36 Color Culture and Science Journal Vol. 17 (2) DOI: 10.23738/CCSJ.170204 The co-evolution of color and digital tools: a new logic for fashion design Maria Martone1, Tiantian Fan2 1 Formerly Department of Civil, Constructional and Environmental Engineering, Sapienza University of Rome, Italy. maria.martone@uniroma1.it 2 School of Media and Art Design, Wuhan Donghu College, Wuhan, China. ftt0532@gmail.com Corresponding author: Maria Martone (maria.martone@uniroma1.it) ABSTRACT This contribution offers a critical analysis of color as a visual code within the digital workflow of fashion design, through a comparative investigation of two iconic brands: Prada and Armani. The study focuses specifically on product color design in digital environments—namely, its representation and material rendering—rather than on dyeing or industrial production processes. The research unfolds across three levels—image, language, and material—and is based on a critical reading of visual and documentary data drawn from official fashion archives (1988–2024). Through a five-phase periodization, the study highlights paradigm shifts in color design, illustrating how digital tools (such as Pantone systems, generative AI, and 3D simulation) have progressively transformed chromatic composition, affecting both visual coherence and the cultural semantics of color palettes. Comparative analyses show that maximum chromatic fidelity is achieved through a conscious selection of material substrates (e.g., cotton vs. silk) and a formal design approach aligned with brand identity. Findings reveal a measurable gap between algorithmic simulation and multisensory experience, which calls for perceptual calibration phases and the integration of semantic and affective parameters into the digital design workflow. KEYWORDS Italian fashion, Pantone, digital tools, color design, chromatic language. RECEIVED 17/06/2025; REVISED 08/07/2025; ACCEPTED 26/09/2025 The co-evolution of color and digital tools: a new logic for fashion design 37 Color Culture and Science Journal Vol. 17 (2) DOI: 10.23738/CCSJ.170204 1. Introduction Since the second half of the twentieth century, fashion’s color language has undergone a radical shift—from symbolic-artisanal frameworks to industrial logic and, more recently, computational paradigms (Barthes 2013). In the digital realm, color is increasingly treated as a computational output generated by algorithmic pipelines (Lab/RGB mappings, rendering, text-to-image systems), whose appearance depends on material substrates, devices, and immersive contexts (Manovich, 2013). This article critically examines the evolution of fashion’s color language from the twentieth to the twenty-first century, with particular emphasis on the transition from analog approaches to digital workflows. Our aim is to clarify color’s role as a visual code in digital product design, defining its affective-semantic dimension and the discontinuities introduced by digital tools. 2. The evolution of chromatic design: from traditional techniques to digital tools Historically, color has served as a visual and cultural code: Renaissance ultramarine in Marian iconography signalled purity and wealth; Rococo pastels conveyed aristocratic lightness; with Picasso, color became psychological narration (Blue and Rose Periods). Long before the digital era, then, color already functioned as a semiotic, affective, and social language (Abelló, edited by, 2025). In the twentieth century, fashion accelerated a chromatic shift—from symbolic, craft-based gestures to industrial standardization—culminating today in a computational logic that shapes new paradigms of digital fashion design. The move from “making” to “simulating” marks an epistemic turn: color is no longer merely a tangible material but a “parametric configuration” within digital environments (Flusser, 2011). Pre-digital practices—hand dyeing, fabric painting, manual sampling—encoded cultural hierarchies and identities yet faced structural limits: low chromatic reproducibility, dependence on physical substrates (silk, cotton, wool), and high perceptual subjectivity (Kawamura, 2005). Digital platforms have recast the designer–color relationship, shifting from descriptive systems to generative ones. Since the 1990s, Adobe Photoshop has evolved from numeric CMYK input to visual RGB/HSV/LAB interfaces. From 2023, several tools have scaled in digital-fashion workflows: CLO 3D enables volumetric prototyping with physically based drape simulation and PBR rendering of color/material/light— effective for visual prototyping rather than chromatic design per se; Marvelous Designer is analogous, focused on garment morphology and fabric dynamics with basic color controls. Adobe Substance 3D (Designer / Sampler / Painter) centres on authoring PBR materials and procedurally generating realistic textures (UDIM, physical maps) to support rendering. For real-time/AR presentation, Unreal Engine and Unity are widely adopted (Adobe Inc., 2024). 