{
  "sims": {
    "unmet_v11_label_background": [
      "Both datasets predominantly feature a single, clearly defined subject (e.g., a tool, chair, bag, or food item) placed centrally in the frame.",
      "Images in both sets often use diffused indoor lighting or directional spot lighting to highlight surface textures and materials.",
      "Backgrounds tend to be neutral or minimally distracting (plain walls, studio backdrops, simple floors or tables).",
      "Subjects are commonly arranged on flat surfaces\u2014tables, stages, or the ground\u2014creating a stable, horizontal composition line.",
      "A shallow depth of field is frequently used to softly blur the background and draw attention to the foreground object.",
      "The framing is generally symmetrical and centered, giving each subject a strong visual presence.",
      "Viewpoints are mostly eye-level or slightly overhead, providing a straightforward and unobstructed perspective of the object.",
      "Both sets emphasize fine details\u2014scratches on metal, wood grain, fabric folds\u2014underscoring the material properties of the objects.",
      "Color palettes are controlled and often limited, using complementary or analogous hues to keep focus on the main item.",
      "There is consistent use of close-up to medium shots that fill the frame with the subject and exclude extraneous elements."
    ],
    "unmet_v11_label_only": [
      "Both datasets include close-up compositions of hand tools (hammers, wrenches, drills) artfully arranged on flat surfaces.",
      "Both contain multiple images of backpacks or bags shown against neutral or textured backgrounds.",
      "Both feature flat-lay or slightly angled shots of food on plates, trays, or boards.",
      "Both show ornate chairs or thrones in museum-style settings, often with velvet ropes or display barriers.",
      "Both include live performance or concert stage scenes with musicians, lighting rigs, and audiences.",
      "Both feature richly decorated architectural interiors (palaces, galleries, churches) with elaborate ceilings and furnishings.",
      "Both employ controlled lighting that produces strong highlights and shadows to emphasize texture.",
      "Both tend to center a single subject (tool, bag, dish, chair) with ample negative space around it.",
      "Both use macro/detail shots to capture the surface qualities of metal, wood, and fabric.",
      "Both often present objects against plain or minimally distracting backgrounds (wood grain, stone floor, solid walls)."
    ],
    "unmet_v11_label_relation": [
      "Both datasets contain multiple semantic categories (tools, backpacks, stage performances, and ornate chairs/thrones) presented in a consistent manner",
      "Objects in both sets are often centered in the frame against varied backgrounds (workbench, museum gallery, outdoor setting, or plain backdrop)",
      "Each image shows a clear, single subject (e.g., a hammer, a throne, a singer, or a backpack) with minimal occlusion",
      "Lighting varies naturally across images in both datasets\u2014ranging from soft daylight outdoors to harsh spot-lighting on stage or museum spotlights",
      "Backgrounds in both collections include textured or patterned environments (wood grain, stone walls, stage curtains, tiled floors) that still allow the subject to stand out",
      "Both datasets mix indoor and outdoor scenes, providing context for the objects or people (gardens, studio rooms, concert halls, workshops)",
      "Composition in both sets is generally straightforward and documentary-style\u2014with the subject clearly separated from the background and often photographed from a side or three-quarter angle",
      "Human interaction appears in both: hands holding or pointing at tools, people wearing or carrying backpacks, and musicians engaging with instruments on stage",
      "Both sets feature still-life arrangements (tools laid out on a bench or a tool kit, trays of food or decorative platters) shot from above or at shallow angles",
      "There is an emphasis on manufactured or crafted items in both datasets\u2014metal tools, carved wood objects, sewn bags, and ornate furniture\u2014highlighting texture and materiality"
    ],
    "unmet_v15_label_only": [
      "Both datasets include close-up shots of hand tools (hammers, mallets, screwdrivers) often laid out on wooden or neutral backgrounds with shallow depth of field.",
      "Both feature backpacks as a prominent subject\u2014some images show bags alone against a plain or textured wall, others show people wearing them in outdoor or street settings.",
      "Each contains multiple photographs of ornate chairs or thrones set in grand architectural or museum-like interiors.",
      "Both have images of stage-based performances with lighting rigs, musicians or actors, and audiences, capturing dramatic stage illumination.",
      "There are decorative service items (trays, platters, bowls) photographed on tabletops, often from an overhead or angled perspective.",
      "Both utilize a mix of natural and artificial lighting\u2014ambient indoor light in palaces or workshops and colored stage lights in concert scenes.",
      "The main subject in many images is centered and fills most of the frame, relying on minimal clutter to emphasize form and texture.",
      "Backgrounds frequently fall into two categories: workshop or industrial environs and ornate interior spaces (palaces, galleries).",
      "Subjects include both people interacting with objects (craftsmen working with tools, performers on stage) and inanimate still-life compositions.",
      "Overall photographic style blends documentary snapshot realism with carefully arranged still-life or product-style presentations."
