WO2009090391A1

WO2009090391A1 - Garment filter generation system and method

Info

Publication number: WO2009090391A1
Application number: PCT/GB2009/000109
Authority: WO
Inventors: Alexandra Bell
Original assignee: Alexandra Bell
Priority date: 2008-01-18
Filing date: 2009-01-16
Publication date: 2009-07-23
Also published as: GB2469256A; GB201013786D0; GB0800958D0

Abstract

A method of generating a garment filter for determining garments suitable for a body, the method comprising the steps of inputting body data representing a plurality of points on or under a surface of the body; determining one or more body measurements from the body data representing a plurality of points to obtain at least a first determined body measurement; determining a relative magnitude of the first determined body measurement and either a second determined body measurement or a predetermined body measurement to generate body proportion data; comparing the body proportion data to at least one predetermined body proportion criterion to give comparison data; from the comparison data generating a garment filter operable to filter garment data relating to a plurality of garments to obtain garment subset data relating to a subset of the garments; and outputting the garment filter.

Description

Garment Filter Generation System and Method

The present invention relates to a system and method for generating a garment filter for determining garments suitable for a body.

Until the early twentieth century clothes were home made, or individually tailor made for those could afford it Garments were created manually for each individual, by a person taking individual body measurements and the garments being made-to-measure or custom-made. In the 1920's mass production of clothes began, involving one form of a garment being created from a pattern devised from a model to give one size and other sizes being created by scaling this pattern up or down by a few centimetres throughout.

Mass production of clothing is today a massive industry. However human beings are a large variety of shapes and sizes and it is rare for a mass produced garment to properly fit and suit an individual. Even if a garment has been tried on in a changing room before purchase, it is unlikely that it will be the right size, shape and style for the individual wearing it.

Mass production today uses a graded sizing system in much the same way as described above. This starts with a base size which is then proportionally graded (scaled) to create a multiple set of sizes. Sizing systems vary in range from only a few sizes to a full spectrum of, say, UK sizes 2-20.

Typically, an apparel company arrives at a sizing system for a product line as follows. First, it defines a target market and typical customers by identifying demographic characteristics, such as age, income, ethnicity, and lifestyle. Then the firm chooses a single person - the "fit model" - to be the idealized body shape for that product and market. Prototype garments are created, then evaluated and modified in fitting sessions on the single fit model. A base size pattern is perfected for this prototype garment and proportional grade rules are used to scale a set of patterns up and down for the rest of the size range.

Proportional grade sizing systems do not address the differences in the basic shapes and body proportions of the population, such as small or large waist, short or long torso, or the differences across ages and target markets. A single fit model has a particular body shape that is translated to the full range of sizes and therefore using a finite set of sizes for an almost infinite range of body types is the reason mass produced clothes do not fit well. Additionally a garment style that suits the fit model will not suit all of the range of body types. This is demonstrated by research from Cornell University, some results of which are shown in figures 1 and 2.

Figure 1 shows three 3D body scans illustrating the significant variation in proportions among three women, each of whom is size 10. It can be seen that a size 10 garment would not properly fit and flatter all, if any, of the women shown. Figure 2 shows a graph of variation in fit of trousers across a female sample. The star on this graph identifies the fit model, whose measurements are used to develop a sizing system for a company's target market. The other points on the graph display the trouser fit for 140 female subjects with various body shapes. The trousers for each subject were chosen by hip fit and it can be seen that the hip trend line is flattest, indicating the least variation in fit at the hip. The waist trend line indicates the greatest variation in fit. Those with curvy proportions are closer to the fit model's measurements and are better fitted. It can be seen that there is a vast range of different body shapes across the sample.

It is possible for purchasers of clothing to improve their chances of acquiring mass produced garments as close as possible to suiting an individual by using personal shoppers, who exist to give advice regarding suitable clothing purchases. This advice may include advice relating to the style of clothing such as the cut, hem lengths, neck shape, texture, materials, surface pattern and colours that suit and flatters the individual. However this facility is not readily available to the average purchaser of clothing. It tends to be expensive and by appointment and in any case, personal shoppers do not take measurements other than to gauge a standard size. Expert individuals such as tailors and couturiers do take measurements and have knowledge and expertise in how to dress a person suitably, for example by identifying the size, shape and style of clothing required to properly fit and flatter a particular individual's body. However, such facilities are even less readily available to the average purchaser of clothing and are very labour intensive and therefore expensive.