2.1. Benefits and challenges of the digital transition Digital tools deliver clear advantages—high colorimetric repeatability, real-time testing across diverse material substrates, and rapid, scalable customization. Yet they also introduce new challenges: diminished sensory engagement with physical materiality, algorithmic mediation of perception, and a risk of global aesthetic homogenization. Technological evolution is never neutral; it embeds cultural assumptions and semantic models that redefine the meaning of color in fashion design. Today, color functions as a visual–material language that structures identity and narrative, targeting cross-media perceptual coherence between physical and digital touchpoints (screens, print, AR/VR). Figure 1 maps the main stages of this transition: from 2D numerical tools (e.g., 1990s Photoshop), through hybrid platforms such as Marvelous Designer and Adobe Substance, to immersive, high-fidelity systems like CLO 3D (2023) (CLO Virtual Fashion, 2023). Along this trajectory, color becomes an integrated visual–material code within project communication and brand storytelling. 3. Toward an emotional and symbolic dimension of color Despite the widespread adoption of digital tools for defining and managing color, its emotional and symbolic dimensions remain underexplored (Heller, 2025). Most software relies on standardized systems—Pantone, HEX, Lab—that rarely account for culturally and ritually embedded meanings in specific contexts. For example, red (Pantone 485 C) commonly signals passion or danger in Western settings (Pantone LLC, 2024), whereas in China it evokes celebration, good fortune, and marriage; conversely, white and gray—icons of purity or elegance in Europe—are ritual mourning colors in China (Pastoureau, 2005). Figure 2 illustrates these semantic divergences, showing that even digitally standardized color is subject to deep perceptual and cultural variation(Gage, 1993; Batchelor, 2000). In global digital design, algorithmic neutralization risks erasing symbolic strata and collective memory, leading to aesthetic homogenization (Calefato, 2004). In interactive systems, neglecting cultural connotations undermines the emotional and narrative resonance of visual experiences. The co-evolution of color and digital tools: a new logic for fashion design 38 Color Culture and Science Journal Vol. 17 (2) DOI: 10.23738/CCSJ.170204 Fig. 1. Evolution of digital tools for color selection in fashion (1990–2023). All UI previews are simulated illustrations generated for academic use. Fig. 2. Cross-cultural interpretation of color symbolism in China and the West.The images in the iconography column are generated by Fan T. using AI-based tools to ensure copyright-free visualizations. A significant gap thus persists between digital high-fashion design pipelines and material perception, raising a key question: can software be calibrated to encode semantic and affective dimensions? Addressing this challenge requires an interdisciplinary approach integrating visual semiotics, perceptual psychology, and interaction design. We argue that embedding cultural parameters into algorithmic color encoding is now essential to preserve identity, meaning, and expressive depth in contemporary fashion design. 4. Case study: Prada vs. Armani — a comparative evolution of chromatic language (1988–2024) The pairing of Prada and Armani is intentional: within the same cultural arena, they embody paradigmatically divergent approaches to color (Pagano, and Di Dio Roccazzella, 2024; Santoro, 2006). Prada treats color as a narrative and experimental device; Armani pursues tonal and material coherence anchored in structural elegance （Evans, 2003). The co-evolution of color and digital tools: a new logic for fashion design 39 Color Culture and Science Journal Vol. 17 (2) DOI: 10.23738/CCSJ.170204 To track this evolution, we identify five chronological phases for each brand (1988–2024), derived from a cross- analysis of Pantone codes, material palettes, runway archives (Prada and Armani), digital rendering workflows, and stylistic narratives (Di Corcia, 2019; Design Beauty, 