    ],
    "unmet_v15_label_background": [
      "Both datasets feature a clear central subject\u2014whether a tool, piece of furniture, or person\u2014framed prominently in the middle of the image.",
      "Images use shallow depth of field or soft focus on backgrounds to direct attention to the main object or figure.",
      "Most photos are shot in indoor settings with natural or ambient light, producing warm tonal quality and gentle shadows.",
      "Backgrounds tend to be simple and unobtrusive\u2014workshop benches, plain walls, stage curtains\u2014so as not to compete with the subject.",
      "Many shots employ a slight overhead or eye-level viewpoint, showing objects laid out on tables or stages in a flat-lay or level perspective.",
      "Tools, trays, chairs, and other props are often arranged systematically, creating a sense of order and balanced composition.",
      "There is frequent inclusion of contextual elements\u2014workspaces or performance stages\u2014that hint at the subject\u2019s purpose without cluttering the frame.",
      "Subjects are evenly lit from the front or side, highlighting surface textures, material details, and subtle color variations.",
      "Negative space is deliberately left around the main subject, guiding the viewer\u2019s eye and creating a harmonious visual balance.",
      "Human figures, when present, are captured interacting with or positioned near objects in a way that mirrors the still-life arrangements of tools and furniture."
    ],
    "unmet_v15_label_relation": [
      "Both datasets contain everyday object still-life photos featuring tools such as hammers and nails laid out on workshop-style surfaces",
      "Both include various bags and backpacks shot front-on or casually placed in real-world contexts",
      "Food and serving vessels (trays, bowls, platters) are presented centrally on a flat surface in both sets",
      "Subjects are often isolated against plain or minimally textured backgrounds (wood, fabric, tile) to emphasize the item",
      "Most images use natural or ambient lighting, producing soft shadows and visible reflections on the objects",
      "Close-up and shallow depth-of-field compositions are common, highlighting materials, textures, and details",
      "Many shots center the primary subject symmetrically, filling the frame with minimal negative space",
      "Workshop or studio environments recur, showing tools hung on walls, workbenches, or scattered on tables",
      "Both contain interior architectural scenes\u2014ornate chairs or thrones and stage setups under theatrical lighting",
      "A documentary-style approach ties them together, capturing objects and scenes in unposed, in-situ arrangements"
    ]
  },
  "diffs_synth_from_real": {
    "unmet_v11_label_background": [
      "Dataset B scenes are set in diverse, often uncontrolled environments (outdoor docks, construction sites, busy shops), while dataset A uses highly controlled, neutral studio\u2010style or minimal indoor backdrops.",
      "Images in dataset B frequently contain multiple objects, tools in use, or people interacting, whereas dataset A focuses on a single isolated subject with minimal contextual clutter.",
      "Lighting in dataset B varies from harsh natural light to mixed artificial sources\u2014creating strong shadows and highlights\u2014whereas dataset A relies on soft, diffused illumination that evenly highlights surfaces.",
      "Dataset B often uses deeper depth of field, keeping background details in focus, while dataset A employs shallow depth of field to gently blur backgrounds and isolate the subject.",
      "Compositions in dataset B lean toward dynamic, off\u2010center framing and candid angles, contrasting with the perfectly centered, symmetrical layouts of dataset A.",
      "Viewpoints in dataset B run the gamut\u2014low angles, side views, wide conversational shots\u2014whereas dataset A sticks mostly to straightforward eye\u2010level or slight overhead perspectives.",
      "Backgrounds in dataset B are busy and contextual (workbenches, crowds, architectural detail), while dataset A backgrounds are plain or softly textured to avoid distraction.",
      "Color in dataset B is more saturated or varied depending on scene (bright construction machinery, greenery, neon), whereas dataset A uses a controlled palette with complementary or analogous tones.",
      "Dataset B includes narrative or action elements (people performing tasks, events in progress), whereas dataset A presents static \u2018product\u2010like\u2019 or still\u2010life object portraits.",
      "Shots in dataset B range from wide environmental and mid\u2010range documentary styles to artistic/AI\u2010styled visuals, unlike dataset A\u2019s consistent close\u2010to\u2010medium studio imaging."
