicated that there is currently no additional detail to share on how participating models would be compensated for work completed by their digitally generated twins. This suggests that the company is still firming up the compensation framework and licensing mechanics as the pilot progresses. The absence of concrete details at this stage does not diminish the importance of establishing a transparent and equitable model for digital-twin compensation, but it does underscore the need for careful negotiations with models and agencies to avoid misunderstandings and to ensure sustainable collaboration.
In this environment, the rights framework also intersects with broader industry practices regarding the reuse of likenesses, cross-brand collaborations, and the potential emergence of standardized licensing channels for digital assets. As digital twins become more common, the market may develop formal guidelines or even regulatory considerations related to consent, ownership, and fair compensation. H&M’s approach—emphasizing model ownership of the clones and transparent deployment with watermarks—positions the company as a participant in shaping evolving norms around synthetic fashion media.
Generative AI in Fashion: Context, Examples, and Brand Implications
The use of virtual influencers and digital personas in fashion has been evolving for several years, with brands experimenting with synthetic characters to complement or extend human models. One notable example from the broader market involves a virtual creator with a distinctive appearance who rose to prominence on social media, accumulating a substantial following and collaborating with well-known brands. This digital figure demonstrates how virtual personalities can build significant brand equity, secure campaigns, and generate engagement that rivals or surpasses that of real models. While virtual models like this can attract large audiences and deliver marketing impact, they remain stand-alone personas not tied to any particular human guarantor in the same way as a real model’s identity.
The distinction between a digital twin and a virtual influencer is meaningful for both brands and audiences. A digital twin is anchored to a real person, designed to reproduce that person’s appearance and movement with high fidelity. In that sense, a twin acts as an extension of a living individual, capable of participating in campaigns under licensing and consent arrangements. A virtual influencer, by contrast, is a fully synthetic character with a crafted personality and backstory. While both concepts leverage AI to generate content, digital twins retain a direct connection to real individuals, which has implications for authenticity, consent, and trust.
The experience of the virtual influencer demonstrates both opportunities and tensions within the industry. On one hand, such personas can expand brand reach, create fresh storytelling angles, and reduce scheduling constraints associated with real shoots. On the other hand, the use of synthetic characters has raised concerns about authenticity, representation, and the broader impact on real-world employment for models and creatives. The use of digital twins by H&M appears to aim for a hybrid approach—retaining real models as central figures while deploying high-fidelity AI clones to augment and accelerate creative output. This approach may appeal to audiences seeking both human connection and novel, technologically advanced visual storytelling.
From a brand strategy perspective, the deployment of digital twins can enable more granular audience targeting and experimentation. Marketers could tailor imagery to specific geographies, cultures, or shopper segments by adjusting lighting, wardrobe, and contexts in a way that would be impractical with traditional shoots. The scalability of digital twins makes it possible to produce variations that would previously have required extensive logistical planning and budget allocations. For a company like H&M, famous for frequent seasonal campaigns and a broad product range, this capability could translate into faster go-to-market timelines, more efficient A/B testing, and diversified creative outputs that align with evolving consumer preferences.
Nevertheless, the adoption of AI-generated twins invites careful scrutiny of ethical considerations, including the potential for bias in generated imagery and the risk of misrepresentation if the digital clone is used in contexts that diverge from the model’s agreed-upon image and message. Brands must establish guardrails to ensure that digital twins are portrayed accurately and respectfully, avoiding scenarios that could mislead or alienate audiences. The watermarking strategy and the explicit acknowledgement of AI-generated content are steps toward building responsible usage, but ongoing governance, monitoring, and stakeholder engagement will be essential to navigate the evolving landscape of synthetic media in fashion.
Leadership Perspective: H&M’s Creative Vision and the Role of AI in the Brand
H&M’s leadership frames the digital twin initiative as an extension of the company’s technology strategy and its commitment to a human-centric approach to fashion. The Chief Creative Officer has described the project as an exploration of how generative AI can contribute to the creative process while remaining faithful to personal style and human expression. The overarching narrative emphasizes curiosity about new creative modalities and a willingness to harness technological advances to augment—rather than replace—the human touch that defines fashion.
This perspective situates AI as a tool that can amplify designers’ and marketers’ capabilities, enabling them to experiment with new forms of expression and storytelling without compromising the brand’s core values. The leadership emphasis on “liberating fashion for the many” and celebrating self-expression underscores a philosophy that sees technology as a democratizing force rather than a source of exclusion. In practical terms, AI-driven digital twins are positioned as a means to expand creative options, accelerate campaign development, and deliver more inclusive and varied representations within the brand’s marketing repertoire.
By articulating a clear boundary between AI-enabled experimentation and the company’s human-centric ethos, H&M seeks to reassure stakeholders—employees, models, agencies, and customers—that technology will augment, not erode, the brand’s commitment to personal style and inclusive fashion. This stance is particularly important as the industry debates the implications of AI on jobs and creative work. The leadership’s framing also suggests an intent to integrate AI responsibly within the brand’s broader digital ecosystem, aligning technology investments with brand storytelling, sustainability goals, and inclusive representation.
