Human skeleton proportions from monocular data

Transcription

1 1266 Peng et al. / J Zheang Unv SCIENCE A (7): Journal of Zheang Unversty SCIENCE A ISSN (Prnt); ISSN (Onlne) E-mal: zus@zu.edu.cn Human skeleton proportons from monocular data PENG En, LI Lng (Department of Computng, Curtn Unversty of Technology, Perth, Australa) E-mal: perryp@cs.curtn.edu.au; lng@cs.curtn.edu.au Receved Apr. 11, 2006; revson accepted Apr. 24, 2006 Abstract: Ths paper ntroduces a novel method for estmatng the skeleton proportons of a human fgure from monocular data. The proposed system wll frst automatcally extract the key frames and recover the perspectve camera model from the 2D data. The human skeleton proportons are then estmated from the key frames usng the recovered camera model wthout posture reconstructon. The proposed method s tested to be smple, fast and produce satsfactory results for the nput data. The human model wth estmated proportons can be used n future research nvolvng human body modelng or human moton reconstructon. Key words: Modelng, Human fgure, Monocular do: /zus.2006.a1266 Document code: A CLC number: TP39 INTRODUCTION The problem of creatng a vrtual human has receved a lot of attenton n the last few decades, due to the ncreasng popularty of applcatons nvolvng human fgures, such as moves and computer games. There are many approaches to create a vrtual human body. Tradtonal ways for creatng a vrtual human fgure nclude employng 3D body scanners or usng 3D modelng technques based on understandng of human anatomy. Detaled 3D human body model can be acqured easly usng such methods. However, t s expensve and clumsy to use the scanner, and more serously, the person to be modeled mght not be avalable for scannng. Meanwhle, the human models based on human anatomy generally fall short n representng personalzed ndvduals. Another way to recover the human body s by usng mages. Snce mages are the popular meda that record the human fgure, recoverng human model from mages can avod many lmtatons n tradtonal ways. The research to model human body usng mages can be dvded nto two groups: (1) usng mult-vew mages; and (2) usng sngle-vew mages. Mult-vew mages are employed by many researchers. Some researchers (Hlton and Gentls, 1998; Lee et al., 2000; Vlla-Urol et al., 2003) recover the human body from mult-vew mages of a statc human fgure, whle others reconstruct the vrtual human from mult-vew mages of a dynamc person performng specfed motons (Cheung et al., 2003; Cohen and Lee, 2002; D Apuzzo et al., 2000; Kakadars and Metaxas, 1995; Starck and Hlton, 2003). Methods usng multple cameras suffer the same drawback: the target person has to pose for the cameras at a specfc locaton, normally n a fully-equpped laboratory or studo. On the contrary, sngle-vew mages are convenently avalable n varous formats (e.g., photos, DVD, VHS vdeo tape) to general publc. Hence, human body reconstructon from sngle-vew mages s a very attractve dea. The approaches to recover the human fgure from sngle-vew mages can be separated nto two groups dependng on the camera model adopted: (1) usng affne camera model; and (2) usng perspectve camera model. Affne camera model s only an approxmaton of the real camera model. Scaled-orthographc camera model s an mportant nstance of the affne camera model and s popularly used by many researchers (Barrón and Kakadars, 2003; Remondno and Rodtaks, 2003). However, such camera model can only handle mages wth very lttle

2 Peng et al. / J Zheang Unv SCIENCE A (7): perspectve effects. To handle mages wth any perspectve effects, the perspectve camera model s requred snce t represents a real camera. There are lmted research efforts (Peng and L, 2005; Zhao et al., 2005) workng on human model reconstructon based on perspectve camera models. Some researchers (Zhao et al., 2005) restrct all body segments of the human fgure as almost parallel to the mage plane n order to acqure accurate human skeleton proportons. Some researchers (Peng and L, 2005) requre estmatng the vrtual scale parameters for each frame. The perspectve camera models used n these researches are ether pre-defned (Zhao et al., 2005) or manually defned (Peng and L, 2005). To automatcally recover a perspectve camera model, camera calbraton s a popular technque whch addresses the ssue of reconstructng the camera