    ],
    "unmet_v11_label_only": [
      "Dataset B predominantly shows isolated objects (tools, bags, dishes, chairs) shot on simple, lightly textured surfaces (wood floors, stone slabs) with minimal contextual environment, whereas dataset A places subjects within rich, varied settings such as concert stages, museum galleries, outdoor streets, and palace interiors.",
      "Dataset B uses soft, diffuse, studio\u2010like lighting that produces gentle highlights and minimal harsh shadows, while dataset A exhibits a wide range of lighting styles\u2014from dramatic stage spotlights and natural daylight to ambient museum illumination\u2014often resulting in stronger contrast and more dramatic shadows.",
      "Dataset B compositions are tightly cropped and centrally framed, focusing narrowly on a single subject with ample negative space immediately around it, whereas dataset A frequently uses wider-angle or full-scene shots that include surrounding elements, architecture, signage, or people.",
      "Dataset B maintains a subdued, pastel\u2010tinged color palette with smooth, even textures and occasional slight blurring or smoothing artifacts; dataset A displays vibrant, natural color saturation, sharp detail, and authentic surface textures of metal, wood, and fabric.",
      "Dataset B backdrops are largely uniform across images\u2014often plain wood, neutral walls, or monotone floors\u2014whereas dataset A employs highly varied backgrounds such as ornate ceilings, tiled walls, cityscapes, audience crowds, and stage scaffolding.",
      "Dataset B contains almost no visible text, logos, or signage inside the frame, while dataset A often includes recognizable branding, banners, signs (e.g., 'Jolly Roger', 'JanSport'), watermarks, and event posters as part of the scene.",
      "Dataset B seldom shows full human figures (at most hands or partial limbs) and emphasizes objects alone, whereas dataset A frequently depicts people interacting with the scene\u2014musicians performing, visitors in galleries, cyclists on streets, or subjects posing with backpacks.",
      "Dataset B often employs a shallow depth of field that softly blurs backgrounds to isolate the subject, while dataset A generally captures scenes in deep focus, keeping foreground and background elements in sharp detail.",
      "Dataset B scenes are minimalistic with few compositional elements and a consistent \u2018still\u2010life\u2019 vibe, whereas dataset A images are more complex and dynamic\u2014multiple objects, architectural details, or human activities often occur simultaneously.",
      "Dataset B exhibits subtle style inconsistencies and odd geometric or reflective artifacts hinting at synthetic or generative origin, whereas dataset A displays the unpredictability and authentic imperfections of real\u2010world photography."