2025; Farran Graves, 2023). Each phase maps to a distinct chromatic–technological paradigm, synthesized in Figure 3, which unifies three comparative tables into a single infographic for clarity and depth [1]. (1) Early phase (Prada: 1988–1993; Armani: 1980–1994) Prada: austere palettes—graphite black, lead grey, matte military green—project a post-industrial, anti-luxury, quasi- militarized aesthetic with sharp tailoring and a structured femininity. Armani: intense navy, sand beige, cool greys in combed wool and gabardine produce restrained, functional elegance with fluid silhouettes. Tooling: color design remains analog (paper Pantone books, offset proofing, tactile fabric trials); digital processes are largely absent. (2) Consolidation (Prada: 1994–2005; Armani: 1995– 2005) Prada: shifts to muted metallics—satin beige-pink, lilac, bronze—on organza and lustrous satin, articulating an “industrial delicacy.” Armani: taupe velvet and powder-toned crêpe, desaturated and tactile, foreground introspection. Both embrace minimalism: Prada concept-driven; Armani material-driven. Tooling: Photoshop gains ground for 2D chromatic visualization; physical verification on materials remains central. (3) Aesthetic expansion (Prada: 2006–2013; Armani: 2006–2012) Prada: techno violet, dusty blush, and vivid electric blue layered on vinyl and duchesse satin; an ironic, fluid visual language engaging with gender fluidity and citation. Armani: an ethereal femininity via pearlescent chiffon, satin, and pastels emphasizing lightness and vertical flow. Tooling: wider CAD adoption; Pantone-based color management is integrated into workflows—still largely visual, not immersive/material. (4) Systematization (Prada: 2014–2020; Armani: 2013– 2019) Prada: synthetic materials (PVC, technical nylon) with fluorescents—acid orange, highlighter green, shocking pink—signal digital culture’s imprint: synthetic sheen and chromatic layering. Armani: structured fabrics (velvet, knitwear) in saturated yet tempered tones, yielding a more theatrical, constructed presence. Tooling: advanced control via CLO3D and Illustrator; nonetheless, digital render lags real material perception. (5) Contemporary phase (Prada & Armani: 2021–2024) Prada: brilliant yellow, copper-beige, and ice blue articulate a cyber, de-structured idiom that merges femininity with synthetic geometries and hybrid materials. Armani: compact, contemplative schemes—electric blues and soft greens—realized in matte jersey and sand- washed organza, balancing classicism with digital hybridity (Breward, 2004). Tooling: chromatic strategy becomes decisively software- mediated—CLO3D, Adobe Substance, Pantone Connect (Adobe, 2024)—and is increasingly shaped by immersive technologies and algorithmic simulations. Tools such as CLO 3D, Adobe Substance, and Pantone Connect enable parametric color generation, material simulation, and cross-media color control. Our analysis shows that, despite distinct visual languages, both brands converge on a strategic, identity-driven, and technologically informed use of color as a semiotic and cultural vector. The Prada–Armani comparison also highlights the rising role of digital color encoding as a design and communication tool. Overall, the study delineates two archetypes of chromatic evolution in the digital era: Prada articulates a postmodern, digitally inflected linguistic approach in which color operates as a narrative code; Armani sustains a tactile, structural continuity in which color embodies material elegance. 5. Toward a new logic of fashion design From Munsell’s three-dimensional models and Albers’s interaction-of-color, through ICC color management, to generative AI (e.g., Midjourney, Pantone Connect AI), each stage has redefined the bond between color, medium, and meaning (Fig. 4). RGB and CMYK— originally devised for print and display—are now embedded in digital-fashion software to map color onto virtual textiles, 3D assets, and online interfaces, enabling controlled, cross-platform chromatic representation. The adoption of ICC profiles, wide-gamut spaces (Adobe RGB, Display P3), and AI-driven algorithms expands expressive range while spotlighting the gap between on-screen rendering and physical realization, thereby heightening color’s affective and symbolic charge(Albers, 2013). The co-evolution of color and digital tools: a new logic for fashion design 40 Color Culture and Science Journal Vol. 17 (2) DOI: 10.23738/CCSJ.170204 Fig. 3. Comparative evolution of color languages and material strategies in Prada and Armani (1980–2024). Visual references are based on runway documentation from the Prada Official Archive (https://www.prada.com/ww/en/pradasphere/fashion-shows.html) and Armani collections as indexed via Getty Images Editorial Platform (https://www.gettyimages.com/search/2/image?family=editorial&phrase=Armani%20runway). The co-evolution of color and digital tools: a new logic for fashion design 41 Color Culture and Science Journal Vol. 17 (2) DOI: 10.23738/CCSJ.170204 Fig. 4. Evolution of Color Theories and Technologies in Design. Images generated via AI to simulate original references. Fig. 5. Conceptual model of AI-based color allocation in digital fashion workflows: from initial input sampling to semantic generation and perceptual calibration. The co-evolution of color and digital tools: a new logic for fashion design 42 Color Culture and Science Journal Vol. 17 (2) DOI: 10.23738/CCSJ.170204 In visual communication, color functions as an adaptive, programmable, cross-media language that preserves semantic and perceptual coherence across channels— from physical packaging to websites and social media. Universal codification (sRGB for the web, Pantone for print) and generative tools (e.g., Google Material Design’s responsive palettes; Adobe Sensei’s color optimization) help ensure consistency across touchpoints. The same hue can be reproduced on a product’s packaging, its online 3D visualization, and its social assets, delivering an integrated perceptual experience. Within fashion, color operates as strategic grammar: building brand identity (e.g., Bottega Veneta green, Valentino red), carrying symbolic narratives (seasonal, emotion-driven palettes), and shaping behavior (e- commerce interfaces often leverage blue to foster trust) (Joung, 2023). No longer a mere surface, color is a semantic-technical device embedded in design workflows, orchestrating coherence between physical collections and their digital storytelling (Rocamora, 2009). 6. Future directions 6.1. Emerging innovations In recent years, color design in fashion has consolidated into a divisibile four-stage pipeline: (1) input capture, (2) generative color modeling, (3) perceptual simulation, and (4) feedback-driven calibration. Inputs include reference images, Pantone palettes, lighting conditions, and material properties (e.g., cotton, silk). Generative modeling uses AI systems—such as CLIP-based embeddings with Lab/RGB mappings—to allocate hues under semantic and technical constraints. AR/3D simulation tests colors on virtual garments and immersive sets, tracking ΔL, ΔC, and ΔH (lightness, chroma, and hue differences per CIEDE2000) (Itten, 1973) and rendering shifts. Feedback and calibration loop：feeds designer/user responses back into the model to reduce color error and improve cross-media coherence. Fig. 5 diagrams this flow (Input → Generative model → Simulation → Calibration). Workflows based on visual presets or chromatic templates may bypass semantic generation, moving directly from input to simulation. A representative case is the ZERO10 × Crosby Studios “interdimensional” pop-up in SoHo (New York), where responsive installations stage an immersive, multisensory chromatic experience across vision, sound, and bodily movement (Fig. 6). AI-enabled color design also advances sustainability: predictive tools (e.g., Firefly, CLO3D) support virtual palette testing across fabrics and lighting scenarios, reducing physical sampling—and thus material waste (cotton, silk) and pre-production energy use. 6.2. Cultural and technical challenges Despite the promise of emerging color technologies, structural and cultural hurdles persist. The foremost is cross-device fidelity: color appearance varies substantially Fig. 6. AI-generated representation of chromatic design in immersive retail and digital fashion environments. Inspired by the ZERO10 x Crosby Studios project. Images created using AI to avoid copyright issues across displays, AR/VR environments, and print media, creating perceptual misalignments between designers and end users. Beyond these technical incongruities, digital toolchains often overlook the cultural, symbolic, and ritual dimensions of chromatic codes—for example, red connotes prosperity in China but frequently signals danger in many Western contexts (Luzzatto, and Pompas, 2017). To highlight the gap between digital encoding and material perception, we examined three tones recurrent in East Asian and Western collections (Chinese red, midnight blue, pearl gray), comparing their appearance on cotton and silk via synthetic visualizations