Another issue with mass production is that of managing the supply chain in terms of making the most efficient use of available resources. For example, garments are made before it can be known what the demand will be for the garments, in terms of both size and style, and therefore it is hard to minimise wastage of stock whilst ensuring that enough stock is available to meet demand.

With the development of the internet it is now possible to buy clothes online. However in this case of choosing and buying clothes remotely it is not even possible to try on clothes before purchase so if mass produced garments are bought in this way the chances of a garment fitting and suiting an individual are even smaller than for off the peg purchases in conventional shops and therefore garments must often be returned to the seller as unsuitable in terms of size or style.

There now exist some improvements in provision for buying clothes over the internet such as enabling a customer to input her/his measurements and then using this information in various ways to attempt to provide garments which are nearer to the correct size for a particular person. For example, a list of predetermined sets of measurements can be displayed for a customer to select those that are closest to her/his own or alternatively a third party may perform this service, in order to determine the most appropriate standard size. However, as well as still being limited by the restraints of using standard sizing, these online approaches have the problem that self-measurement and self-evaluation is required, which tends to be inaccurate. Further, this does not help in providing garments which are a suitable style for the person's body.

There have been developed some approaches which enable provision of clothing of improved size, including when purchasing over the internet, that do not rely on self-measurement. Data from a body may be captured for example with three- dimensional (3D) body scanners. These can gather size data in a faster and more reliable manner than is possible compared to a person taking their own measurements.

This size data can be used to provide clothes for a specific individual on the basis of a scan of the individual's body. However, current approaches making use of techniques such body scanning have only managed limited success in providing clothing that fits and suits an individual. This is because obtaining accurate size measurements only goes some way towards enabling selection of appropriate garments for an individual. There is a lack of consideration of appropriate styling for an individual in the clothing provided on the basis of data captured, for example, in a body scan.

There is currently no system which offers automated 'tailoring' of clothing which provides both the benefits of traditional human services, such as provided by a traditional human tailor or personal shopper, with the benefits of technology to provide mass customization.

The present invention is set out in the claims.

The disclosed system and method making use of body proportion data generated by determining a relative magnitude of a first determined body measurement and either a second determined body measurement or a predetermined body measurement from which comparison data is obtained and then a garment filter generated enables body proportions to be taken into account when determining appropriate garments for a particular body. A garment filter obtained in this way, taking into account body proportion, is able to filter out, for example, garments with style features that are ill suited to the body from a set of garments, to present to a user a subset of garments from the set from which the user may select a garment. In this way, when garment options are displayed, for example on a web page, the amount of data required to be transmitted is reduced, and reduced in such a way that benefits the user, by displaying only garments determined as appropriate for the body of that user. This enables streamlining of data to be transmitted to a garment manufacturer or individual by only transmitting data relating appropriate style choices for the individual's body.

Futhermore, automatic creation of garments actually suited to an individual's body shape and size is enabled. These fit better than is currently possible with mass produced garments and are flattering. Optimisation of resources used in manufacturing and purchasing garments is enabled, by enabling optimised mass customization of clothing to be provided. The benefits of customized products and services such as tailoring, style advice and made-to measure, made-to-order garments and custom-made garments may be brought to the masses.

Examples of the present invention will now be described with reference to the accompanying drawings, in which:

Figure 1 shows 3D body scans illustrating a significant variation in proportions among three women;

Figure 2 shows a graph of variation in fit of trousers across a female sample;

Figure 3 is a diagram of an example body scan system;

Figure 4 is a flow diagram of example steps carried out in generating a garment filter; and Figures 5 shows diagrams of the front and back respectively of a body showing example locations on the body surface for which data items are acquired.

In overview, with reference to figure 4, body data is input 100 to an analyser 3 (see figure 3). The body data represents points on a surface of body or under a surface of a body. An example of surface data is data points (such as coordinates in 3D space) for locations at, say, a wrist or shoulder. The data may also include data points for locations under the surface, such as points representing the position of a hip bone, which may be located below the surface using known methods. The body data may contain a collection of points that can be output in the form of a virtual model if captured by a scanner. Methods of determining the body data are discussed below.