parameters usng 2D mage ponts and the correspondng known 3D obect ponts (Bacakoglu and Kamel, 1997; J and Zhang, 2001; Memon and Khan, 2001; Batsta et al., 1998). However f only the 2D nformaton s avalable as n monocular mages or vdeos, such camera calbraton wll not be applcable. The human body s an artculated 3D obect. Skeleton proportons domnate the appearance of a human fgure, regardless of the heght of the human fgure. Skeleton proportons may be dfferent between and among dfferent populatons (e.g., men vs women, adults vs chldren). Estmatng the skeleton proportons s the crucal step before reconstructng the 3D shape of a human body, and t s crtcal for any model-based human moton reconstructon and trackng. Therefore, t s clearly desrable to develop new algorthms to automatcally recover a perspectve camera model based on 2D features only and then recoverng the skeleton proporton of a human fgure usng the recovered camera model. Ths paper proposes a novel system to estmate the skeleton proportons from sngle-vew mage sequence usng an automatcally recovered perspectve camera model. In the proposed system, the camera s assumed to be fxed and almost parallel to the floor durng capturng and there should be at least one foot of the human fgure touchng the floor at any moment durng capturng. The rest of ths paper s organzed as follows: The next secton provdes an overvew of the proposed algorthm; Sectons KEY FRAMES EX- TRACTION, CAMERA MODEL RECOVERY, and SKELETON PROPORTIONS ESTIMATION dscuss the maor parts of the proposed system; Secton RESULTS show the results wth dscusson; and the last secton concludes the paper. OVERVIEW Ffteen feature ponts are assumed known from any nput source, as shown n Fg.1. WR 1 EB 1 SD 1 HP 1 KN 1 AK 1 HT NK SD 1 AM HP 2 KN 2 AK 2 EB 2 WR 2 Fg.1 Feature ponts of human fgure The proposed system s shown n the chart n Fg.2. There are three mportant modules n ths system: (1) key frames extracton; (2) camera model recovery; and (3) skeleton proportons estmaton. 2D data from nput frames (3) (1) (3) Extracted key frames (2) Estmated human skeleton proportons Recovered perspectve camera model Fg.2 Human proportons estmaton system (1) Key frames extracton; (2) Camera model recovery; (3) Skeleton proportons estmaton The key frames extracton module dentfes the frames wth both feet touchng the floor from all nput

3 1268 Peng et al. / J Zheang Unv SCIENCE A (7): frames. It s assumed that n the nput mage sequence at least one foot touches the ground at any moment. Hence there wll be two possbltes for each frame: (1) one foot s the stance foot; (2) both feet are stance feet. The proposed algorthm can determne the probable case for each frame from nosy nput data. The module s further dscussed n a later secton. The camera model recovery module wll reconstruct the perspectve camera model. Tradtonal camera calbraton method requres correspondence between both the 2D and 3D nformaton and hence could not be adopted n ths proect. The proposed method wll be based only on 2D data, whch are the proectons of 15 feature ponts. The module wll also be further dscussed later. The skeleton proporton estmaton module wll reconstruct the human skeleton proportons from the 2D data n the key frames usng the recovered perspectve camera model. A vrtual human fgure wll be reconstructed to match the proectons from all nput frames under the vrtual envronment. The skeleton proportons of ths vrtual human fgure are the desred human skeleton proportons, snce scalng does not change the proportons. Agan ths module wll be further dscussed n a later secton. KEY FRAMES EXTRACTION The nput 2D data contan a movng human fgure, assumngly wth at least one foot placed on the floor at any moment. Each nput frame dsplays ether both feet touchng the floor or only one foot touchng the floor. The key frames extracton module frst determnes whch foot touches the ground for each frame, then automatcally choose the frames wth both feet touchng the floor. Two steps are requred for the determnaton of the stance foot. Frst t s determned whether a foot n the frame s a possble statc foot. Next t s determned whether the statc foot s a stance foot. Possble statc foot Snce the camera s fxed, f a foot remans statc for a perod, ts proecton should also stay at the same poston. Hence, f the nput data are perfect, when