    ],
    "unmet_v11_label_relation": [
      "Dataset B images have a hyper-real, generative look with unnaturally smooth or repetitive textures, whereas Dataset A shows genuine camera artifacts (noise, grain, lens blur) and real-world surface variability",
      "Objects in Dataset B are often rendered in perfect focus and evenly lit with diffuse, studio-style lighting, whereas Dataset A contains mixed lighting conditions (harsh spotlights, sunlit exteriors, tungsten indoor light) and frequently employs shallow depth-of-field",
      "Backgrounds in Dataset B are typically simplified, stylized, or painterly\u2014sometimes blending into the subject\u2014whereas Dataset A backgrounds are complex, cluttered real scenes (workbenches, concert crowds, museum interiors)",
      "Dataset B compositions are centrally framed with symmetrical, almost product-shot layouts, whereas Dataset A uses a wider variety of angles (three-quarter views, side profiles, slight tilts) and more spontaneous, documentary framing",
      "Many items in Dataset B exhibit subtle geometric distortions or odd shape blends (hallmarks of AI synthesis), whereas the objects and architecture in Dataset A maintain physically plausible proportions and perspectival consistency",
      "Color palettes in Dataset B lean toward oversaturated or pastel schemes that feel artificial, whereas Dataset A features more faithful color reproduction in line with real-world lighting and materials",
      "Scene diversity in Dataset B includes improbable combinations (fantastical interiors, whimsical tool designs, unreal furniture hybrids), whereas Dataset A confines itself to plausible, everyday scenarios captured by human photographers",
      "Dataset B interiors and exteriors often lack real atmospheric cues (dust, weathering, motion blur), whereas Dataset A scenes contain authentic environmental details (scratches, dirt, reflections, timestamp overlays)",
      "Materials in Dataset B (woodgrain, metal, fabric) appear too uniform or perfectly patterned, whereas Dataset A materials bear natural wear, variation, and imperfections from real manufacturing and use",
      "Overall, Dataset B feels like stylized, CGI or AI-generated imagery with consistent artificial aesthetics, whereas Dataset A is clearly assembled from real photographs with varied camera equipment, photographers, and contexts"
    ],
    "unmet_v15_label_only": [
      "Dataset A images are real\u2010world consumer photographs with varied camera artifacts (noise, chromatic aberration, timestamps, watermarks), while dataset B images look uniformly clean and studio\u2010quality or AI\u2010rendered without such flaws.",
      "Dataset A uses a mix of portrait and landscape orientations with diverse aspect ratios; dataset B predominantly uses square framing with centered subjects.",
      "Dataset A backgrounds are authentic environments (museum halls, streets, concert venues) full of contextual clutter; dataset B backgrounds are simplified textures (aged wood, stone walls) or stylized natural scenes with minimal distractions.",
      "Dataset A lighting varies from harsh flash to low\u2010light ambient exposures, often with visible hotspots or lens flares; dataset B applies even, soft, or deliberately stylized illumination without harsh shadows or flare artifacts.",
      "Dataset A features candid human subjects in unpredictable poses and motion blur; dataset B shows few people, and when they appear they are posed or rendered in consistent, controlled styles (e.g., backpackers in wilderness).",
      "Dataset A shows branded items, visible labels, and real wear and tear (scuffs, scratches); dataset B shows pristine, unbranded products or objects arranged deliberately for aesthetic symmetry.",
      "Dataset A compositions vary widely in angle and perspective (tilted, oblique, overhead); dataset B compositions adhere to straight\u2010on or top\u2010down orthogonal views emphasizing symmetry.",
      "Dataset A often captures dynamic live scenes (concert crowds, performers mid\u2010act) with environmental context; dataset B includes stage or throne imagery but rendered more as static, ornamental still\u2010lifes.",
      "Dataset A tools and trays are shot in real workshops or kitchens on cluttered surfaces; dataset B tools are laid out on rustic wood or pebble ground in carefully arranged, harmonious layouts.",
      "Dataset A color palettes are organic and unpredictable (mixed indoor/outdoor lighting); dataset B maintains consistent color grading\u2014rich, saturated tones or neutral earth tones with minimal color cast."