Body measurements are determined 110 from the body data. For example, a length measurement may be obtained by calculating the distance from a data point at the waist to a point at the neck. Area or volume may be obtained in a similar manner or directly from the body data if this is appropriate (for example in the case the the data forms a 3d mesh as discussed below). In this example, this firstly requires appropriate points on the waist and neck to be located.

Body landmarks may be automatically located within the body data, or from markers applied to the body, in order to locate where on the surface of the body the required points are to be found. Points on the surface of the body may be identified for the purpose of determining the desired body measurements), and also for location of points under the body surface such as defining hip bone position as mentioned above, by any suitable method. Examples are a 3D optical system employing light reflective markers on the body, an automatic landmark recognition system or other 2D or 3D methods known to the skilled person, as discussed further below. Figure 5 shows examples of points on a body surface which may be used for determining body measurements and which may therefore be located from the body data. Points A to I in figure 5 are as follows: A at the waist, B at the neck, C at the coccyx, D at the elbow, E at the highest point of the collarbone, F at the widest point of the hip bone, G at the wrist, H at the knee, I at the heel. Areas J to O are as follows: J at the buttocks, K at the hip/upper leg, L at the chest, M at the lower leg, N at the upper arm, O at the belly, P at the top of the head. Points may be obtained from any part of the body, including fleshy areas.

The body measurements determined may be lengths, areas, angles and volumes of the body. Examples of lengths which may be calculated from the points shown in figure 5 are the lengths from A to B, A to C, E to F, F to H, H to I, D to G, height of D relative to A, height of G relative to F. Areas, angles and volumes may be determined as necessary depending on the form of the body data. This may be, for example, using appropriate known calculation using available data points on the body surface.

Proportion measurements are each determined 120 by either determining the relative magnitude of body measurements determined 110 from the body data or determining the relative magnitude of a determined body measurement compared to a standard, predetermined body measurement, as discussed below.

Relative magnitudes of body measurements may be determined, for example by calculating ratios of pairs of obtained body measurements, to obtain 120 body proportion data. Examples of ratios which may be acquired are the lengths of the arms to the wrist compared to the length of the torso, the length from knee to ankle compared to hip to knee, the width of the back compared to the arm length, the width of the back at the shoulder blades to the length of torso; the shoulder to hip length to hip to ankle length. Ratios may be obtained from one or more of length, angle, area and volume measurements. For example, as well as length to length ratios such as the examples given above, a ratio may be a length to volume ratio or an area to area ratio or an angle to angle ratio, such as the angle between a vertical and a line joining a hip to a shoulder versus a 90 degree angle, and any combination of any such ratios.

Another possibility for determining body proportion data is by determining the relative magnitude of a determined body measurement with a predetermined body measurement. For example, a ratio of the volume of the stomach to a standard stomach volume may be determined, wherein the standard volume may be, for example, an average volume.

The acquired body proportion data is compared 130 with one or more body proportion criteria to give comparison data. Possible body proportion criteria include a threshold value and a range of values. This comparison data may then therefore contain information such as a ratio of two determined body measurements, or a ratio of a determined body measurement to a standard, being higher or lower than a threshold value specified as a body proportion criterion or outside a range specified as a criterion.

A garment filter is generated from the comparison data. Comparison data is mapped on to filter criteria for excluding certain garments or categories of garments based on the comparison of the body proportion data with the criterion or criteria. Certain garments or categories of garments may be excluded because they have a particular style feature, for example a particular neck shape. This garment filter is operable to filter garment data relating to a plurality of garments to obtain garment subset data relating to a subset of the garments. Various different comparisons may be made and information thereon combined to provide a filter with a desired level of filtering. For example, the garment filter may comprise information on an appropriate style type of garment dependent on filter criteria for filtering out inappropriate garment styles based comparison data which shows whether the proportion data on, for example, measured stomach volume compared to a standard stomach volume falls within a predetermined range of proportion values or whether or a ratio of elbow to wrist length to shoulder to wrist length is greater than a predetermined value. Alternatively the comparison data may combine the information on both these possibilities. It can be seen that in this way a complex combination of different proportion information may be taken into account by the filter by mapping comparison data for a plurality of different comparisons of various body proportions with various body proportion criteria.

The garment filter, for example comprising a list of style criteria appropriate for the body, may be displayed to an individual, or transmitted to a garment manufacturing facility. The garment data may be used to filter a list of available garments, for example, a list of an online store's entire clothing range, to display to the individual or manufacturer, for example, only garments meeting the style criteria determined to be appropriate for the individual's body.