the proecton of a foot remans at the same 2D poston wthn a few neghborng frames, ths foot may reman at the same 3D poston durng ths tme, t may also move along the straght lne passng through the camera center and the foot tself. In ths system, the foot from any of these two possbltes s consdered as a possble statc foot. Due to errors n feature extracton, such as noses, the extracted feature pont of a statc foot may not accurately represent ts actual proecton. It s possble that both feet appear to be dynamc n all frames, udgng only from ther proectons. If the noses n the nput data are not sgnfcant, an error threshold δ can be utlzed. When the proectons of the same feature pont wthn k frames fall nsde a crcle wth radus δ, ths feature pont s consdered possbly statc wthn these k frames. In ths way, the statc foot can be determned for the nput frames. There are four possbltes for each frame: N none of the feet s statc; 1 foot 1 s possbly statc; 2 foot 2 s possbly statc; B both feet are possbly statc. Stance foot To determne whether a possble statc foot s a stance foot, the sequence of foot status s nvestgated. If a frame has the status 1 or 2, the correspondng foot must be a stance foot. But when a frame has the status N or B, t needs to be further analyzed. Status N ndcates that nether foot s statc accordng to the extracted proectons. It happens manly when the nput error exceeds the gven threshold. Such stuatons can be handled as follows: for the case NN...N11 1 and case NN N22 2, f the number of N frame s very small, the proecton n the frame wth N can be adusted accordng to the proectons around ths frame, n whch way the status N can be elmnated. Smlarly, cases 11 1NN...N22 2 and 22 2NN...N11 1 can be adusted and become 11 1BB B22 2 and 22 2BB B11 1. However, when the number of N frames s large, ths system wll not be able to be handled because t ndcates the nput data are very unstable. Status B ndcates that both feet are statc. However one of the feet may be statc n the ar or movng along the straght lne passng through the camera center and the foot tself. Such foot cannot be consdered as a stance foot. In the case BB B11 1 or 22 2BB B22 2, one

4 Peng et al. / J Zheang Unv SCIENCE A (7): foot remans statc durng the whole perod whle the other foot keeps stll only for a short perod of tme. So, the other foot s unlkely a stance foot and such cases can be revsed to 11 1 and Only n cases 11 1BB B22 2 or 22 2BB B11 1 can the status B be consdered as both feet touchng the floor. Such frames are the desred key frames. Camera center y O f 36 mm Image plane x z 24 mm CAMERA MODEL RECOVERY y The camera model recovery module uses a novel method to obtan a pn-hole camera model from 2D data. To recover a pn-hole camera model, the poston, orentaton, flm sze and focal length of the camera need to be recovered. In ths proect, the camera coordnate system s used, whch means that the camera center s the orgn of the coordnate system, the orentaton of the camera defnes the z-axs. The flm sze s set as the conventonal 35 mm (36 mm 24 mm). Therefore, the focal length of the camera s the only factor needed to be recovered. As shown n Fg.3a, the camera wth focal length f s located at the center of the world coordnate system, and the mage has the sze of 36 mm 24 mm. Snce the orentaton of the camera s assumed parallel to the floor, the floor can be represented as y= h C, where h C s the heght of the camera from the floor. Assume that the human fgure wth heght h H s upstandng at the moments M and M, and the dstance between the camera and the human fgure n z drecton s d at the moment M, and d at the moment M. Fg.3b shows the scene at moment M, where the proecton of the human fgure has the heght of h n the mage plane. The followng relatonshps can be determned for the moment M and M : Moment M : h / hh = f / d, (1) Moment M : h / h = f / d. (2) H Camera center h C O Image plane f h d Actual human fgure Floor (y= h C ) Fg.3 Camera model used n ths proect. Camera model; Sde vew of the scene As h and h can be obtaned drectly from the mages, f the rato d d /h H s also known, the focal length f of the camera s possble to be calculated drectly from 2D data usng Eq.