    ],
    "unmet_v15_label_background": [
      "Dataset A images are authentic photographs with consistent, natural lighting and color, whereas dataset B images exhibit artificial or hyperreal lighting, inconsistent color casts, and painterly or surreal hues.",
      "Dataset A backgrounds are simple, uncluttered, and contextually plausible (plain walls, workshop benches, stage curtains), while dataset B backgrounds are busy, cluttered, or fantastical\u2014often merging industrial, natural, and imaginary elements.",
      "Dataset A uses straightforward compositions with a single central subject framed prominently, but dataset B presents complex, multi\u2010subject scenes with layered elements, odd object groupings, and surreal geometry.",
      "In dataset A the camera angles are mostly eye\u2010level or slight overhead with shallow depth of field to isolate the subject, whereas dataset B wildly varies in perspective, depth cues, and focus\u2014sometimes defying realistic camera optics.",
      "Subjects in dataset A adhere to real\u2010world scale and proportions, but dataset B frequently shows warped, disproportionately sized, or fused objects and figures that betray physical plausibility.",
      "Dataset A maintains a cohesive photographic style across images, while dataset B blends a spectrum of genres\u2014industrial workshops, beaches, forests, fantasy castles\u2014in a single collection.",
      "Color reproduction in dataset A remains true to life, but dataset B often uses unnatural or pastel palettes, strong atmospheric effects, and mixed light sources uncommon in standard photography.",
      "Scenes in dataset A are geographically and functionally plausible (concert stages, palaces, tool layouts), whereas dataset B includes improbable or imaginary settings like frozen thrones, melting palaces, and coral cathedrals.",
      "Dataset B images often show visual artifacts\u2014texture bleeding, distorted reflections, geometry glitches, warped edges\u2014unlike the crisp, artifact-free captures seen in dataset A.",
      "Dataset A subjects are captured in real-world contexts with minimal post-processing, whereas dataset B appears heavily edited or AI-generated, blending styles and inconsistently rendering objects and textures."
    ],
    "unmet_v15_label_relation": [
      "Dataset B images often look synthetic or CGI-like with hyper-realistic textures and flawless surfaces, whereas dataset A images are genuine amateur photographs showing natural imperfections, noise, and camera artifacts.",
      "Dataset B favors uniform, studio-style or minimalist backgrounds (flat walls, controlled curtains) to isolate subjects, while dataset A presents objects within varied, cluttered real-world environments and casual settings.",
      "Dataset B compositions frequently use centered, symmetrical layouts or flat-lay overhead shots in an editorial manner, whereas dataset A employs candid, casual snapshot compositions with objects in active use or everyday contexts.",
      "Dataset B lighting is consistently even, high-dynamic-range, and color-graded to produce soft shadows and saturated hues, in contrast to dataset A\u2019s ambient or stage lighting that yields stronger contrasts, mixed color temperatures, and visible grain.",
      "Dataset B includes many fantastical or highly ornate architectural and throne scenes rendered in stylized palettes, while dataset A shows real museum interiors, historic chairs, and thrones under practical, natural lighting.",
      "Dataset B objects appear pristine, polished, and sometimes \u2018floating\u2019 against backgrounds, whereas dataset A\u2019s objects bear wear, dirt, and are placed on real workbenches, tables, or floors with visible texture.",
      "Dataset B rarely includes people (or shows them as passive, stylized figures), focusing almost exclusively on objects and environments, while dataset A often depicts people interacting with the scene or standing within it.",
      "Dataset B images share a uniform post-processing style\u2014consistent color grading, vignettes, or painterly effects\u2014whereas dataset A is a diverse collection of contributor snapshots with widely varying exposures, white balances, and resolutions.",
      "Dataset B\u2019s stage and theatrical shots are empty stages, red curtains, or abstract performance spaces without performers, whereas dataset A captures live music performances and audiences in active concert settings.",
      "Dataset B incorporates modern or fantasy-inspired props, stylized thrones, and hyper-designed bags in controlled compositions, while dataset A features authentic artifacts, consumer-grade tools, and backpacks in genuine, everyday scenarios."