The body data may be acquired, for example from a body scan. An example scanning system is shown in figure 3, comprises a 3D body scanner 2 for scanning the body 1 of a person and an analyzer 3 for analyzing the scan results as discussed above in order to generate the garment filter. The analyzer 3 may be part of the body scanner as shown, or remote from the body scanner 2. A body 1 may be scanned by the body scanner to obtain body data which is used as the input 10 shown in figure 4. At the same time, information on colour, such as hair and skin tone, may be obtained, for example by measuring light reflected from the body, which may be used in generating one or more further filtering criteria. For example, a particular skin colour may not suit a particular garment colour, tone or shade.

A specific example is discussed below, using an example of a body scanner.

A person with light reflective markers placed at desired positions on their body steps into a scanner to be scanned. The whole body is scanned (either static or with some movement). The 3D scan produces a plurality of points in 3D space, which may be, for example, in the form of a cloud point model or polygonal model, representing the body that was scanned, with the reflective marker points mapped to the model. The model is to scale. Each point represents a point on the surface of the body, with the marker points highlighted. From this body scan, body size data may be extracted.

Points on the to-scale model may be located from the markers. Various body measurements are determined and a garment filter using the acquired information is produced for the individual as described above. This may be thought of as the individual's "style DNA" It is the basic code of information that enables the individual that was scanned to be able to order mass customised or individually tailored clothes remotely (on the internet) that will fit them and flatter them in terms of, for example, size, cut, colour, fabric, texture, pattern, materials and form.

For example, for a proportionally higher stomach volume than a preset average model, as determined, for example, by a ratio of stomach volume to a preset volume being > 1, such information being comprised by the comparison data, the comparison data may translate to a garment filter which selects flat fronted trousers with a wide waistband and cut low on the hip by filtering out trousers which do not fit these criteria. The comparison data may also comprise, for example, information on proportional height (compared to a standard height and/or, for example, torso length) so that many known variables of an individual shape and proportion may be taken into account for styling and tailoring.

In a second example, in simple terms, a short torso for example may suit gently waisted jackets with a hem at low the hip in a textured fabric. A longer torso may suit a longer jacket that finishes higher on the the hip to make the 'shorter' legs appear longer, giving a visual effect of balance. Therefore a ratio of body measurements of torso length to whole body length may be determined and the result of the ratio calculation compared to a body proportion criterion of a torso to body height range. If the resultant comparison data is that the ratio is below the range, then the garment filter generated from this may be operable to filter out jackets longer than a certain length, for example, from a displayed jacket selection (for example on a web page), and if it is above the range, then the filter generated is operable to filter out jackets shorter than a certain length.

In more detailed related example, the type of jacket which suits a person may also depend on the volume of the chest area. For example, a person with a large chest volume may advisedly wear a single breasted jacket with a traditional collar. Therefore data relating to chest proportion (for example, ratio of measured chest volume to a standard chest volume ) may be also be compared to a different appropriate body proportion criterion of, say, a particular ratio value (e.g. 2), to add to the comparison data information on whether chest proportion is above or below this value, to generate a garment filter which filters jacket length based on both torso and chest proportions.

Another example is for where the waist of a dress falls on the body. A wide back with proportionately shorter arm-length may suit jackets with a different cut of lapel, sleeve and collar style than a body that is lean and long.

Hem lengths of jackets, trousers, skirts and sleeves, collar style (round neck, V- neck) are further examples of style elements that may be filtered by the garment filter depending on body proportion. Factors such as the choice of fabric, the cut and colour of the garment, the type of fabric and how the fabric falls and flows over the body and how these apply to different body proportions may be taken into account to determine mappings from comparison data to the garment filter. Where the lines of the clothing (hems or sleeve-length for example) 'cut', shape or hug the body may be considered. AU these factors make the difference between being elegantly dressed or making a poor style choice for an individual and therefore may contribute to a useful filter to filter available garment data in an optimal manner.

An individual's proportions may be used to classify an individual has having a particular 'style type' or one of a number of 'style types' as determined form the obtained body proportion data . However each person is unique and the individual results of style DNA data for the filter may represent this.