(3). Human walkng s a common human moton that exsts n many sources. Durng human walkng, one foot lands before the other takes off. Therefore, there s at least one foot that touches the floor at any moment. Each tme one foot gong forward wll make a step, and two successve steps make a strde, as shown n Fg.4. The step length s the dstance from one foot strke to the next (left to rght or rght to left) whle the strde length s double the step length. The normal strde length s roughly the same as the human heght (stature) (Krtley, 1998). Therefore the normal step length can be consdered as half of the human heght. Each tme the human fgure makes a normal foot step, the fgure moves about half of ts heght. h H z Wth Eq.(1) and Eq.(2), the followng relatonshp between the focal length f and the other factors can be establshed: Step Step f d d hh =. h h h H (3) Strde Fg.4 Foot step and foot strde (Krtley, 1998)

5 1270 Peng et al. / J Zheang Unv SCIENCE A (7): If the moments M and M represent two successve steps, the dstance between the human fgures n the two moments s about h H /2, whch leads to d d h H /2. Thus, Eq.(3) can be smplfed to: hh f. 2 h h (4) In ths proect, the stance foot for each frame s determned after applyng the algorthms n Secton KEY FRAMES EXTRACTION. Key frames wth both feet on the floor should have been extracted. Hence, t s not dffcult to automatcally obtan a par of key frames representng two successve steps. Actually, a number of pars can be obtaned f the vdeo contans a long human walkng sequence. Let the number of such pars of key frames be n, accordng to Eq.(4), each par of such key frames wll yeld: f f k (k=1, 2,, n). (5) Thus, the focal length f of the camera model can be approxmated as: f mn( f 1, f 1,, f n ). (6) SKELETON PROPORTIONS ESTIMATION The basc dea of the skeleton proporton estmaton s to fnd a vrtual human model whch can reproduce the proecton data n the vrtual envronment. The skeleton proporton can be calculated from ths vrtual human. It s well-known that any proecton pont on the vewng plane can be back-proected to an nfnte number of possble 3D postons. Fortunately, the stance foot s determned n each frame. Thus, gven the vrtual ground and the perspectve camera model, the 3D poston of the stance foot can be calculated. The vrtual human fgure on the vrtual ground wll be modeled from the stance foot. The estmaton process s shown n Fg.5. Durng the estmaton process, there are three basc actons PICK, TEST and CALC. PICK The purpose of ths acton s to assgn a length CALC (AK) PICK (AK-KN) PICK (KN-HP) PICK (HP-AM) PICK (HP 1 -HP 2 ) TEST (KN, HP, AM) If no better result after u 1 tmes of retres CALC (KN, HP, AM) PICK (AM-NK) PICK (NK-SD) PICK (SD-EB) Retry PICK (NK-HT) PICK (EB-WR) Fg.5 Skeleton proportons estmaton system for a specfed skeleton segment. Ths length should be wthn the range of all possble skeleton lengths. Hence the lower boundary and the upper boundary of the range should be determned frst. The lower boundary of the range s defned as follows: f the skeleton segment has length below ths lower boundary, t s not possble to have ts proecton matchng all key frames under the estmated camera model from the gven 3D poston. Instead, f a skeleton s longer than the lower boundary of the range, t wll forever be able to produce the gven proecton. The upper boundary of the range s ntroduced for the purpose of mnmzng the searchng range. It s defned to allow a skeleton segment to gve the proecton from any possble 3D poston. For the th nput key frame, the lower boundary and upper boundary of the possble skeleton length can be calculated as L mn () and L max (), ndcatng that the skeleton length l should satsfy L mn () l L max () n the th frame. Therefore, f all key frames are consdered, the global upper and lower boundares can be acqured as: L mn =max(l mn (), =1, 2,, n), (7) L max =max(l max (), =1, 2,, n). (8) Any length wthn ths range, L mn l L max, s possble for the skeleton segment. TEST The purpose of ths acton s to test whether the estmated skeleton could produce reasonable postures