    ]
  },
  "diffs_real_from_synth": {
    "unmet_v11_label_background": [
      "Dataset B images are candid, real-world photographs capturing people, crowds, and live scenes, while Dataset A images are curated still-life setups with isolated objects or subjects.",
      "Dataset B uses ambient, mixed lighting (stage lights, sunlight, neon) resulting in dramatic highlights and color casts, whereas Dataset A employs soft, even illumination typical of controlled studio or tabletop shoots.",
      "Dataset B backgrounds are varied and often busy\u2014with buildings, audience, event signage, or natural vistas\u2014while Dataset A backgrounds are neutral, plain, or minimally textured to avoid distraction.",
      "Dataset B compositions are dynamic and asymmetrical, featuring multiple subjects or off-center framing; Dataset A compositions are generally symmetrical and centered around a single item.",
      "Dataset B frequently shows deep depth of field so that foreground and background details remain in focus, while Dataset A utilizes a shallow depth of field to softly blur the background and isolate the main subject.",
      "Dataset B often depicts people in motion (performers on stage, cyclists, crowds) producing motion blur or dynamic poses, whereas Dataset A almost exclusively portrays static inanimate objects in crisp, sharp focus.",
      "Dataset B images are shot across diverse environments\u2014including concert stages, outdoor festivals, city streets\u2014while Dataset A maintains a consistent indoor, tabletop, or studio environment throughout.",
      "Dataset B photos often contain incidental branding, text, or signage from real venues and products, whereas Dataset A deliberately avoids logos or extraneous visual clutter.",
      "Dataset B color palettes vary widely with saturated or unpredictable hues dictated by real-world lighting, while Dataset A maintains controlled, complementary color schemes to keep focus on the subject.",
      "Dataset B framing often includes contextual elements beyond the main subject (crowd, stage rigging, architecture), whereas Dataset A tightly crops around the object to exclude extraneous scene information."
    ],
    "unmet_v11_label_only": [
      "Dataset A images are predominantly studio-style compositions with isolated objects shot against plain or minimally textured surfaces, whereas dataset B images are real-world snapshots featuring cluttered environments with multiple objects and people.",
      "Dataset A consistently uses even, diffuse lighting and neutral color grading to minimize shadows and distractions, whereas dataset B exhibits varied lighting\u2014from harsh stage lights and colored concert strobes to mixed indoor/outdoor ambient light\u2014resulting in dramatic highlights, lens flares, and high-ISO noise.",
      "Dataset A frames subjects in tight, controlled close-ups or top-down flat-lays with shallow depth of field that blurs backgrounds, whereas dataset B includes a mix of wide-angle environmental shots, eye-level viewpoints, and complex depth that keeps scenes in focus.",
      "Dataset A generally omits human figures or shows only partial hands for scale, whereas dataset B frequently includes full people, crowds at concerts, performers on stage, or bystanders in street scenes.",
      "Dataset A maintains a coherent, high-resolution, watermark-free aesthetic, whereas dataset B displays visible watermarks, date stamps, and other artifacts (logos, signage) typical of user-generated Flickr and concert photos.",
      "Dataset A scenes are static and deliberately arranged (tools artfully laid out, food neatly plated), whereas dataset B captures dynamic, on-the-fly moments (musicians playing, audiences reacting, passersby at intersections).",
      "Dataset A backgrounds are limited to purpose-chosen textures\u2014wood grain, clean tables, studio walls\u2014whereas dataset B backgrounds span museum galleries, concert stages, outdoor festivals, city streets, and exhibition tents.",
      "Dataset A employs consistent, balanced compositions with ample negative space to spotlight a single object, whereas dataset B often crowds the frame with stage equipment, architectural details, or groups of people, leading to busy, unpredictable layouts.",
      "Dataset A focuses on everyday objects in isolation (hammers, backpacks, trays of food) photographed under controlled conditions, whereas dataset B includes thematic variety with ornate thrones, live performances, and interior architecture rarely seen in A\u2019s controlled catalog.",
      "Dataset A images have uniform color profiles and neutral white balance, whereas dataset B images vary widely in color temperature (warm tungsten under museum lights, cool daylight outdoors, colored concert lights), reflecting heterogeneous capture devices and settings."