The strategic articulation also acknowledges potential challenges and pushback. Skeptics may worry that AI could erode the value of human talent or degrade the quality of creative work. In response, H&M emphasizes that digital twins are a complement to human models, designed to support and expand their careers rather than undermine them. This distinction is critical for maintaining trust among models and agencies, securing ongoing collaboration, and ensuring that AI usage aligns with professional standards and ethical practices. The company’s approach reflects a broader industry trend toward balancing innovation with accountability, transparency, and respect for human talent in fashion.
Economic, Legal, and Compensation Considerations: Navigating a New Revenue Model
A central question raised by H&M’s digital twin initiative concerns compensation and the broader licensing framework for digital clones. While traditional model work involves compensation tied to usage rights and negotiated agency fees, the digital twin paradigm introduces the idea of compensating the digital representation itself for usage rights. A spokesperson indicated that there is nothing further to share on compensation details at this stage, signaling that the company is still shaping how this new revenue stream will be structured, negotiated, and implemented.
Industry observers note that compensating a digital twin would represent a novel revenue stream, distinct from the compensation models used for physical appearances. If the digital clone is licensed for use across campaigns and possibly with other brands, the model would need a structured agreement that accounts for ongoing usage, duration, geography, and media channels. This could involve upfront fees, ongoing royalties, or milestone-based payments, all governed by licensing terms negotiated by the model and their agency. The fact that the digital clone could be used beyond H&M underscores the potential for models to monetize their digital likeness independently, while still collaborating with the brand that facilitated the initial creation.
The rights framework described by industry insiders emphasizes that the real-model’s ownership of the clone would empower them to license it to other brands. This arrangement would require clear contracts that specify licensing scopes, exclusivity, and compensation. Agencies are likely to play a central role in negotiating terms, ensuring fair value for the model’s digital representation, and coordinating cross-brand opportunities. The practical implications extend to data security and IP protection, as licensing digital likenesses across platforms and jurisdictions can raise questions about how to enforce rights and prevent misuse.
From a commercial perspective, H&M’s use of digital twins can offer cost efficiencies and scalability advantages. Once the digital clone is created, it can be deployed for multiple campaigns and across numerous product lines without the logistical costs of new photo shoots. This capability can reduce production time, lower travel and location expenses, and enable rapid iteration of creative concepts in response to market signals. At the same time, the company must manage the risk of over-reliance on synthetic imagery, balancing efficiency with the need for authentic storytelling that resonates with diverse audiences. The compensation and licensing framework will be a key determinant of whether digital twins generate a sustainable and mutually beneficial business model for both brands and models.
For the models themselves, the potential to leverage their digital clones in other campaigns could provide additional revenue streams beyond their work with H&M. However, this possibility hinges on favorable licensing terms and the ability of models to retain control over where and how their images are used. The agencies representing models will likely negotiate terms that protect their clients’ reputations and ensure that brand collaborations align with the models’ professional standards and personal values. The success of this approach will depend on careful governance, transparent communication, and fair financial arrangements that reflect the unique nature of digital likeness rights in a changing media landscape.
Industry Impact, Public Perception, and Ethical Considerations
The adoption of AI-driven digital twins in fashion is not without controversy. Critics have raised concerns about the potential displacement of creative jobs and the broader implications for human workers in the industry. A notable example from the past decade involved a major denim brand that faced backlash after exploring AI-generated solutions aimed at increasing diversity in online shopping; the plan faced significant public pushback and was ultimately shelved. This episode underscores the importance of balancing innovation with consideration of the human workforce and the ethical dimensions of automation in marketing and design.
Transparency, rightsization, and accountability are central to shaping public perception of AI in fashion. H&M’s decision to watermark AI-generated imagery is a step toward responsible communication, but it is only one element of a broader governance framework. Audiences are increasingly attuned to questions about authenticity, representation, and consent—particularly when real people’s likenesses are replicated and repurposed in synthetic formats. The fashion industry’s response to these concerns will influence consumer trust, loyalty, and the willingness of audiences to engage with AI-enhanced campaigns.
Issue areas likely to attract scrutiny include consent for data collection, the potential for bias in AI-generated imagery, and the risk that digital twins could be used in ways that misrepresent models or mislead consumers about a brand’s values or offerings. To mitigate such risks, brands should implement clear usage guidelines, establish oversight mechanisms, and maintain ongoing dialogue with models, agencies, and the public. The watermarking strategy contributes to transparency, but it must be supported by robust governance practices, including audits, impact assessments, and stakeholder feedback channels. Transparency alone is not a panacea; it must be part of a comprehensive approach to responsible AI in fashion.
The broader industry implications are also worth considering. As more brands experiment with digital twins and synthetic media, market dynamics could shift in terms of production efficiency, creative collaboration models, and the economics of talent representation. Some photographers, stylists, and other creative professionals may explore new roles that complement AI-driven workflows, while others may experience changes in demand for traditional production expertise. In this evolving landscape, the fashion industry will need to define standards for ethical AI use, protect the rights and dignity of human talent, and create pathways for workers to adapt to new technologies without losing their professional identities.