6 Peng et al. / J Zheang Unv SCIENCE A (7): n frames. Startng from ont AK (=1, 2 as shown n Fg.1), there are up to 4 possble 3D postons for the correspondng ont HP. Subsequently, there are up to 16 possble combnatons of postons for the ont HP 1 and HP 2. Each of such combnatons forms a unque trangle HP 1 -AM-HP 2 from the proecton. The sdes of these trangles are compared to the estmated length of HP -AM and HP 1 -HP 2. If none of these trangles match the estmated trangle n sde lengths, more teraton should be conducted, untl the accumulated dfference s under a gven threshold or the maxmum number of teratons s reached. CALC The purpose of ths acton s to calculate a unque 3D poston of the feature ponts n the vrtual scene for each key frame. For example, CALC(AK ) wll calculate the postons of the ont AK wth the help of the vrtual camera and vrtual ground. CALC (KN, HP, AM) wll calculate the postons of ont KN, HP, and AM that can provde the best result n the acton TEST (KN, HP, AM). RESULTS The proposed system s tested on the 2D data extracted from two real vdeo sequences. Both vdeo sequences record the walkng moton of a human fgure. The vdeo camera s fxed and mounted on a trpod, wth ts orentaton parallel to the floor. The frst sequence Vdeo 1 contans 113 frames. It s captured by the vdeo camera wth focal length mm (n 35 mm format). The second sequence Vdeo 2 contans 141 frames and s captured by the vdeo camera wth the focal length 400 mm (n 35 mm format). Fgs.6a and 6b show an example frame of Vdeo 1 and Vdeo 2, whle Fgs.6c and 6d llustrate the 2D data extracted from these two example frames. The proposed system wll work on the extracted 2D data. The 2D data from each nput vdeo sequence are processed followng the flow chart shown n Fg.2. Three maor steps are nvolved: (1) extract the key frames from the 2D data; (2) recover the camera model from the key frames; (3) estmate the human skeleton proportons from the key frames usng the recovered camera model. The algorthm used n each step s tested and the results are presented and dscussed n the followng subsectons. (c) Fg.6 Input data. Vdeo 1; Vdeo 2; (c) 2D data 1; (d) 2D data 2 Test of key frames extracton In the proposed system, key frames are defned as the frames that record the moment when both human feet are placed on the floor. The frst step of the proposed system s to fnd all such frames. Key frames are automatcally extracted from the 2D data n the key frames extracton module. The extracton algorthm frst determnes the foot that remans motonless n each frame, and then extracts the stance foot from the motonless foot. Fnally, the frames wth two stance feet are chosen as the key frames. The experment results of the key frames ex- Estmaton: Actual: Frame ID: Estmaton: Actual: Frame ID: Fg.7 Key frames extracton results. Key frames n nput 2D data 1; Key frames n nput 2D data 2 (d)

7 1272 Peng et al. / J Zheang Unv SCIENCE A (7): tracton algorthm are shown n Fg.7. The horzontal axs of the fgure ndcates the frame ID. If a frame s extracted as a key frame, a bar wll be drawn: the red bars on the top are the extracton results by the proposed algorthm, whle the blue bars at the bottom ndcate the manually extracted key frames through the udgment of human eyes for comparson purpose. It can be seen that the key frames extracted by the proposed algorthm and the manually extracted key frames are very close. The proposed algorthm may extract some frames by mstake. However, such frames are very close to the key frames. Such frames can be accepted as key frames snce both feet are actually very close to the floor n them. Test of camera model recovery Camera model recovery s the second mportant module of the proposed system. The camera model used n ths proect s a fxed perspectve camera wth ts orentaton parallel to the floor. As dscussed before, the only camera parameter to be recovered s the focal length. The proected human heght n each key frame s frst calculated from the 2D data. Next the proposed algorthm s employed to calculate the focal length range for each par of neghborng key frames usng Eq.(4). Lastly, the focal length s approxmated usng Eq.(6) by choosng the upper bound of the smallest focal length range. Fg.8 shows the experment results. In Fg.8, the th column bar represents the range of the focal length calculated for the th par of key frames. Each par of such key frames ndcates a foot step. For example n Fg.7, the 4th and 5th key frame consst of the frst par of such key frames n Vdeo 1. When the human fgure s not walkng towards the camera, such par may produce a very large range of focal length accordng to Eq.