This paper compares the two heuristic inverse kinematics methods: Forward And Backward Reaching Inverse Kinematics (FABRIK) and Cyclic Coordinate Descent (CCD) in the use cases concerning quadruped models. Unoptimised versions of the two algorithms were implemented into a game engine and evaluated on a quadruped model. The two algorithms were evaluated by computational time, iterations and average error. The results show that FABRIK outperformed CCD in all of our test scenarios, in number of iterations required, average error as well as function execution time. However, results also showed that FABRIK was less superior when targets were barely within reach, since one leg might be able to reach a target, but two connected legs might not be able to reach two targets because of their interference with each other. This suggests that to improve on FABRIK it should be optimised when considering a bipedal or quadruped model. / Den här artikeln jämför de två heuristiska Inverse Kinematics-metoderna: Forward And Backward Reaching Inverse Kinematics (FABRIK) och Cyclic Coordinate Descent (CCD) i användningsfallet fyrbenta modeller. Ooptimerade versioner av de två algoritmerna implementerades i en spelmotor och utvärderades på en fyrbent modell. De två algoritmerna utvärderades därefter baserat på beräkningstid, antal iterationer och avstånd till målet. Resultaten visar att FABRIK överträffade CCD i alla testscenarium, i antal iterationer, avstånd till mål samt funktionskörningstid. Resultaten visade dock även att FABRIK var mindre överlägsen när målen var precis utom räckhåll; detta då ett ben kunde nå ett mål om det utvärderades ensamt, men när benet kopplades samman med motstående ben via en höftled kunde inget av benen nå sina mål på grund av att de påverkade varandra. Detta tyder på att för att förbättra FABRIK bör den optimeras till att ta hänsyn till huruvida modellen har två eller fyra ben.

No / We introduce a new "marker-based" model for use in capturing equine movement. This model is informed by a sound biomechanical study of the animal and can be deployed in the pursuit of many undertakings. Unlike many other approaches, our method provides a high level of automation and hides the intricate biomechanical knowledge required to produce realistic results. Due to this approach, it is possible to acquire solved data with minimal manual intervention even in real-time conditions. The approach introduced can be replicated for the production of many other animals. The model is first informed by the veterinary world through studies of the subject's anatomy. Second, further medical studies aimed at understanding and addressing surface processes, inform model creation. The latter studies address items such as skin sliding. If not otherwise corrected these processes may hinder marker based capture. The resultant model has been tested in feasibility studies for practicality and subject acceptance during production. Data is provided for scrutiny along with the subject digitally captured through a variety of methods. The digital subject in mesh form as well as the motion capture model aid in comparison and show the level of accurateness achieved. The video reference and digital renders provide an insight into the level of realism achieved.