Further filter criteria may be used in addition to the criteria derived from comparison data. This may allow for factors such as hair colour and skin colour to be taken into account, as discussed above. Additionally, filter criteria may take into account factors such as a person's age, culture or other personal preferences.

After the scan the person may obtain a logon ID for accessing the garment filter via internet or intranet. This may be used to log on to a website that uses the garment filter to filter garments and allows the scanned person to view collections of clothing on the internet and order garments suited to their body.

A website may operate as a dedicated personal stylist and also have other related services. For example, the garment filter may filter appropriate garments from mass produced garments available from an online retailer to show to a person only those garments with style details deemed suitable for the body proportion data obtained for that person by application of the predetermined body proportion criteria.

The garment filter may be used in combination with captured size information, for example from a body scan. This may be used, for example, to also allow a person to choose the correct 'standard size' from manufacturers'/retailers' made-to-stock collections. Manufacturers' and retailers' standard sizes differ widely from each other and the numbers are often meaningless. Size information captured, analysed and output from a scan, may give an accurate size match to each manufacture's or retailer' standard sizes and when provided with the garment filter, together this facilitates online ordering of size accurate and style accurate made-to-stock garments. Such a service may be provided for a manufacturer/retailor which signs up to a service incorporating the garment filter. This is just like having a digital personal stylist when having to choose from a range of made-to-stock finished inventory.

Thus an individual may choose suitable items of clothing and be able put together whole outfits to be purchased on the internet that flatter their unique shape. This means that it does not matter that it is not possible to try clothes on when buying from the internet the individual may be confident that the clothes will be the correct size and style.

Currently with traditional mass production methods items are made-to-stock and then offered for sale. The garment filter allows for a service to be provided to retailers enabling stock levels to be managed effectively with the option of offering custom made or made-to-order clothing , hence offering mass customization. A user may log on to a website incorporating, or having access to, the garment filter that allows the individual to view design collections on the internet and order garments that have not yet been made but exist only as 3d designs (or images of designs) Again, only the items of the collection that would flatter the individual are viewed by the individual, dependent on filtering criteria determined by their body proportion data.

The consumer may then order online from the selection provided by the filter and this order is communicated to the manufacturers) who may then make up the garment. The manufacturer may receive the exact body measurements of the customer from the 3D scan with the order so that the garment can be custom made. The styling/tailoring consultancy part of the total service has already take place in the form of the filtering with the garment filter which selected suitable designs. In this way there is no manufacturing wastage in over forecasted made - to - stock surplus finished inventory as all items are accurately made-to-order.

The output garment filter may be used to custom make a garment and be part of an automated process taking advantage of new technologies aimed at the apparel market. For example a to-scale 3D model may be used to generate a custom made template for pattern cutting , whereby, for example, a 3D polygonal mesh created by a body scan can be unwrapped from a 3D model to a flat 2D grid to create an individual cloth cutting template to cut cloth to size. In addition to this the garment filter provides the information on the style of tailoring.

Furthermore, individual designers may design directly for a consumer on acquiring the consumer's garment filter, which may be combined with size information.

In either case, a consumer may view online items selected for potential purchase, for example on a personalised avatar modelled from both body size data and body proportion data, so proportionally correct.

The disclosed method and system allows virtual advice to be given on styling of garments, which is not just about the size of the garments but about what kinds of style elements suit which kinds of body proportions. This is in a manner similar to stylists, tailors and couturiers delivering a product that visually suits the proportions of a client as well as being concerned about the physical fit.

The disclosed method and system enables automated filtering with a garment filter from data relating to a range of possible garment styles so that accurate customization may be provided using the filtered data, whilst minimizing data transmission required. Style advice may be generated for a customer, for example on the internet. A system that enables consumers to be digitally measured for size and proportion and, for example, for a 3D virtual model to be generated from this more accurate data enables improved provision of mass customization. It means that it is possible to provide clothes with improved fit and that suit individual body shape, owing to improved information in terms of detail and analysis by taking into account body proportion by determining various body ratios, whilst at the same time making use of some of the benefits of new technologies and/or digital production methods as discussed above.

A combination of 3D body scanning hardware, software, analysis and a complimentary web resource for online styling advice and purchase may enable producer-retailers to move from mass production to mass customisation.