(4). However, the fnal result wll not be affected as long as the vdeo contans at least one frame n whch the human fgure s walkng towards the camera. It can be seen that the recovered focal length s very close to the actual focal length when usng 35 mm flm format as a standard. For Vdeo 1, the recovered focal length s mm whle the actual focal length s mm. For Vdeo 2, the recovered focal length s mm whle the actual focal length s 400 mm. Focal length (mm) Focal length (mm) Key frame par number Recovered focal length: mm Actual focal length: mm Key frame par number Recovered focal length: mm Actual focal length: 400 mm Fg.8 Camera model recovery results. Results n Vdeo 1; Results n Vdeo 2 Therefore, the proposed camera recovery algorthm s tested to be very satsfactory, consderng that t does not requre any nformaton on the mage background or known 3D ponts. Test of skeleton proportons estmaton Based on the recovered perspectve camera model, the proposed algorthm wll reconstruct the vrtual human fgure for each of the extracted key frames. The vrtual scene s frst reconstructed, where the vrtual floor s defned as the plane passng through the bottom of the 35 mm flm and parallel to the orentaton of the camera model. Next the vrtual human fgure s constructed from both feet towards the other three ends of the human body: head and both hands usng the method dscussed above. Once the vrtual human fgure s reconstructed for all key frames, the skeleton proporton can be calculated by dvdng the length of each skeleton segment by the sum of them.

8 Peng et al. / J Zheang Unv SCIENCE A (7): The experment results are shown n Fg.9: and show the vrtual scene examples used n the proposed system; (c) and (d) compare the skeleton proportons of the estmated vrtual human fgure aganst the actual human fgure, where the horzontal axs represents the skeleton ID and the vertcal axs represents the skeleton proporton. The defnton of the skeleton segment ID s lsted n Table 1. It can be seen from Fgs.9c and 9d that the maxmum estmaton error s 1.13% for Vdeo 1 and 1.60% for Vdeo 2. The error can be further mnmzed f the number of teratons u 1 s ncreased. The detaled numercal evaluaton of the three modules n the proposed system showed that the proposed algorthms have successfully estmated the skeleton proportons of the human fgure drectly from 2D data. The expermental results are hghly satsfactory. CONCLUSION Skeleton proporton Skeleton proporton 16% 12% 8% 4% 0% 16% 12% 8% 4% 0% Estmaton Key frame par number Estmaton Table 1 Skeleton segment ID Skeleton ID Descrpton Skeleton ID Descrpton 1 HP-AM 6 EB-WR 2 AM-NK 7 HP 1 -HP 2 3 NK-HT 8 HP-KN 4 NK-SD 9 KN-AK 5 SD-EB (c) Actual Actual Key frame par number (d) Fg.9 Skeleton proporton estmaton results. Vrtual scene example for Vdeo 1; Vrtual scene example for Vdeo 2; (c) Skeleton proporton estmaton results n Vdeo 1; (d) Skeleton proporton estmaton results n Vdeo 2 Ths paper proposed a novel system to recover the human skeleton proportons from 2D uncalbrated data. The proposed system extracts the key frames, recovers the perspectve camera model, and then estmates the skeleton proportons of the human fgure n the source vdeo. The proposed method s tested on the 2D data from two real vdeo sequences. The experments acheved satsfactory results. The recovered human skeleton model s very close to the orgnal and can be used n future research such as full body reconstructon and human moton reconstructon from monocular data. References Bacakoglu, H., Kamel, M., An Optmzed Two-step Camera Calbraton Method. Proceedngs of IEEE Internatonal Conference on Robotcs and Automaton, 2: Barrón, C., Kakadars, I.A., Estmatng anthropometry and pose from a sngle uncalbrated mage. Computer Vson and Image Understandng, 81(3): [do: /cvu ] Barrón, C., Kakadars, I.A., On the mprovement of anthropometry and pose estmaton from a sngle uncalbrated mage. Mach. Vson Appl., 14(4): [do: /s ] Batsta, J., Arauo, H., De Almeda, A.T., Iteratve Mult-Step Explct Camera Calbraton. IEEE Internatonal Conference on Computer Vson (ICCV 98). Bombay, Inda, p Cheung, K.M., Baker, S., Kanade, T., Shape-From- Slhouette of Artculated Obects and ts Use for Human Body Knematcs Estmaton and Moton Capture. Proceedngs of the IEEE Conference on Computer Vson and Pattern Recognton. Cohen, I., Lee, M.W., D Body Reconstructon for Immersve Interacton. 2nd Internatonal Workshop on Artculated Moton and Deformable Obects.