    ],
    "unmet_v11_label_relation": [
      "Dataset A images have a synthetic or generative-art look with smooth, often surreal textures and occasional geometric distortions, whereas Dataset B images are authentic real-world photographs showing natural material detail and imperfections.",
      "Dataset A tends to isolate objects against softly blurred, stylized or gradient backdrops, while Dataset B places subjects in fully contextual environments\u2014workshops, museum galleries, outdoor scenes and concert stages\u2014often with clutter and background detail.",
      "Lighting in Dataset A is uniformly even and \u201cstudio-soft,\u201d giving a flat appearance, whereas Dataset B exhibits a wide variety of real lighting conditions (harsh spotlights, stage colored gels, directional sunlight, low-light grainy scenes).",
      "Color palettes in Dataset A lean toward oversaturated or pastel hues with artificial transitions, while Dataset B retains natural color reproduction with realistic contrasts, film-like grain and white-balance variations.",
      "Compositions in Dataset A are more stylized and symmetrical, with objects often centered and unobstructed; Dataset B\u2019s compositions are candid and documentary-style, frequently showing occlusion by hands, other objects or people in the frame.",
      "Dataset A almost never shows human interaction with objects, reinforcing a \u201cproduct-photo\u201d or purely conceptual mood; Dataset B regularly features people carrying, holding or using the subjects, lending narrative context.",
      "Photographic imperfections (lens flare, motion blur, high ISO noise, reflections) are absent or minimized in Dataset A but are commonplace in the real-world images of Dataset B.",
      "Perspective in Dataset A can feel slightly off or manipulated\u2014angles are idealized\u2014whereas Dataset B maintains realistic camera viewpoints, including three-quarter, overhead and low-angle documentary shots.",
      "Dataset A often reads like digital illustrations or CGI still-lives, while Dataset B reads like field-photography: objects and people are interwoven with their surroundings instead of being isolated.",
      "Overall, Dataset A evokes a controlled, art-directed aesthetic, whereas Dataset B captures spontaneous, on-location photography with all the attendant texture, context and lighting variability of real scenes."
    ],
    "unmet_v15_label_only": [
      "Dataset A images are static, studio-like compositions with isolated objects neatly arranged on uniform backgrounds; Dataset B images are candid, in-situ photographs featuring real-world environments filled with multiple subjects and contextual details.",
      "Dataset A tends to use a square, evenly-lit crop and pastel or muted color grading; Dataset B employs varied aspect ratios with natural to harsh lighting and realistic color rendering.",
      "Dataset A often presents single tools, backpacks or food trays in flat-lay or tight close-up with minimal clutter; Dataset B frequently captures dynamic multi-subject scenes such as concerts, street crossings, or ornate palace interiors.",
      "Backgrounds in Dataset A are minimalistic\u2014plain wood panels, neutral floors or simple textures; backgrounds in Dataset B are complex\u2014crowded stages, city streets, detailed museum galleries or palace walls.",
      "Lighting in Dataset A is controlled and diffuse, yielding soft, even shadows; lighting in Dataset B is mixed\u2014spotlights, stage haze, flash, ambient daylight\u2014resulting in higher contrast, colored glares or lens flares.",
      "Dataset A maintains crisp, uniform focus across the subject; Dataset B displays variable depth-of-field, occasional motion blur and real-world noise or grain.",
      "Color palettes in Dataset A appear stylized or artificially enhanced; Dataset B preserves authentic material textures and natural hues.",
      "Human figures in Dataset A are rare, static or highly posed; Dataset B often shows real people interacting with objects, performing on stage, or moving through urban scenes.",
      "Compositions in Dataset A are centered with clear negative space around a single object; compositions in Dataset B are more dynamic, sometimes off-center, integrating environment and action.",
      "Dataset A exhibits consistent studio-quality clarity and staging; Dataset B reveals real-world imperfections\u2014under/over-exposure, cluttered frames, and unplanned background elements."