A retailer may provide information on a garment range to a garment filter resource and then receive orders of garments selected by individuals from a filtered subset of garments from the range. Material resources may be used more efficiently as garments may be obtained or made after a specific order is received, thus reducing wastage. A large retail space carrying as finished inventory the, whole collection in various standard sizes may be reduced to a much smaller space with a scanning booth in a welcome lounge and no finished inventory garments.

In the case that a body scanner is used, any suitable body scanner, such as of the type giving a point model as an output, may be used. Examples of body scanners which may be used are a body scanner employing laser scanning or white light scanning may be used, together with appropriate software or hardware or combination of the two. The scanning routine may be a known routine, with the possible modification of marking locations on a body surface before the surface is scanned, as discussed above.

The scanner 2 may be of a type fixed in a retail outlet or of a mobile type that may be brought to a person. Any required software or hardware may be incorporated into the scanner 2, as shown in figure 3, or alternatively be remote from the scanner 2. For example, data may be sent over the internet for analysis.

Body data captured by any suitable known means may be used. The examples above discuss using body scan data but information obtained in other suitable ways allowing determination of body measurements may be used.

Any suitable known system for identifying points on the body surface may be used, such as a system of the type used for motion capture using markers placed on the body to capture moving points. Such a type of method uses light- reflective markers put in the desired body locations for the necessary data capture, which can be then mapped to a to-scale scanned model.

An alternative type of system is for a scan to produce a 3D model from a scan and known recognition software to identify points required on a suface mesh produced.

Bones, under the surface, such as hip bones may be pinpointed, for example, in 3d space in known procedures such as used in ASIS PSIS (Anterior Superior Illiac Spine/ Posterior Superior Illiac Spine) analysis.

Any suitable software and/or hardware may be used which has the required functionality, for the analyzer and in the system as a whole.

The body to be scanned is not limited to a human body.

Claims

1. A method of generating a garment filter for determining garments suitable for a body, the method comprising the steps of: inputting body data representing a plurality of points on or under a surface of the body; determining one or more body measurements from the body data representing a plurality of points to obtain at least a first determined body measurement; determining a relative magnitude of the first determined body measurement and either a second determined body measurement or a predetermined body measurement to generate body proportion data; comparing the body proportion data to at least one predetermined body proportion criterion to give comparison data; from the comparison data generating a garment filter operable to filter garment data relating to a plurality of garments to obtain garment subset data relating to a subset of the garments; and outputting the garment filter.

2. A method according to claim 1, further comprising the steps of: filtering, with the garment filter, garment data relating to a plurality of garments to generate the garment subset data; and outputting the garment subset data.

3. A method according to claim 2, wherein outputting the garment subset data comprises displaying at least one of a list or images of garments suitable for the body.

4. A method according to claim 3, wherein the list and/or images are displayed on a website.

5. A method according to any preceding claim, wherein the step of determining one or more body measurements comprises: identifying locations of at least first and second points on or under the body surface; determining the one or more body measurements from body data representing the at least first and second points.

6. A method according to claim 5, wherein the point locations are each at one of: a waist, a neck, a coccyx, an elbow, a collarbone, a hip, a wrist, a knee, a heel and a top of a head.

7. A method according to claim 5 or claim 6, wherein locations are identified from location markers defined in the body data.

8. A method according to claim 7, wherein defining the location markers comprises defining the location markers from markings on the body surface.

9. A method according to any preceding claim wherein the body measurements are each one of a length, angle, area or volume.

10. A method according to any preceding claim, wherein the body measurements are each one of: a distance from waist to neck, a distance from waist to coccyx, a distance from a point of a collarbone to a widest point of a hip, a distance from a widest point of a hip to a knee, a distance from a knee to a heel, a distance from an elbow to a wrist, a distance from an elbow to a waist, a distance from a wrist to a widest point of a hip, a distance from a top of a head to a waist, a top of a head to a heel, a length of a neck to a width of four fingers.

11. A method according to any preceding claim, wherein determining a relative magnitude comprises determining the relative magnitude of first and second determined body measurements by determining a ratio of the first and second determined body measurements.

12. A method according to claim 10, wherein the ratio is one of: length of an arm to a wrist compared to a length of a torso, a length from a knee to an ankle compared to a hip to a knee, a width of the back compared with an arm length, a width of a back at the shoulder blades to a length of a torso, or a shoulder to hip length to hip to ankle length.

13. A method according to any preceding claim, wherein the step of generating a garment filter from the comparison data comprises mapping the comparison data on to garment style data.