9 1274 Peng et al. / J Zheang Unv SCIENCE A (7): D Apuzzo, N., Gruen, A., Plankers, R., Fua, P., Least Squares Matchng Trackng Algorthm for Human Body Modellng, XIX ISPRS Congress, Amsterdam, Netherlands. Hlton, A., Gentls, T., Popup People: Capturng Human Models to Populate Vrtual Worlds (Sketch). SIG- GRAPH 98. J, Q., Zhang, Y., Camera calbraton wth Genetc Algorthms. IEEE Transactons on Systems, Man and Cybernetcs-Part A: Systems and Humans, 31(2): [do: / ] Kakadars, I.A., Metaxas, D., D Human Body Model Acquston from Multple Vews. ICCV 95: Proceedngs of the Ffth Internatonal Conference on Computer Vson, p.618. [do: /iccv ] Krtley, C., Walkng, Normal and Pathologcal. Lee, W.S., Gu, J., Magnenat-Thalmann, N., Generatng Anmatable 3D Vrtual Humans from Photographs. Computer Graphcs Forum (Eurographcs 2000), 19(3). Memon, Q., Khan, S., Camera calbraton and three-dmensonal world reconstructon of stereo-vson usng neural networks. Internatonal Journal of Systems Scence, 32(9): [do: / ] Peng, E., L, L., Estmaton of Human Skeleton Proporton from 2D Uncalbrated Monocular Data. Proceedngs of Computer Anmaton and Socal Agents (CASA 2005). Hong Kong. Remondno, F., Rodtaks, A., Human Fgure Reconstructon and Modellng from Sngle Image or Monocular Vdeo Sequence. 3-D Dgtal Imagng and Modellng 2003 (3DIM 2003), Proceedngs, Fourth Internatonal Conference on, p [do: /im ] Starck, J., Hlton, A., Model-Based Multple Vew Reconstructon of People. Proceedngs of the Nnth IEEE Internatonal Conference on Computer Vson (ICCV 03), p.915. [do: /iccv ] Taylor, C.J., Reconstructon of artculated obects from pont correspondences n a sngle uncalbrated mage. Computer Vson and Image Understandng, 80(3): [do: /cvu ] Vlla-Urol, M., Sanz, M., Kuester, F., Bagherzadeh, N., Automatc creaton of three-dmensonal avatars. Vdeometrcs VII, Proceedngs of the SPIE, 5013: [do: / ] Zhao, J., L, L., Kwoh, C.K., Posture Reconstructon and Human Anmaton from 2D Feature Ponts. Computer Graphcs Forum, 24(4): [do: / x] Welcome vstng our ournal webste: Welcome contrbutons & subscrpton from all over the world The edtor would welcome your vew or comments on any tem n the ournal, or related matters Please wrte to: Helen Zhang, Managng Edtor of JZUS E-mal: zus@zu.edu.cn Tel/Fax: /