Operational Risks, Governance, and Quality Assurance
Deploying digital twins across multiple campaigns entails significant operational considerations. Ensuring high fidelity and consistency in generated imagery requires robust quality assurance processes, including continuous monitoring of output against brand guidelines, stylistic parameters, and product-specific requirements. The integration of AI-generated content with live photography and CGI elements must be carefully managed to preserve the brand’s visual language and avoid dissonant aesthetics.
Data governance is another critical area. The storage, access, and licensing of data used to create digital twins—such as model imagery, motion data, and other biometric-like information—must conform to privacy and intellectual property standards. While the models’ ownership of their digital clones helps address some consent questions, brands must implement secure data handling practices to prevent misuse or unauthorized duplication of assets. Clear data retention policies, restricted access controls, and encryption measures are essential components of a responsible AI-enabled production environment.
From a risk-management perspective, the potential for technology failures, misrepresentations, or breach of licensing terms requires proactive mitigation strategies. Brands should establish escalation procedures for addressing disputes around rights, usage limits, and compensation changes. In addition, routine audits and third-party reviews can help verify adherence to licensing agreements and ensure that digital twins are used within the permitted boundaries. A robust governance framework should also include mechanisms to handle revocation or modification of permissions if circumstances change—such as an artist’s career evolution, changes in brand strategy, or shifts in regulatory requirements.
The collaboration dynamics with agencies and models add another layer of complexity. Agencies will need clear contractual language covering compensation, rights to license, and the scope of use for digital twins. Models must feel adequately protected and fairly compensated, with visibility into how their likeness will appear across campaigns and channels. Maintaining a collaborative, transparent environment will be essential to sustaining trust and ensuring long-term success of the program.
Future Outlook: How AI Digital Twins Could Reshape Fashion Marketing
Looking ahead, H&M’s digital twin initiative could serve as a blueprint for broader adoption of synthetic media in fashion. If successful, the approach could unlock new efficiencies, enabling brands to accelerate campaign development, increase content variety, and respond more rapidly to market signals. The scalability of digital twins means that a single model could support multiple campaigns, product lines, and regional markets, thereby expanding the creative reach without proportional increases in production costs.
The potential cost savings come with caveats. While AI-generated imagery can reduce shoot-related expenses and logistical challenges, there are upfront investments in data capture, model agreements, licensing frameworks, and ongoing governance. The net effect on the marketing budget will depend on how effectively the digital twins are integrated into existing workflows, how well the copyright and licensing terms are managed, and how audiences respond to AI-driven creative. The ability to measure engagement, conversion, and sentiment around AI-generated visuals will be critical to determining the approach’s financial viability.
Another dimension of future impact involves the evolution of talent management and brand partnerships. If models retain ownership rights to their digital clones, new licensing ecosystems could emerge that enable cross-brand collaborations while preserving the dignity and control of the human talent involved. Models could strategically curate a portfolio of digital assets that complement their real-world work, expanding their professional horizons and revenue opportunities. This evolution could influence talent representation and the structure of contracts in the fashion industry, prompting agencies to develop specialized expertise in digital-rights management, licensing, and cross-brand negotiations.
From a consumer perspective, the integration of AI-generated twins into fashion marketing might alter expectations about what constitutes authentic representation. Brands will need to balance the desire for efficiency and creative experimentation with a commitment to truthful storytelling and respect for the people whose likenesses are used. As audiences become more discerning about synthetic media, transparency measures such as watermarking and clear disclosures will likely become standard practice across the industry. The long-term success of digital-twin programs will depend on building and sustaining trust with consumers, models, agencies, and other stakeholders.
Conclusion
H&M’s plan to develop AI-powered digital twins of its models marks a significant moment in the fashion industry, reflecting a broader shift toward synthetic media as a strategic tool for marketing and creative exploration. By partnering with an AI provider to create lifelike clones of thirty real-life models, the brand aims to enhance campaign production, reduce shoot-related costs, and enable rapid content generation. The use of watermarks signals a commitment to transparency, while the proposed rights framework positions models as owners of their digital likenesses, potentially unlocking new licensing opportunities beyond H&M. Leadership emphasizes that the technology is intended to augment the creative process and reinforce a human-centric approach to fashion, rather than replace real talent.
The initiative sits within a broader industry context where virtual influencers and digital personas already influence brand storytelling. The example of a prominent virtual model demonstrates the market’s appetite for synthetic media, while also illustrating the distinctions between digital twins anchored to real individuals and fully synthetic characters. H&M’s approach seeks to blend these paradigms—leveraging the scalability and speed of AI while preserving authenticity, consent, and ownership for models.
As with any ambitious AI-driven strategy, the path forward will require careful navigation of ethical considerations, compensation structures, and governance frameworks. The success of digital twins will hinge on how well the industry integrates new technologies with clear rights, fair compensation, robust data governance, and transparent communication with audiences. If executed with diligence and accountability, AI-powered digital twins could expand creative possibilities, improve efficiency, and offer models new opportunities to monetize their likenesses, all while maintaining the human-centered ethos that underpins fashion as a form of personal expression. The coming months will reveal how this balance plays out in practice and what it means for brands, models, and the broader fashion ecosystem as they navigate a rapidly evolving digital landscape.