    ],
    "unmet_v15_label_background": [
      "Dataset A consists largely of generative or heavily processed images\u2014textures, edges, and lighting often look painterly or CGI-like\u2014whereas Dataset B is made up of straightforward real-world photographs with natural detail and crisp rendering.",
      "Dataset A frames entire scenes with multiple objects, people, and busy backgrounds (workshops, outdoor ruins, market stalls, forest interiors), while Dataset B isolates a single subject\u2014tools, trays, chairs, performers\u2014against simple or intentionally unobtrusive backdrops.",
      "Dataset A uses a variety of camera angles\u2014wide-angle, cinematic, occasional top-down or dramatic perspective\u2014whereas Dataset B sticks to eye-level or slight overhead viewpoints that keep the main object centered and clearly visible.",
      "Dataset A displays highly variable lighting\u2014from moody, low-key interior scenes to bright outdoor sun\u2014often with complex shadows and color casts; Dataset B favors soft, even illumination that highlights surface textures and subtle material details.",
      "Dataset A\u2019s color palettes range from muted monochrome to surreal high-contrast tones, giving a painterly or stylized effect; Dataset B maintains relatively natural, true-to-life color balance across all examples.",
      "Datasets A\u2019s compositions are more freeform and cluttered\u2014objects and people scattered without a clear focal hierarchy\u2014whereas Dataset B uses balanced, symmetrical arrangements and deliberate negative space around the subject.",
      "Dataset A often contains human figures moving or working in context (woodworkers, hikers, chefs) captured in dynamic, environmental storytelling frames; Dataset B either omits people entirely or shows a lone figure posed plainly next to the object like part of a still life.",
      "Depth of field in Dataset A varies widely and sometimes shows everything in sharp focus or unrealistic depth layering; Dataset B consistently employs a shallow depth of field or softly blurred background to isolate the foreground subject.",
      "Backgrounds in Dataset A are richly detailed\u2014urban decay, vast landscapes, ornate architecture\u2014reinforcing scene context; Dataset B backgrounds are deliberately plain or softly textured (studio walls, workbench surfaces) so as not to compete with the main object.",
      "Dataset A images have a more documentary or exploratory feel with unstructured, environmental clutter; Dataset B images read as carefully staged product or still-life photographs with restrained composition and controlled lighting."
    ],
    "unmet_v15_label_relation": [
      "Dataset A images appear to be synthetic or computer-generated with painterly or hyper-stylized rendering, whereas Dataset B consists of natural, candid photographs showing real-world lighting and textures.",
      "Dataset A chiefly depicts isolated objects or fantasy scenes (floating thrones, surreal interiors) in artistically composed settings, while Dataset B records everyday tools, bags, food, and concerts in authentic workshop, museum, or outdoor contexts.",
      "Dataset A backgrounds are often clean or deliberately blurred and color-graded for dramatic effect, whereas Dataset B backgrounds are literal\u2014wood benches, museum walls, tile floors\u2014with ambient clutter and environmental cues.",
      "Lighting in Dataset A is frequently dramatic, directional, or otherworldly (glowing highlights, unreal reflections), whereas Dataset B uses natural or stage lighting that produces visible shadows, lens flares, and photographic artifacts.",
      "Dataset A exhibits uniform framing, perfect focus, and minimal noise characteristic of CG renders, while Dataset B shows camera artifacts, variable focus, occasional blur, and even watermarks or photographer credits.",
      "Dataset A rarely features people, focusing instead on objects or digital scenes, whereas Dataset B includes human subjects interacting with the scene\u2014musicians, cyclists, museum visitors\u2014adding documentary context.",
      "Dataset A compositions tend to be symmetrical and centrally framed with minimal negative space for a surreal look, whereas Dataset B often uses off-center or top-down angles and embraces environmental negative space.",
      "Dataset A often places elaborate thrones or chairs in fantastical or landscaped environments, while Dataset B shows real chairs or thrones in genuine museum or historical interiors under ambient lighting.",
      "Dataset A close-ups display hyper-detailed, algorithmically perfect textures with uncanny sharpness, whereas Dataset B close-ups use genuine shallow depth-of-field and optical bokeh to highlight real material surfaces.",
      "Dataset A color palettes swing between oversaturated fantasy tones and pastel gradients, often stylized, whereas Dataset B maintains realistic color reproduction true to object materials and ambient conditions."
    ]
  }
}