under controlled conditions (standard illuminant D65, 2° standard observer, sRGB color space). The reported chromatic-difference values (ΔE00), where provided, are indicative estimates derived from uniform render patches and do not replace spectrophotometric measurements. Fig. 7 qualitatively visualizes these perceptual differences for cotton and silk substrates. These findings confirm that chromatic consistency cannot rely on digital codes alone; it requires control of material response and attention to cultural context. We therefore advocate adaptive color systems that integrate objective data (Lab, RGB, Pantone) with subjective parameters (emotional resonance, collective memory, tactile perception) (cf. Pantone, 2024). The co-evolution of color and digital tools: a new logic for fashion design 43 Color Culture and Science Journal Vol. 17 (2) DOI: 10.23738/CCSJ.170204 Fig. 7. Qualitative comparison of color rendering on different materials (cotton vs. silk) using identical Pantone codes. Visualizations were generated by Fan T. under controlled conditions (standard illuminant D65, 2° observer, sRGB profile). The ΔE00 values shown are approximate perceptual estimates based on rendering patches, not derived from spectrophotometric instruments. Differences reflect material response (e.g., gloss, reflectance) under simulated conditions. 7. Conclusions This study highlights that the co-evolution of digital tools and chromatic language, while enabling new paradigms of design in digital fashion—interactive, iterative, and customizable—has also increased the risk of aesthetic homogenization and a consequent erosion of direct sensory experience. It is therefore hoped that future digital technologies in fashion will also acknowledge semantic and affective parameters, allowing color—within its cultural and perceptual dimensions—to guide the evolution of fashion design toward a new balance between human sensitivity and digital precision, between artistic intuition and parametric logic, between ritual memory and technological innovation. 8. Conflict of interest declaration The authors declare no conflict of interest including financial, personal or other relationship with other people and organizations within three years of beginning the submitted work that could inappropriately influence, or be perceived to influence, this work. 9. Funding source declaration This research did not receive any specific grant from funding agencies in the public, commercial, or-not-for- profit sectors. 10. Acknowledgment Based on a collaboration of the authors, the scientific coordination is by Maria Martone, and the paragraphs are by Tiantian Fan. The Figures are edited by Tiantian Fan. 11. Short biography of the authors Maria Martone - Architect, Associate Professor in Drawing, has taught at the Sapienza University of Rome. She carries out her scientific research on the themes of representation and critical documentation of architecture, the city and the territory with the application of digital techniques related to surveying, photomodelling and geographic information systems. The results of her research are published in numerous scientific journals, conference proceedings and monographs. Tiantian Fan - Ph.D. in Architecture; Lecturer, Wuhan Donghu College (Wuhan, China). Her research focuses on color design and urban/fashion applications, combining digital simulation, color management, and multisensory communication in cultural heritage and contemporary design. Notes [1] In Figure 3 for editorial reasons it was not possible to publish the figures representing the models of the two brands: Prada and Armani. Therefore, for each model mentioned we refer to a web page where the The co-evolution of color and digital tools: a new logic for fashion design 44 Color Culture and Science Journal Vol. 17 (2) DOI: 10.23738/CCSJ.170204 corresponding image is visible. The date of last access, for this as for the indicated sites is 10/06/2025. Licensing terms Articles published in the “Cultura e Scienza del Colore -Color Culture and Science" journal are open access articles, distributed under the terms and conditions of the Creative Commons Attribution License (CC BY). You are free to share (copy and redistribute the material in any medium or format) and adapt (remix, transform, and build upon the material for any purpose, even commercially, under the following terms: you must give appropriate credit to authors, provide a link to the license, and indicate if changes were made. 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