14. A method according to any preceding claim, further comprising the step of outputting the garment filter together with body size data.

15. A method according to any preceding claim, wherein the body data is scan data obtained by a body scanner.

16. A method according to any preceding claim, further comprising the step of scanning the body with a body scanner to obtain the scan data.

17. A method according to any preceding claim, wherein the body data comprises body surface data.

18. A method according to any preceding claim, wherein the step of comparing the body proportion data to at least one predetermined body proportion criterion comprises determining whether a ratio value comprised by the body proportion data item has a value above or below a predetermined value or within a predetermined range.

19. A method according to any preceding claim, further comprising inputting colour data and/or age data and generating the garment filter from the comparison data and the colour and/or age data.

20. A method according to any preceding claim, further comprising the steps of: generating a three-dimensional virtual model from the body data and body size data; outputting the model.

21. A system for generating a garment filter for determining garments suitable for a body, the system comprising: an input arranged to input body data representing a plurality of point on or under a surface of the body; body measurement determining means arranged to determine at least a first body measurement from the body data representing a plurality of points; proportion data determining means arranged to determine a relative magnitude of the first determined body measurement and either a second determined body measurement or a predetermined body measurement to generate body proportion data; comparison means arranged to compare the body proportion data to at least one predetermined criterion body proportion criterion to give comparison data; garment filter generating means arranged to generate from the comparison data a garment filter operable to filter garment data relating to a plurality of garments to obtain garment subset data relating to a subset of the garments; and an output arranged to output the garment filter.

22. A system according to claim 21, wherein the body measurement determining means is arranged to identify locations of at least first and second points on the body surface and to determine the body measurements from body scan data items representing the at least first and second points.

23. A system according to claim 22, wherein the locations are each at one of: a waist, a neck, a coccyx, an elbow, a collarbone, a hip bone, a wrist, a knee, a heel a top of a head.

24. A system according to claim 22 or claim 23, wherein locations are identified from location markers defined in the body scan data.

25. A system according to claim 24, wherein the system is arranged to detect body surface markings on the body surface and to define the location markers from markings on the body surface.

26. A system according to any of claims 21 to 25, wherein the body measurements are each one of a length, angle, area and volume.

27. A system according to any of claims 21 to 26, wherein the body measurements are each one of: a distance from waist to neck, a distance from waist to coccyx, a distance from a point of a collarbone to a widest point of a hip bone, a distance from a widest point of a hip to a knee, a distance from a knee to a heel, a distance from an elbow to a wrist, a distance from an elbow to a waist, a distance from a wrist to a widest point of a hip bone, a distance from a top of a head to a waist, a top of a head to a heel, a length of a neck to a width of four fingers.

28. A system according to any of claims 21 to 27, wherein the proportion data determining means is arranged to determine ratios of one or more of:: length of an arm to a wrist compared to a length of a torso, a length from a knee to an ankle compared to a hip to a knee, a width of the back compared with an arm length, a width of a back at the shoulder blades to a length of a torso, or a shoulder to hip length to hip to ankle length.

29. A system according to any of claims 21 to 28, wherein the comparison means is arranged to determine whether the body proportion data comprises a value within a predetermined range.

30. A system according to any of claims 21 to 29, wherein the garment filter generating means is arranged to map the comparison data on to garment style data.

31. A system according to any of claims 21 to 30, wherein the output is further arranged to output body size data together with the garment filter.

32. A system according to any of claims 21 to 31, wherein the system is further arranged to filter garment data relating to a plurality of garments to obtain garment subset data relating to a subset of the garments; and further comprising an output arranged to output the garment subset data.

33. A system according to any of claims 21 to 32, wherein the body data comprises body surface data.

34. A system according to any of claims 21 to 33, wherein the body data comprises colour data.

35. A system according to any of claims 21 to 34, further comprising: a virtual model generator arranged to generate a three-dimensional virtual model from the body scan data and the garment data and to output the model.

36. A body scanning system comprising: a body scanner arranged to scan the body surface to obtain the body surface data; and a garment filter generating system for generating a garment filter according to any of claims 21 to 35.

37. A computer program comprising instructions configured to carry out the method of any of claims 1 to 20.

38. A computer arranged to implement the computer program of claim 37.

39. A system or method substantially as subscribed herein with reference to the accompanying drawings.